The effect of climate change on malaria transmission in the southeast of Iran.

Malaria is a vector-borne disease, likely to be affected by climate change. In this study, general circulation model (GCM)-based scenarios were used for projecting future climate patterns and malaria incidence by artificial neural networks (ANN) in Zahedan district, Iran. Daily malaria incidence data of Zahedan district from 2000 to 2019 were inquired. The gamma test was used to select the appropriate combination of parameters for nonlinear modeling. The future climate pattern projections were obtained from HadGEM2-ES. The output was downscaled using LARS-WG stochastic weather generator under two Representative Concentration Pathway (RCP2.6 and RCP8.5) scenarios. The effect of climate change on malaria transmission for 2021-2060 was simulated by ANN. The designed model indicated that the future climate in Zahedan district will be warmer, more humid, and with more precipitation. Assessment of the potential impact of climate change on the incidence of malaria by ANN showed the number of malaria cases in Zahedan under both scenarios (RCP2.6 and RCP 8.5). It should be noted that due to the lack of daily malaria data before 2013, monthly data from 2000 were used only for initial analysis; and in preprocessing and simulation analyses, the daily malaria data from 2013 to 2019 were used. Therefore, if proper interventions are not implemented, malaria will continue to be a health issue in this region.

Calibration and validation of the Angstrom-Prescott model in solar radiation estimation using optimization algorithms.

The Angstrom-Prescott (A-P) model is widely suggested for estimating solar radiation (Rs) in areas without measured or deficiency of data. The aim of this research was calibration and validation of the coefficients of the A-P model at six meteorological stations across arid and semi-arid regions of Iran. This model has improved by adding the air temperature and relative humidity terms. Besides, the coefficients of the A-P model and improved models have calibrated using some optimization algorithms including Harmony Search (HS) and Shuffled Complex Evolution (SCE). Performance indices, i.e., Root Mean Square Error (RMSE), Mean Bias Error, and coefficient of determination (R2) have used to analyze the models ability in estimating Rs. The results indicated that the performance of the A-P model had more precision and less error than improved models in all the stations. In addition, the best results have obtained for the A-P model with the SCE algorithm. The RMSE varies between 0.82 and 2.67 MJ m-2 day-1 for the A-P model with the SCE algorithm in the calibration phase. In the SCE algorithm, the values of RMSE had decreased about 4% and 7% for Mashhad and Kerman stations in the calibration phase compared to the HS algorithm, respectively.

The effect of meteorological variables on salmonellosis incidence in Kermanshah, West of Iran: a generalized linear model with negative binomial approach.

PURPOSE: Salmonella is one of the main causes of gastroenteritis, and its incidence may be affected by meteorological variables. This is the first study about the effect of climatic factors on salmonella incidence in Kermanshah, Iran. METHODS: Data about salmonellosis cases in Kermanshah were inquired from Center for Communicable Disease Control, at the Ministry of Health and Medical Education of Iran, for the 2008 to 2018 time-frame. Meteorological variables including maximum, minimum and mean of temperature and humidity, sunshine hours and rainfall were inquired for the same time frame. Negative binomial generalized linear models (GLM) were used to assess the effect of meteorological variables on the weekly incidence of salmonellosis. RESULTS: During the years under study, 569 confirmed cases were registered in Kermanshah province. Study results showed a 3 % increase in salmonellosis incidence, after 1 % increase in minimum humidity in the week before (incidence rate ratio (IRR): 1.03; 95 % confidence interval (CI):1.02-1.05) and also a 4 % increase in incidence for 1 °C increase in mean temperature in the same week (IRR: 1.04; 95 % CI:1.02-1.06). CONCLUSIONS: Increase in minimum humidity and mean temperature may have a role in increasing the incidence of salmonellosis in Iran.

The effect of climate variables on the incidence of cutaneous leishmaniasis in Isfahan, Central Iran.

In recent years, there have been considerable changes in the distribution of diseases that are potentially tied to ongoing climate variability. The aim of this study was to investigate the association between the incidence of cutaneous leishmaniasis (CL) and climatic factors in an Iranian city (Isfahan), which had the highest incidence of CL in the country. CL incidence and meteorological data were acquired from April 2010 to March 2017 (108 months) for Isfahan City. Univariate and multivariate seasonal autoregressive integrated moving average (SARIMA), generalized additive models (GAM), and generalized additive mixed models (GAMM) were used to identify the association between CL cases and meteorological variables, and forecast CL incidence. AIC, BIC, and residual tests were used to test the goodness of fit of SARIMA models; and R2 was used for GAM/GAMM. 6798 CL cases were recorded during this time. The incidence had a seasonal pattern and the highest number of cases was recorded from August to October. In univariate SARIMA, (1,0,1) (0,1,1)12 was the best fit for predicting CL incidence (AIC=8.09, BIC=8.32). Time series regression (1,0,1) (0,1,1)12 showed that monthly mean humidity after 4-month lag was inversely related to CL incidence (AIC=8.53, BIC=8.66). GAMM results showed that average temperature with 2-month lag, average relative humidity with 3-month lag, monthly cumulative rainfall with 1-month lag, and monthly sunshine hours with 1-month lag were related to CL incidence (R2=0.94). The impact of meteorological variables on the incidence of CL is not linear and GAM models that include non-linear structures are a better fit for prediction. In Isfahan, Iran, meteorological variables can greatly predict the incidence of CL, and these variables can be used for predicting outbreaks.

