TY - JOUR
T1 - Development and comparison of dengue vulnerability indices using gis‐based multi‐criteria decision analysis in lao pdr and Thailand
AU - Zafar, Sumaira
AU - Shipin, Oleg
AU - Paul, Richard E.
AU - Rocklöv, Joacim
AU - Haque, Ubydul
AU - Rahman, Md Siddikur
AU - Mayxay, Mayfong
AU - Pientong, Chamsai
AU - Aromseree, Sirinart
AU - Poolphol, Petchaboon
AU - Pongvongsa, Tiengkham
AU - Vannavong, Nanthasane
AU - Overgaard, Hans J.
N1 - Funding Information:
This research was supported by the Research Council of Norway [DENCLIM project, grant number 281077].
Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2021/9
Y1 - 2021/9
N2 - 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, DVIWADI 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 DVISE was validated in 22% (12–40%) of districts in Laos and in 13% (3–38%) of subdistricts in Thailand. The DVIBWM 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 DVIWADI indicated high vulnerability in urban centers and in areas with plantations and forests. In 2019, high DVIWADI 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, DVIWADI was the most suitable vulnerability index for the study area. The DVIWADI 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.
AB - 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, DVIWADI 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 DVISE was validated in 22% (12–40%) of districts in Laos and in 13% (3–38%) of subdistricts in Thailand. The DVIBWM 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 DVIWADI indicated high vulnerability in urban centers and in areas with plantations and forests. In 2019, high DVIWADI 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, DVIWADI was the most suitable vulnerability index for the study area. The DVIWADI 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.
KW - Epidemiology
KW - Exposure
KW - Health status indicators
KW - Spatial analysis
KW - Susceptibility
UR - http://www.scopus.com/inward/record.url?scp=85114323516&partnerID=8YFLogxK
U2 - 10.3390/ijerph18179421
DO - 10.3390/ijerph18179421
M3 - Article
C2 - 34502007
AN - SCOPUS:85114323516
SN - 1661-7827
VL - 18
JO - International journal of environmental research and public health
JF - International journal of environmental research and public health
IS - 17
M1 - 9421
ER -