Prevalence of non-alcoholic fatty liver and its related factors in Iran: Systematic review and meta-analysis.

Non-alcoholic fatty liver disease (NAFLD) is a systemic disorder with a complex multifactorial and heterogeneous pathogenesis and has become the most common cause of chronic liver disease in many countries around the world. Numerous studies in Iran have presented different results on the prevalence and risk factors of NAFLD, in this study, which has been done in a systematic review and meta-analysis, provides a good estimate of the prevalence and risk factors of the disease in Iran. Following the peer review of electronic search strategies (PRESS and the preferred reporting items for systematic reviews and meta-analyses [PRISMA] statement, we searched Web of Science, PubMed, Embase, Scopus, and Persian scientific searcher (Elmnet) from inception to September 19, 2022. In the present study, 71 articles were reviewed for qualitative and meta-analysis. The overall mean prevalence of NAFLD in children studies was 22.4% (95% confidence interval [CI]: 10.9% to 33.9%). The prevalence was notably higher in adult studies 40.5% (95% CI: 35.1% to 46%). In 24 studies, the association between NAFLD and sex was reported, 10 of which showed significant relationships. Out of 46 studies observed that NAFLD prevalence increased significantly with body mass index (BMI). Eight out of 14 studies reported significant associations between FBS and NAFLD in children's studies. Though Iran has a high NAFLD prevalence compared to most areas, and due to the unfavorable situation of risk factors contributing to the NAFLD, it is necessary to take the necessary interventions to control these risk factors and prevent NAFLD.

Reactogenicity within the first week after Sinopharm, Sputnik V, AZD1222, and COVIran Barekat vaccines: findings from the Iranian active vaccine surveillance system.

BACKGROUND: This study aimed to evaluate the reactogenicity effects of COVID-19 vaccines, used in Iran. METHODS: At least 1000 people were followed up with phone calls or self-report in a mobile application within 7 days after vaccination. Local and systemic reactogenicities were reported overall and by subgroups. RESULTS: The presence of one or more local and systemic adverse effects after the first dose of vaccines was 58.9% [(95% Confidence Intervals): 57.5-60.3)] and 60.5% (59.1-61.9), respectively. These rates were reduced to 53.8% (51.2-55.0) and 50.8% (48.8-52.7) for the second dose. The most common local adverse effect reported for all vaccines was pain in the injection site. During the first week after the first dose of vaccines, the frequency of the pain for Sinopharm, AZD1222, Sputnik V, and Barekat was 35.5%, 86.0%, 77.6%, and 30.9%, respectively. The same rates after the second dose were 27.3%, 66.5%, 63.9%, and 49.0%. The most common systemic adverse effect was fatigue. In the first dose, it was 30.3% for Sinopharm, 67.4% for AZD1222, 47.6% for Sputnik V, and 17.1% for Barekat. These rates were reduced to 24.6%, 37.1%, 36.5%, and 19.5%, in the second dose of vaccines. AZD1222 had the highest local and systemic adverse effects rates. The odds ratio of local adverse effects of the AZD1222 vaccine compared to the Sinopharm vaccine were 8.73 (95% CI 6.93-10.99) in the first dose and 4.14 (95% CI 3.32-5.17) in the second dose. Barekat and Sinopharm had the lowest frequency of local and systemic adverse effects. Compared to Sinopharm, systemic adverse effects were lower after the first dose of Barekat (OR = 0.56; 95% CI 0.46-0.67). Reactogenicity events were higher in women and younger people. Prior COVID-19 infection increased the odds of adverse effects only after the first dose of vaccines. CONCLUSIONS: Pain and fatigue were the most common reactogenicities of COVID-19 vaccination. Reactogenicities were less common after the second dose of the vaccines. The adverse effects of AZD1222 were greater than those of other vaccines.

COVID-19 cases, hospitalizations and deaths after vaccination: a cohort event monitoring study, Islamic Republic of Iran.

