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The present study aimed to explore the interaction effect between PM10 and season as well as temperature on hospital admissions (HAs) in a region in North East of Iran. Distributed lag non-linear m...

Introduction: Mental disorders (depression, anxiety and stress) cause disability and aggravate various diseases, including diabetes mellitus and its common complication such as diabetic nephropathy. Objectives: The current study aimed to investigate the prevalence of mental health disorders (depression, anxiety and stress) and their associated factors among the Iranian older people with type 2 diabetes mellitus (T2DM). Patients and Methods: In this cross-sectional study, 501 subjects with T2DM and 551 subjects without T2DM were selected by a multistage stratified sampling method from Yazd Greater Area, Iran. Delphi method and experts panel were used for selection of priorities of effective factors. The obtained data were analyzed using the chi-square test and Stata software version 14. A P value of ≤0.05 was considered statistically significant. Results: The prevalence of moderate to severe depression and anxiety was 51.9% and 61.9%, among diabetic, 22% and 33.4% among non-diabetic groups, respectively. According to multiple logistic regression analysis results in diabetics group specified female gender (OR=1.97, 95% CI=1.03-3.75), poor sleep quality (OR=1.08, 95% CI=1.02-1.15), decreased social support (OR=0.89, 95% CI=0.54-1.68), and body mass index (BMI) ≥30 kg/m² (OR=1.6, 95% CI=1.2-5.63) for depression, as well as female gender (OR=2.97, 95% CI=1.5- 5.87), BMI ≥30 kg/m² (OR=2.69, 95% CI=1.22-5.96), and decreased social support (OR=0.87, 95% CI=0.78-0.98) for anxiety as the predicting characteristics. Conclusion: The present research findings revealed a significantly higher prevalence of depression and anxiety in older people with T2DM rather than older people without T2DM. Therefore, appropriate interventions such as screenings and psychiatric evaluations are necessary to diagnose and treat depression and anxiety in this population, especially the females to avoid diabetic complications like diabetic kidney disease.

This study was conducted in order to explore the effect of thermal comfort on all-cause mortality using three indices in different lag times, in a semi-arid to dry region of Iran. Three thermal comfort indices based on the energy balance of the human body including physiologically equivalent temperature (PET), predicted mean vote (PMV), and standard effective temperature (SET) were used to assess the effects of thermal comfort on mortality. Distributed lag non-linear models were used to assess the relation. The natural cubic spline was chosen as the basis function for the space of predictors and lags, with 4 degrees of freedom. All three indices showed the same pattern in general, but the relative risk for PMV values were more than the other indices in different lags. For all three indices, lag 0 had the highest relative risk of mortality in warm and hot indices. The relative risk for warm and hot values was more than cool and cold values in lag 0, and for the PMV index, it was larger than the two other indices. These results were different in lags 5 to 8, and the relative risks for cool and cold values were more than warm and hot values. This study showed that heat stress has a stronger and more immediate adverse effect on mortality than cold stress. Also, the elderly and females are more vulnerable than others. The most apparent effect was seen in lags 0–12.

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.

Background: This study aimed to estimate hospitalization risk/number attributed to air extreme temperatures using time-stratified case crossover study and distributed lag non-linear model in a region of Iran during 2015-2019.Methods: A time-stratified case crossover design based on aggregated exposure data was used in this study. In order to have no overlap bias in the estimations, a fixed and disjointed window by using one-month strata was used in the design. A conditional Poisson regression model allowing for over dispersion (Quasi-Poisson) was applied into Distributed Lag Non-linear Model (DLNM). Different approaches were applied to estimate Optimum Temperature (OT). In the model, the interaction effect between temperature and humidity was assessed to see if the impact of heat or cold on Hospital Admissions (HAs) are different between different levels of humidity.Results: The cumulative effect of heat during 21 days was not significant and it was the cold that had significant cumulative adverse effect on all groups. While the number of HAs attributed to any ranges of heat, including medium, high, extreme and even all values were negligible, but a large number was attributable to cold values; about 10000 HAs were attributable to all values of cold temperature, of which about 9000 were attributed to medium range and about 1000 and less than 500 were attributed to high and extreme values of cold, respectively.Conclusion: This study highlights the need for interventions in cold seasons by policymakers. The results inform researchers as well as policy makers to address both men and women and elderly when any plan or preventive program is developed in the area under study.

Background The SARS-CoV-2 virus pandemic is primarily transmitted by direct contact between infected and uninfected people, though, there are still many unknown factors influencing the survival and transmission of the virus. Air temperature is one of the main susceptible factors. This study aimed to explore the impact of air and land surface temperatures on Covid-19 transmission in a region of Iran. Method Daily Land Surface Temperature (LST) measured by satellite and Air Temperature measured by weather station were used as the predictors of Covid-19 transmission. The data were obtained from February 2020 to April 2021. Spatio-temporal kriging was used in order to predict LST in some days in which no image was recorded by the satellite. The validity of the predicted values was assessed by Bland-Altman technique. The impact of the predictors was analyzed by Distributed Lag Non-linear Model (DLNM). In addition to main effect of temperature, its linear as well as non-linear interaction effect with relative humidity were considered using Generalized Additive Model (GAM) and a bivariate response surface model. Sensitivity analyses were done to select models’ parameters, autocorrelation model and function of associations. Results The dose-response curve revealed that the impact of both predictors was not obvious, though, the risk of transmission tended to be positive due to low values of temperatures. Although the linear interaction effect was not statistically significant, but joint patterns showed that the impact of both LST and AT tended to be different when humidity values were changed. Conclusion However the findings suggested that both LST and AT were not statistically important predictors, but they tended to predict the Covid-19 transmission in some lags. Because of local based evidence, the wide confidence intervals and then non-significant values should be cautiously interpreted.