The effect of cold and heat waves on mortality in Urmia a cold region in the North West of Iran.

Few studies have investigated the different extreme temperature effects (heat-cold) of one geographical location at the same time in Iran. This study was conducted to assess the impact of heat and cold waves on mortality in Urmia city, which has a cold and mountainous climate. Distributed Lag Non-linear Models combined with a quasi-Poisson regression were used to assess the impact of heat (HW) and cold waves (CW) on mortality in subgroups, controlled for potential confounders such as long-term trend of daily mortality, day of week effect, holidays, mean temperature, humidity, wind speed and air pollutants. The heat/cold effect was divided into two general categories A-main effect (the effect caused by temperature), B-added effect (the effect caused by persistence of extreme temperature). Results show that there was no relation between HW and respiratory and cardiovascular death, but in main effects, HW(H1) significantly increased, the risk of Non-Accidental Death (NAD) in lag 0 (Cumulative Excess Risk (CER) NAD = 31(CI; 4-65)). Also in added effects, HW had a significant effect on NAD (CER H1; NAD; lag;0-2 = 31(CI; 5, 51), CER H2; NAD; lag;0-2 = 26(CI; 6, 48)). There was no relation between CW and respiratory death and cardiovascular death, but in added effects, CW(C1) significantly decreased, the risk of non-accidental death in initial lags (CER C1; NAD; lag;0-2 = -19 (CI; -35, -2)). It seems that high temperatures and heat waves increase the risk of non-accidental mortality in northwest of Iran.

The effect of climate variables on the incidence of Crimean Congo Hemorrhagic Fever (CCHF) in Zahedan, Iran.

BACKGROUND: The Crimean-Congo Hemorrhagic fever (CCHF) is endemic in Iran and has a high fatality rate. The aim of this study was to investigate the association between CCHF incidence and meteorological variables in Zahedan district, which has a high incidence of this disease. METHODS: Data about meteorological variables and CCHF incidence was inquired from 2010 to 2017 for Zahedan district. The analysis was performed using univariate and multivariate Seasonal Autoregressive Integrated Moving Average (SARIMA) models and Generalized Additive Models (GAM) using R software. AIC, BIC and residual tests were used to test the goodness of fit of SARIMA models, and R2 was used to select the best model in GAM/GAMM. RESULTS: During the years under study, 190 confirmed cases of CCHF were identified in Zahedan district. The fatality rate of the disease was 8.42%. The disease trend followed a seasonal pattern. The results of multivariate SARIMA showed the (0,1,1) (0,1,1)12 model with maximum monthly temperature lagged 5 months, forecasted the disease better than other models. In the GAM, monthly average temperature lagged 5 months, and the monthly minimum of relative humidity and total monthly rainfall without lag, had a nonlinear relation with the incidence of CCHF. CONCLUSIONS: Meteorological variables can affect CCHF occurrence.

Projection of mortality attributed to heat and cold; the impact of climate change in a dry region of Iran, Kerman.

BACKGROUND: Estimating the effects of climate change on human health can help health policy makers plan for the future. In Iran, there are few studies, about investigating the effects of climate change on mortality. This study aimed to project the effect of low (cold) and high (heat) temperature on mortality in a dry region of Iran, Kerman. METHODS: Mortality attributed to temperature was projected by estimating the temperature-mortality relation for the observed data, projection of future temperatures by the statistical downscaling model (SDSM), and quantifying the attributable fraction by applying the observed temperature-mortality relation on the projected temperature. Climate change projection was done by three climate scenarios base on Representative Concentration Pathways (RCP2.6, RCP4.5 and RCP8.5). Adaptation was considered by using different minimum mortality temperatures (MMT) and risk reduction approaches. The current decade (2010-19) was considered as the reference period. RESULTS: All three climate change scenarios, showed that the mean of temperature will rise about 1 °C, by 2050 in Kerman. The number of deaths attributed to heat were obviously higher than cold in all periods. Assuming no adaptation, over 3700 deaths attributed to temperature will happen in each decade (2020s, 2030s and 2040s) in the future, in which over 3000 deaths will be due to heat and over 450 due to cold. In the predictions, as Minimum Mortality Temperature (MMT) went up, the contribution of heat to mortality slightly decreased, and cold temperature played a more important role. By considering the risk reduction due to adaptation, the contribution of heat in mortality slightly and insignificantly decreased. CONCLUSION: The results showed that although low temperatures will contribute to temperature-related mortality in the future, but heat will be a stronger risk factor for mortality, especially if adaptation is low.