Objective: To investigate the incidence of coronavirus disease 2019 (COVID-19) cases, hospitalizations and deaths in Iranians vaccinated with either AZD1222 Vaxzevria, CovIran® vaccine, SARS-CoV-2 Vaccine (Vero Cell), Inactivated (lnCoV) or Sputnik V. Methods: We enrolled individuals 18 years or older receiving their first COVID-19 vaccine dose between April 2021 and January 2022 in seven Iranian cities. Participants completed weekly follow-up surveys for 17 weeks (25 weeks for AZD1222) to report their COVID-19 status and hospitalization. We used Cox regression models to assess risk factors for contracting COVID-19, hospitalization and death. Findings: Of 89 783 participants enrolled, incidence rates per 1 000 000 person-days were: 528.2 (95% confidence interval, CI: 514.0-542.7) for contracting COVID-19; 55.8 (95% CI: 51.4-60.5) for hospitalization; and 4.1 (95% CI: 3.0-5.5) for death. Compared with SARS-CoV-2 Vaccine (Vero Cell), hazard ratios (HR) for contracting COVID-19 were: 0.70 (95% CI: 0.61-0.80) with AZD1222; 0.73 (95% CI: 0.62-0.86) with Sputnik V; and 0.73 (95% CI: 0.63-0.86) with CovIran®. For hospitalization and death, all vaccines provided similar protection 14 days after the second dose. History of COVID-19 protected against contracting COVID-19 again (HR: 0.76; 95% CI: 0.69-0.84). Diabetes and respiratory, cardiac and renal disease were associated with higher risks of contracting COVID-19 after vaccination. Conclusion: The rates of contracting COVID-19 after vaccination were relatively high. SARS-CoV-2 Vaccine (Vero Cell) provided lower protection against COVID-19 than other vaccines. People with comorbidities had higher risks of contracting COVID-19 and hospitalization and should be prioritized for preventive interventions.

Study protocol: cohort event monitoring for safety signal detection after vaccination with COVID-19 vaccines in Iran.

BACKGROUND: New vaccines that are initially approved in clinical trials are not completely free of risks. Systematic vaccine safety surveillance is required for ensuring safety of vaccines. This study aimed to provide a protocol for safety monitoring of COVID-19 vaccines, including Sputnik V, Sinopharm (BBIBP-CorV), COVIran Barekat, and AZD1222. METHODS: This is a prospective cohort study in accordance with a template provided by the World Health Organization. The target population includes citizens of seven cities in Iran who have received one of the available COVID-19 vaccines according to the national instruction on vaccination. The participants are followed for three months after they receive the second dose of the vaccine. For each type of vaccine, 30,000 people will be enrolled in the study of whom the first 1,000 participants are in the reactogenicity subgroup. The reactogenicity outcomes will be followed seven days after vaccination. Any hospitalization, COVID-19 disease, or other minor outcomes will be investigated in weekly follow-ups. The data are gathered through self-reporting of participants in a mobile application or phone calls to them. The study outcomes may be investigated for the third and fourth doses of vaccines. Other long-term outcomes may also be investigated after the expansion of the follow-up period. We have planned to complete data collection for the current objectives by the end 2022. DISCUSSION: The results of this study will be published in different articles. A live dashboard is also available for managers and policymakers. All data will be available on reasonable requests from the corresponding author.The use of the good and comprehensive guidelines provided by WHO, along with the accurate implementation of the protocol and continuous monitoring of the staff performance are the main strengths of this study which may be very useful for policymaking about COVID-19 vaccination.

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.

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 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.

Association of cigarette smoking, obesity, and underlying medical conditions with COVID-19 hospitalization and mortality in Iran: A nationwide retrospective ecological study.

