leishmaniasis is a complex disease with different clinical presentations and is caused by a parasite belonging to the leishmania genus .
although leishmaniasis is a worldwide vector - borne disease affecting 88 countries , recent studies show that 90% of cutaneous leishmaniasis ( cl ) cases occur in seven east mediterranean countries including afghanistan , algeria , brazil , iran , peru , saudi arabia and syria .
today , due to the increase in urbanisation , agricultural development , deforestation , irrigation , and more recently human immunodeficiency virus ( hiv ) infection in many countries , the transmission and spread of this disease shows an increasing trend .
it is estimated that more than 15 million people are infected with this disease , with 1.5 - 2 million new cases per year .
leishmaniasis is now considered as a severe public health problem particularly in developing countries and iran and a great economic burden on the health resources , despite advances in its diagnosis and treatment .
it has been underestimated for many years until 1993 and is now classified by the world health organization ( who ) as one of the most neglected tropical diseases .
it is obvious that today the disease is much more prevalent than previously suspected and even its risk will be on increase in the future .
also , the burden of disease is increasing such that its global burden is estimated to be equal to 2,357,000 years .
cl is a disabling form of the disease when lesions are multiple , frequently self - healing in the old world and is the most common form of the disease in iran .
fars province in the south of iran is one of the important foci of the disease .
based on the reported evidence , most rural areas in this province such as arsanjan , neiriz and marvdasht cities can be considered as a cl focus .
many vector - bone diseases including malaria , rift valley fever , lyme disease , fasciola and schistosoma have a focal area , where the spatial distribution of the parasite , host , vector and required environmental conditions coincide and geographic information systems ( gis ) are used for description of these diseases .
leishmaniasis is another vector - borne disease related to environmental factors and its vector has a spatial distribution .
gis is a method for spatial analysis that could create , archive , analyse traditional map and place data of diseases distribution in epidemiology and combine these with environmental data . with this tool , data from different scales and types ,
for example , population health data , can be integrated with environmental data such as topographic maps or climatic information .
there are many studies conducted on the risk factors which effect on the spread of cl diseases , but because environmental and geographical characteristics have an important effect on the disease spread , all recent studies in iran have focussed on characteristics of vectors of the disease or patients.[461316 ] so , we designed and conducted the present study to determine the eco - environmental characteristics of this disease by spatial analysis .
this was an ecological study conducted on cl cases in relation to eco - environmental factors .
fars province is located in the south of iran [ figure 1 ] at 2731 n , 5055 e and contains approximately 8% of the total surface area of iran including 125,000 km .
this province covers 188 counties and has mountains with an average of 5000 feet height above the sea level ; its climate is quite dusty and dry , with warm summers , mild winters and a great deal of sunshine throughout the year .
the study subjects consisted of all the residents in the counties of fars province , including 188 counties .
eco - environmental and climate data such as earth topology , herb coverage , temperature , relative humidity , rainfall , evaporation rates and wind speed were obtained by weather bureau of fars province , collected by meteorograph stations in separate shape file format .
also , cl cases were gathered as census by health surveillance system in all counties and in control diseases center ( cdc ) of fars province .
then , these data were interpolated to all the counties or their neighbourhood by kiriging method .
the questionnaire contained data on the demographic variables of patients , such as place of residence , lesion characteristics , vector , probable reservoir and treatment .
this form was completed for all patients by auxiliary or health workers and sent to cdc in province health center ( phc ) .
cl cases data were gathered for all the counties from 2002 to 2009 and were considered as outcome variable .
cumulative incident cases of the disease were rechecked by all the reported cases in these years by health centers . because in the south of iran cl is a rodent disease which be caused by leishmania major and transmitted mainly by phlebotomus papatasi sand flies and also the main reservoir hosts of the disease is tatera indica , the agent and vector of disease were not considered as covariates .
after collection of cl and climate data , all the collected data were entered and a data file was created that was useful for arc gis 9.3 software and spatial analysis .
we analysed the data in county level instead of cities due to the wide variation in cl cases and for better fitting of the spatial models . to study the spatial distribution and
spatial models such as geographic weighted regression ( gwr ) were used for analysis and compared with ordinary least square ( ols ) model . in the first stage , we ran the ols with 12 explanatory variables as shown in table 1 , and then residuals of ols test by moran 's index were used for spatial autocorrelation . in the third stage , we used gwr because of the existence of autocorrelation in residual of ols regression models , unfitness of linear regression model and significant non - stationary nature of the regression model .
explanatory variables included in the study gwr is one of the several spatial regression techniques and an extension of traditional regression to predict , detect and estimate spatial non - stationary coefficient for model variables .