Enterococci are members of human gut microbiota which their colonization has been demonstrated even before birth. This indicates their importance in infant health. As this population is dynamic and varies with age, this study was designed to assess and compare the antibacterial effects of enterococci from breast-fed infants and those from adults. Fecal isolates of enterococci were isolated from infants and healthy adults and were identified to the species level by phenotypic and genotypic methods. Further, they were evaluated for their potential to exert antibacterial effect against ten standard bacterial strains using bilayer spot test. Of a total of eighty-nine recovered enterococcal isolates, Enterococcus faecium and E. faecalis were the most common species (98%) and showed inhibitory effects at least against one indicator strain. Comparison between isolates from two studied groups showed that isolates from neonates introduced significantly higher growth inhibitory effects against six indicator strains (P

Background: External causes include accidents, injuries or health problems that arise immediately after accidents or other external factors. The purpose of this review was to collect information about the relation between temperature and mortality due to external causes. Methods: A systematic search of articles was conducted in PubMed, Scopus, Science Direct, Embase, and Web of Science until January 2017, with no restriction. Broad searches were conducted in all fields, using key words related to ambient temperature exposure and deaths due to external causes. Results: Nine articles were included in the study. Except two articles, one of which had been designed in a cross-sectional way and the other that was retrospective, the rest of the articles were ecological. The outcomes reported were heterogeneous and different metrics had been used in the results; therefore conducting of meta-analysis was not possible. Five articles had found direct and significant relations between high temperature and deaths due to external causes. Only one study found no relation. In the case of cold, one study showed that the proportion of death due to external causes was low during cold waves. Another study showed that there was no relation between cold waves and mortality due to external causes. But one article showed that death due to external causes increased on cold days, significantly. Conclusion: The results of this systematic review showed that high temperatures (heat) were more likely to cause mortality due to external causes than low temperatures (cold). Due to the small number of studies in this field, especially in the context of cold and death due to external causes, it is difficult to make robust conclusions.

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.

Background: Traffic accidents is one of the main causes of mortality, and has a high impact on overall health and development. Iran is a country with a high prevalence of traffic accidents, and this has been a big concern. In this study, we aimed to study the past decade’s trend in mortality due to traffic accident in Kerman, Iran, and its relation with ambient temperature by using Generalized Additive Mixed Models. Methods: Meteorological data, such as temperature, humidity, and wind speed were obtained from the Meteorological Organization of Kerman and traffic mortality data were obtained from the Health Deputy of Kerman University of Medical Sciences. In order to explore the relations, Generalized Additive Mixed Models (GAMMs) were used. Additive models are nonparametric forms of regression models in which the summation of regression coefficients × explanatory variables of a linear regression is replaced by a summation of arbitrary smooth functions of the explanatory variables. Results: About 2900 deaths due to traffic accident occurred from March 2005 until May 2017 in Kerman. The monthly average deaths were 20.01(±14.43) that was more for men than women (15.42 vs 4.58), and was highest in the age 31-60 group. Non-linear effect of time on mortality was significant. There was a negative effect of time on mortality until 2009, while all other confounders were adjusted. After a short-term increasing trend till early 2011, and then a decreasing trend until 2013, an increasing trend was observed again. Holding pollutants, temperature and year (trend effect) stable, mortality tended to be higher for the first half of year, with an increasing slope in the first 3 months of the year. Monthly average temperatures below 13°C was related with increased mortality, but average temperatures around 20°C had no effect on mortality. High temperatures (>23°C) were associated with less mortality. Conclusions: Most traffic accident related deaths occurred in first half of year (spring and summer), and it is probably because of increased inter-city traveling during the summer break. Our results showed that high extreme temperatures have less contribution on the mortality. Keywords: Accident, Mortality, Trend, Ambient Temperature

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.

Introduction: The present study was conducted to compare the added effect of heat waves on mortality and years of life lost in Kerman, Iran during the years 2005-2016.Methods: Daily mean temperatur...

Purpose: The aim of this study was to curtail the full version of instrument by 50% (short version) and 25% (ultra-short version) of its original size (22 items) and evaluate the reliability and validity of both versions. Methods: The experts’ comments were used to make short and ultra-short versions. The two versions were evaluated by use of indicators related to reliability (internal consistency and test-retest) and diagnostic validity, diagnosis of marginal areas from central areas using ROC curve analysis, among 1600 households. Also, intraclass correlation coefficient (ICC), Pearson correlation coefficient and Bland-Altman plot were used in order to assess agreement between full and shortened versions. Results: The short and ultra-short versions were included 10 items and 6 items, respectively. Cronbach’s Alpha was 0.83 and 0.65 for short version and ultra-short version respectively. The Pearson correlation coefficient and the coefficient of agreement were desirable and acceptable for both versions (p