The impact of heat waves on mortality and years of life lost in a dry region of Iran (Kerman) during 2005-2017.

The present study was conducted to compare the impact of heat waves on mortality and years of life lost (YLL) in Kerman, Iran during the years 2005-2017. Daily mean temperature in a combination of intensity and duration were used in order to define heat waves (90, 95, and 98th percentile and ≥ 2, 3, and 4 consecutive days). YLL was calculated according to Iran's life table and by considering the discount rate. In order to investigate the impact of heat waves in different lags and its cumulative effect on mortality and YLL, Poisson and linear models within distributed lag nonlinear models were used respectively. A maximum lag of 14 days was considered. The best model was selected based on AIC (Akaike Information Criteria). The model was adjusted for air pollutants, public holidays, days of the week, and humidity. The average daily mortality and YLL were 10.54 ± 4.31 deaths and 175.58 ± 91.39 years respectively. They were higher in men and in heat waves matching a definition of above the 98th temperature percentile and ≥ 3 days, than others. Except heat waves defined as the 98th percentile and ≥ 4 days, the impact of heat waves on mortality and YLL were the highest at lag 0. The cumulative relative risk of total mortality was significantly higher in heat waves above the 95 and 98th percentiles. The cumulative effect of heat waves on total YLL was significantly higher only above the 98th percentile. Men over 65 years old were the most vulnerable and had the highest mortality and YLL. Heat waves with temperatures above the 98th percentile that lasted at least 2 or 3 consecutive days had a significant effect in increasing both total YLL and mortality in Kerman, Iran.

Attributable risk of mortality associated with heat and heat waves: A time-series study in Kerman, Iran during 2005-2017.

The association between heat or heat waves and mortality should often be reported in a way that makes it sensible by health policymakers. In this study we aimed to assess the effect of heat and heat waves on mortality using attributable risks during 2005-2017. Nine heat waves were defined using a combination of severity and duration of mean daily temperature. Heat wave effects were assessed using added and main effects. Added effects were assessed as a binary variable and main effects were assessed by comparing the median temperature (in heat wave days) to Minimum Mortality Temperature (MMT). The effects of heat, mild heat and extreme heat on mortality were also assessed. Distributed Lag Non-linear Models were used to assess the relations in a bi-dimensional perspective in which the quadratic b-spline was chosen as the basis function for the dimension of the exposure and the natural cubic b-spline was chosen for lag dimension. The backward perspective was used to estimate the attributable risks. The total mortality attributed to non-optimal temperatures for all days was 1.91% (CI 95%: -6.36, 8.47). The attributable risks (AR) were 2.23%, 2.02% and 0.25% for heat, mild heat and extreme heat days, respectively. AR was more for females and the above 65 years old groups than other groups in heat, mild heat and extreme heat days. While the stronger heat waves defined based on temperature above the 95 and 98th percentile had a significant attributable risk for total mortality in the added effects; the weaker heat waves (defined based on temperature above of the 90th percentile (HW1, HW2, HW3) had higher attributable risks, significant for HW1 and HW2, in the main effects. Apparently weaker heat waves show more immediate effects, while stronger heat waves increase mortality over several days.

Physiological Equivalent Temperature Index and mortality in Tabriz (The northwest of Iran).

There are few epidemiological studies about climate change and the effect of temperature variation on health using human thermal indices such as the Physiological Equivalent Temperature (PET) Index in Iran. This study was conducted in Tabriz, the northwest of Iran and Distributed Lag Non-linear Models (DLNM) combined with quasi-Poisson regression models were used to assess the impacts of PET on mortality by using the DLNM Package in R Software. The effect of air pollutants, time trend, day of the week and holidays were controlled as confounders. There was a significant relation between high (30°C, 27°C) and low (-0.8°C, -9.2°C and -14.2°C) PET and total (non-accidental) mortality; and a significant increase in respiratory and cardiovascular deaths in high PET values. Heat stress increased Cumulative Relative Risk (CRR) for total (non-accidental), respiratory and cardiovascular mortality significantly (CRR Non Accidental Death, PET=30°C, lag 0-30=1.67, 95%CI: 1.31-2.13; CRR Respiratory Death, PET=30°C, lag 0-13=1.88, 95%CI: 1.30-2.72; CRR Cardiovascular Death, PET=30°C, lag0-30=1.67 95%CI: 1.16-2.40). Heat stress increases the risk of total (non-accidental), respiratory mortality, but cold stress decreases the risk of total (non-accidental) mortality in Tabriz which is one of the cold cities of Iran.