this spatially localised model assumes variability in relationships between the regression variables over space and generates a set of local models for the outcome variable or process we are trying to predict and fit a regression equation to every feature .
fars province is located in the south of iran [ figure 1 ] at 2731 n , 5055 e and contains approximately 8% of the total surface area of iran including 125,000 km .
this province covers 188 counties and has mountains with an average of 5000 feet height above the sea level ; its climate is quite dusty and dry , with warm summers , mild winters and a great deal of sunshine throughout the year .
the study subjects consisted of all the residents in the counties of fars province , including 188 counties .
eco - environmental and climate data such as earth topology , herb coverage , temperature , relative humidity , rainfall , evaporation rates and wind speed were obtained by weather bureau of fars province , collected by meteorograph stations in separate shape file format .
also , cl cases were gathered as census by health surveillance system in all counties and in control diseases center ( cdc ) of fars province .
then , these data were interpolated to all the counties or their neighbourhood by kiriging method .
the questionnaire contained data on the demographic variables of patients , such as place of residence , lesion characteristics , vector , probable reservoir and treatment .
this form was completed for all patients by auxiliary or health workers and sent to cdc in province health center ( phc ) .
cl cases data were gathered for all the counties from 2002 to 2009 and were considered as outcome variable .
cumulative incident cases of the disease were rechecked by all the reported cases in these years by health centers . because in the south of iran cl is a rodent disease which be caused by leishmania major and transmitted mainly by phlebotomus papatasi sand flies and also the main reservoir hosts of the disease is tatera indica , the agent and vector of disease were not considered as covariates .
after collection of cl and climate data , all the collected data were entered and a data file was created that was useful for arc gis 9.3 software and spatial analysis .
we analysed the data in county level instead of cities due to the wide variation in cl cases and for better fitting of the spatial models . to study the spatial distribution and
spatial models such as geographic weighted regression ( gwr ) were used for analysis and compared with ordinary least square ( ols ) model . in the first stage , we ran the ols with 12 explanatory variables as shown in table 1 , and then residuals of ols test by moran 's index were used for spatial autocorrelation . in the third stage , we used gwr because of the existence of autocorrelation in residual of ols regression models , unfitness of linear regression model and significant non - stationary nature of the regression model .
explanatory variables included in the study gwr is one of the several spatial regression techniques and an extension of traditional regression to predict , detect and estimate spatial non - stationary coefficient for model variables .
this spatially localised model assumes variability in relationships between the regression variables over space and generates a set of local models for the outcome variable or process we are trying to predict and fit a regression equation to every feature .
table 1 shows the explanatory variables included in the present study and summarises the mean , median , minimum and maximum of these variables .
the location of fars province and standardised variation in cl cases from 2002 to 2009 in all counties of the province are plotted in figure 1 .
location of fars province and standardised variation in leishmaniasis cases from 2002 to 2009 in ols model , chi - square of joint wald statistic was equal to 58.8 and robust overall model was significant , so the robust t - statistics and its probability were used for deciding about predictor variables .
ols ( global ) model results are shown in table 2 , indicating that rainy days , minimum temperature , wind velocity , maximum and minimum relative humidity were significant variables that can predict the cl ( p < 0.05 ) .
the standardised residuals of ols model are plotted in figure 2 , showing that the model has no goodness of fit in center , south and west south of the study area .
also , the jarque - bera statistic probability ( p < 0.05 ) shows deficiency of a magnitude variable in the model .
variables and their statistics in ordinary least square model standardised residual plot of ols in the study area by all explanatory variables we used the spatial autocorrelation ( moran 's i ) tool to ensure that residuals of ols were not spatially autocorrelated .
this test showed that there was a significant spatial autocorrelation in residuals of ols model ( z=2.45 and p - value=0.014 ) . on other hand ,
cl cases were more likely to be similar among nearby counties than among greater distant counties .
the probability of koenker statistics was less than 0.05 , suggesting that the data were non - stationary , so we used gwr to remove the effect of non - stationary features of data .
after running gwr with all explanatory variables , the model was severely complex and not executed , so we conducted factor analysis to reduce the variables . based on principal component analysis method , rainy days , minimum temperature , wind velocity and maximum relative humidity
so , in the next stage , we included population density as surrogate of acquired immunity and then the r and adjusted r increased to 0.425 and 0.389 , respectively .
standardised residual plot of gwr in the study area by rain days , minimum temperature , wind velocity , maximum relative humidity and population density table 3 compares the goodness of fit parameters in three used models and shows that the gwr model is more efficiently .
the akaike information criteria ( aic ) and r show that gwr model with population density is better fitted .
