Cutaneous leishmaniasis prevalence and morbidity based on environmental factors in Ilam, Iran: Spatial analysis and land use regression models.
Identifieur interne : 000008 ( PubMed/Corpus ); précédent : 000007; suivant : 000009Cutaneous leishmaniasis prevalence and morbidity based on environmental factors in Ilam, Iran: Spatial analysis and land use regression models.
Auteurs : Mehdi Mokhtari ; Mohammad Miri ; Ali Nikoonahad ; Ali Jalilian ; Razi Naserifar ; Hamid Reza Ghaffari ; Farogh KazembeigiSource :
- Acta tropica [ 1873-6254 ] ; 2016.
English descriptors
- KwdEn :
- Agriculture (statistics & numerical data), Altitude, Cities, Environment, Forests, Geographic Information Systems, Humans, Iran (epidemiology), Iraq, Leishmaniasis, Cutaneous (epidemiology), Poisson Distribution, Prevalence, Rain, Regression Analysis, Risk Factors, Soil, Spatial Analysis, Temperature.
- MESH :
- chemical : Soil.
- epidemiology : Iran, Leishmaniasis, Cutaneous.
- statistics & numerical data : Agriculture.
- Altitude, Cities, Environment, Forests, Geographic Information Systems, Humans, Iraq, Poisson Distribution, Prevalence, Rain, Regression Analysis, Risk Factors, Spatial Analysis, Temperature.
Abstract
The aim of this study was to investigate the impact of the environmental factors on cutaneous leishmaniasis (CL) prevalence and morbidity in Ilam province, western Iran, as a known endemic area for this disease. Accurate locations of 3237 CL patients diagnosed from 2013 to 2015, their demographic information, and data of 17 potentially predictive environmental variables (PPEVs) were prepared to be used in Geographic Information System (GIS) and Land-Use Regression (LUR) analysis. The prevalence, risk, and predictive risk maps were provided using Inverse Distance Weighting (IDW) model in GIS software. Regression analysis was used to determine how environmental variables affect on CL prevalence. All maps and regression models were developed based on the annual and three-year average of the CL prevalence. The results showed that there was statistically significant relationship (P value≤0.05) between CL prevalence and 11 (64%) PPEVs which were elevation, population, rainfall, temperature, urban land use, poorland, dry farming, inceptisol and aridisol soils, and forest and irrigated lands. The highest probability of the CL prevalence was predicted in the west of the study area and frontier with Iraq. An inverse relationship was found between CL prevalence and environmental factors, including elevation, covering soil, rainfall, agricultural irrigation, and elevation while this relation was positive for temperature, urban land use, and population density. Environmental factors were found to be an important predictive variables for CL prevalence and should be considered in management strategies for CL control.
DOI: 10.1016/j.actatropica.2016.08.002
PubMed: 27496622
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pubmed:27496622Le document en format XML
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<affiliation><nlm:affiliation>Department of Environmental Health Engineering, School of Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran.</nlm:affiliation>
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<author><name sortKey="Miri, Mohammad" sort="Miri, Mohammad" uniqKey="Miri M" first="Mohammad" last="Miri">Mohammad Miri</name>
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<affiliation><nlm:affiliation>Department of Environmental Health Engineering, School of Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran; Department of Environmental Health Engineering, School of Health, Ilam University of Medical Sciences, Ilam, Iran. Electronic address: nikoonahad_ali_n@yahoo.com.</nlm:affiliation>
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<author><name sortKey="Ghaffari, Hamid Reza" sort="Ghaffari, Hamid Reza" uniqKey="Ghaffari H" first="Hamid Reza" last="Ghaffari">Hamid Reza Ghaffari</name>
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<series><title level="j">Acta tropica</title>
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<term>Forests</term>
<term>Geographic Information Systems</term>
<term>Humans</term>
<term>Iran (epidemiology)</term>
<term>Iraq</term>
<term>Leishmaniasis, Cutaneous (epidemiology)</term>
<term>Poisson Distribution</term>
<term>Prevalence</term>
<term>Rain</term>
<term>Regression Analysis</term>
<term>Risk Factors</term>
<term>Soil</term>
<term>Spatial Analysis</term>
<term>Temperature</term>
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<term>Environment</term>
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<term>Iraq</term>
<term>Poisson Distribution</term>
<term>Prevalence</term>
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<front><div type="abstract" xml:lang="en">The aim of this study was to investigate the impact of the environmental factors on cutaneous leishmaniasis (CL) prevalence and morbidity in Ilam province, western Iran, as a known endemic area for this disease. Accurate locations of 3237 CL patients diagnosed from 2013 to 2015, their demographic information, and data of 17 potentially predictive environmental variables (PPEVs) were prepared to be used in Geographic Information System (GIS) and Land-Use Regression (LUR) analysis. The prevalence, risk, and predictive risk maps were provided using Inverse Distance Weighting (IDW) model in GIS software. Regression analysis was used to determine how environmental variables affect on CL prevalence. All maps and regression models were developed based on the annual and three-year average of the CL prevalence. The results showed that there was statistically significant relationship (P value≤0.05) between CL prevalence and 11 (64%) PPEVs which were elevation, population, rainfall, temperature, urban land use, poorland, dry farming, inceptisol and aridisol soils, and forest and irrigated lands. The highest probability of the CL prevalence was predicted in the west of the study area and frontier with Iraq. An inverse relationship was found between CL prevalence and environmental factors, including elevation, covering soil, rainfall, agricultural irrigation, and elevation while this relation was positive for temperature, urban land use, and population density. Environmental factors were found to be an important predictive variables for CL prevalence and should be considered in management strategies for CL control.</div>
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<Abstract><AbstractText>The aim of this study was to investigate the impact of the environmental factors on cutaneous leishmaniasis (CL) prevalence and morbidity in Ilam province, western Iran, as a known endemic area for this disease. Accurate locations of 3237 CL patients diagnosed from 2013 to 2015, their demographic information, and data of 17 potentially predictive environmental variables (PPEVs) were prepared to be used in Geographic Information System (GIS) and Land-Use Regression (LUR) analysis. The prevalence, risk, and predictive risk maps were provided using Inverse Distance Weighting (IDW) model in GIS software. Regression analysis was used to determine how environmental variables affect on CL prevalence. All maps and regression models were developed based on the annual and three-year average of the CL prevalence. The results showed that there was statistically significant relationship (P value≤0.05) between CL prevalence and 11 (64%) PPEVs which were elevation, population, rainfall, temperature, urban land use, poorland, dry farming, inceptisol and aridisol soils, and forest and irrigated lands. The highest probability of the CL prevalence was predicted in the west of the study area and frontier with Iraq. An inverse relationship was found between CL prevalence and environmental factors, including elevation, covering soil, rainfall, agricultural irrigation, and elevation while this relation was positive for temperature, urban land use, and population density. Environmental factors were found to be an important predictive variables for CL prevalence and should be considered in management strategies for CL control.</AbstractText>
<CopyrightInformation>Copyright © 2016 Elsevier B.V. All rights reserved.</CopyrightInformation>
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