Diurnal temperature range and mortality in Urmia, the Northwest of Iran.

Diurnal Temperature Range (DTR) is a meteorological index which represents temperature variation within a day. This study assesses the impact of high and low values of DTR on mortality. Distributed Lag Non-linear Models combined with a quasi-Poisson regression model was used to assess the impact of DTR on cause, age and gender specific mortality, controlled for potential confounders such as long-term trend of daily mortality, day of week effect, holidays, mean temperature, humidity, wind speed and air pollutants. As the effect of DTR may vary between the hot season (from May to October) and cold season (from November to April of the next year), we conducted analyses separately for these two seasons. In high DTR values (all percentiles), the Cumulative Relative Risk (CRR) of Non-Accidental Death, Respiratory Death and Cardiovascular Death increased in the full year and hot season, and especially in lag (0-6) of the hot season. In the cold season and high DTR values (all percentiles), the CRR of Non-Accidental Death and Cardiovascular Death decreased, but the CRR of Respiratory Death increased. Although there was no clear significant effect in low DTR values. High values of DTR increase the risk of mortality, especially in the heat season, in Urmia, Iran.

Multi-criteria analysis of site selection for groundwater recharge with treated municipal wastewater.

Geographic information systems (GIS) and remote sensing techniques are used as a decision support system to identify potential soil aquifer treatment (SAT) sites for groundwater recharge of Kerman aquifer, which is located in the southeast of Iran. These sites are identified using a single-objective multi-criteria analysis. To ensure technical feasibility, environmental sustainability, social acceptability and economical viability a number of criteria are considered for the site selection. The criteria selected for the different variables and acceptable ranges are based on standards published in national and international guidelines and technical documents. Multi-criteria evaluation was performed combining all produced thematic maps by means of the weighted index overlay method in order to select sites meeting all the criteria. The resulting map of this analysis shows potential sites are located in the north, southwest and southeast of the study area. Considering field observations, a potential site, which is located in the southwest of the study area, is proposed as the most suitable site for SAT. The result indicates that the study area has sufficient required suitable space for groundwater recharge with treated wastewater.

The role of season and climate in the incidence of congenital hypothyroidism in Kerman province, Southeastern Iran.

BACKGROUND: Congenital hypothyroidism (CH) is a common endocrine disease and an important cause of mental retardation. The purpose of this study was to investigate the probable role of season and climatic factors in the incidence of CH in Kerman province, Iran. METHODS: Incidence data were collected from the CH screening program files from 2005 to 2011 in Kerman province, a number of 288,437 infants were included in the study. Climate data were collected from the Meteorological Office. The relations were tested by χ2-test, Pearson correlation, and negative binomial regression. RESULTS: The overall incidence of CH in Kerman province was 2.68 per 1000 births. There was a significant difference in both the monthly and seasonal incidence of CH (p<0.05). There were a few significant, but weak correlation between some climatic factors and the incidence of CH in some regions, but the results were inconsistent. CONCLUSIONS: It seems like there is no clear relation between CH incidence and climate factors, in Kerman Province. However, CH incidence was highest in October (Autumn) and lowest in June (Summer).

The relation between climatic factors and malaria incidence in Kerman, South East of Iran.

BACKGROUND AND OBJECTIVES: Malaria is among the most important parasitic diseases, and is one of the endemic diseases in Iran. This disease is often known as a disease related to climate changes. Due to the health and economic burden of malaria and the location of Kerman province in an area with high incidence of malaria, the present study aimed to evaluate the effects of climatic factors on the incidence of this disease. MATERIAL AND METHODS: Data on the incidence of malaria in Kerman province was inquired from Kerman and Jiroft Medical Universities and climatic variables were inquired from the meteorological organization of Kerman. The data was analyzed monthly from 2000 to 2012. Variations in incidence of malaria with climatic factors were assessed with negative binomial regression model in STATA11software. In order to determine the delayed effects of meteorological variables on malaria incidence, cross-correlation analysis was done with Minitab16. RESULTS: The most effective meteorological factor on the incidence of malaria was temperature. As the mean, maximum, and minimum of monthly temperature increased, the incidence rate raised significantly. The multivariate negative binomial regression model indicates that a 1 °C increase in maximum temperature in a given month was related to a 15% and 19% increase on malaria incidence on the same and subsequent month, respectively (p-value = 0.001). Humidity and Rainfall were not significant in the adjusted model. CONCLUSION: Temperature is among the effective climatic parameters on the incidence of malaria which should be considered in planning for control and prevention of the disease.