goodness of fit for the three models used in the study including rainy days , minimum temperature , wind velocity , maximum relative humidity
recent advances in techniques and computer - based geographical information systems help the scientists and researchers to predict , monitor and diagnose the environmental and ecological factors affecting the spatial and temporal distribution of several vector - borne diseases including malaria , leishmaniasis , and schistosomiasis , and also other diseases such as coronary heart diseases .
determination of the relationship of descriptive data with geographical places was an important characteristic of this study that contributes to evaluation of eco - environmental factors related to cl . based on the results of ols model , among the ecological factors under study , rainy days , minimum temperature , wind velocity and maximum relative humidity were the most important predictors of spatial distribution of cl . in the present study
, we predicted the most important eco - environmental factors pertaining to spatial relationship and compared ols and gwr models . in ordinary regression models , autocorrelation can affect the coefficients of the variables and disturb the fitness of the model , which was shown in the present study .
although in this study r of gwr is reduced because of fewer explanatory variables in the model , gwr in comparison with ols with fixed explanatory variables has a higher adjusted r ( 0.18 vs. 0.15 ) because the gwr weights to any spatial according to its features but ols can not do it .
grillet et al . showed that gwr models in mosquito - borne infection greatly improved prediction of malaria risk as compared to ols regression models .
low adjusted r in gwr and ols models indicates the deficiency of important variables in the model .
so , population density was used as a surrogate for acquired immunity variable and included in the final model .
it was found that rainy days , minimum temperature , wind velocity , maximum relative humidity and population density can be considered as predictors of eco - environmental factors of cl .
some other studies have been conducted in this area and have come to the same conclusion .
one study in spatial relations between environmental and meteorological factors in central spain showed that shelter against wind , lower summer mean temperature and lower annual mean precipitation are important factors in sand fly frequency . in another study conducted in sudan , altitude and downpour were the most important factors .
however , in our study , although the same conclusion was reached as to rain , altitude was not a significant factor in our results .
there are some important factors such as demographic and human behaviors that were not included in this study while they are related to cl .
in addition , seasonal , cycle and genetic variation of the vector can affect the presence of cl .
but gonzalez et al . predicted that due to the capacity of vectors to persist in the habitats with different degrees of ecological suitability and climate changes , the number of cases will increase in upcoming decades .
the surveillance system of cl in iran is a passive system and many of the patients with small lesions or with lesions in the masked area might not refer to health centers or surveillance system
. it can affect the number of reported cases in some areas , especially in rural areas .
it caused a wide variation in the reported cases in the province , which is reflected in our maps , but studying on counties reduces this effect and helps us in disease mapping and modeling of related factors .
future researches are required to evaluate the effect of other ecological and sero - epidemiologic features in other agents and vectors .
spatial analysis can be a good tool for detection and prediction of cl disease . as compared to autocorrelated and non - stationary data , gwr model yields a better fitness than ols model .
therefore , based on our results and other recent studies on demographic factors that affect cl cases and disease pattern , it is essential for the health authorities to take measures to control this epidemic and prevent its spread to non - contaminated or low - rated counties . also , rapid treatment of patients in the coming years is essential as well . since cl is a multifactorial disease , and ecological , demographic , temporal and climatic factors affect it , for the control of leishmaniasis and cl , close cooperation between the universities of medical sciences , health centers and the government is recommended .
 background : despite the advances in the diagnosis and treatment of leishmaniasis , it is still considered as a severe public health problem particularly in developing countries and a great economic burden on the health resources . 
 the present study was designed and conducted to determine the eco - environmental characteristics of the leishmaniasis disease by spatial analysis.materials and methods : in an ecological study , data were collected on eco - environmental factors of fars province in iran and on cutaneous leishmaniasis ( cl ) cases from 2002 to 2009 . geographic weighted regression ( gwr ) 
 was used to analyse the data and compare them with ordinary least square ( ols ) regression model results . 
 moran 's index was applied for analysis of spatial autocorrelation in residual of ols . 
 p value less than 0.05 was considered as significant and adjusted r2 was used for model preferences.results:there was a significant spatial autocorrelation in the residuals of ols model ( z=2.45 , p=0.014 ) . 
 gwr showed that rainy days , minimum temperature , wind velocity , maximum relative humidity and population density were the most important eco - environmental risk factors and explained 0.388 of the associated factors of cl.conclusion:spatial analysis can be a good tool for detection and prediction of cl disease . in autocorrelated and non - stationary data , gwr model yields a better fitness than ols regression model . 
 also , population density can be used as a surrogate variable of acquired immunity and increase the adjusted r2 . 
INTRODUCTION
MATERIALS AND METHODS
Study area and population
Data collection
Statistical analysis
RESULTS
DISCUSSION
CONCLUSION