Background: Coronavirus Disease 2019 (COVID-19) has resulted in a considerable number of deaths worldwide. This ecological study aimed to explore the relationship between COVID-19 hospitalization and mortality with smoking, obesity, and underlying conditions in Iran. Methods: Provincial-level COVID-19 data were obtained from the official reports. Two outcomes were assessed: the total number of hospitalizations and deaths. Data on underlying health conditions, cigarette smoking, and obesity were obtained from national surveys. Negative binomial regression was used to report incident rate (IRR) ratios. Results: As of April 22, 2020, a total number of 43 950 lab-confirmed COVID-19 hospitalizations and 5391confirmed COVID-19 deaths were officially reported. Adjusting for underdetection to cover the number of clinically-confirmed COVID-19 cases, a total of 76 962 additional hospitalizations (ie, total lab- and clinically-confirmed hospitalizations = 120 912; 175% increase) and 7558 additional deaths (ie, total lab- and clinically-confirmed deaths = 12 949; 140% increase) were estimated during the same period. Provinces with a higher prevalence of obesity (IRR: 2.75, 95% CI: 1.49, 5.10), cigarette smoking (1.81; 95% CI: 1.01, 3.27), hypertension (1.88; 95% CI: 1.03, 3.44), and diabetes mellitus (1.74; 95% CI: 0.96, 3.16) had a higher likelihood of COVID-19 death rates. Conclusion: Inequality in COVID-19 hospitalization and mortality was observed in provinces whose populations had underlying diseases, in particular, obesity, cigarette smoking, hypertension, and diabetes.

Population Size Estimation of Tramadol Misusers in Urban Population in Iran: Synthesis of Methods and Results.

BACKGROUND: Estimating the population who use drugs is essential for planning, monitoring, and evaluation of substance use prevention and treatment. This study aims to estimate the population who misuse tramadol in urban population in Iran. METHODS: We used the wisdom of the experts (WOTE) and network scale-up (NSU) methods to calculate the population of tramadol misusers in 10 provincial capitals of Iran, in 2016. The WOTE was conducted among pharmacists in drugstores and the personnel of traditional medicinal herbs stores. They guessed the best estimation of tramadol misusers population in their cities. The NSU method was conducted among the general population and participants were questioned about ever and daily, non-medical use of tramadol during last 12 months in their network. The median of the methods was used to calculate the proportion of the adult population (15-49 years old). FINDINGS: The population size of tramadol misusers in studied cities was 83300 [95% uncertainty limits (UL): 47960-256220]. This corresponded to 6.6 per 1000 (95% UL: 3.88-20.30) of the 15-49-year-old population. The projected number of tramadol misusers for all 31 provincial capitals was 118290 (95% UL: 68100-363130840) and 212440 (95% UL: 122310-653410) for all urban areas. NSU also estimated the number of people who misuse tramadol on daily basis. These numbers were 52000 (95% UL: 19940-176570) for studied cities, 73840 (95% UL: 28320-250740) for all 31 provincial capital cities, and 132610 (95% UL: 50860-450310) for all urban areas in Iran. CONCLUSION: This study presents information on high prevalence of tramadol misuse in urban population. We need national control measures and demand reduction programs to control tramadol misuse.

Assessment and prediction of road accident injuries trend using time-series models in Kurdistan.

BACKGROUND: Road traffic accidents are commonly encountered incidents that can cause high-intensity injuries to the victims and have direct impacts on the members of the society. Iran has one of the highest incident rates of road traffic accidents. The objective of this study was to model the patterns of road traffic accidents leading to injury in Kurdistan province, Iran. METHODS: A time-series analysis was conducted to characterize and predict the frequency of road traffic accidents that lead to injury in Kurdistan province. The injuries were categorized into three separate groups which were related to the car occupants, motorcyclists and pedestrian road traffic accident injuries. The Box-Jenkins time-series analysis was used to model the injury observations applying autoregressive integrated moving average (ARIMA) and seasonal autoregressive integrated moving average (SARIMA) from March 2009 to February 2015 and to predict the accidents up to 24 months later (February 2017). The analysis was carried out using R-3.4.2 statistical software package. RESULTS: A total of 5199 pedestrians, 9015 motorcyclists, and 28,906 car occupants' accidents were observed. The mean (SD) number of car occupant, motorcyclist and pedestrian accident injuries observed were 401.01 (SD 32.78), 123.70 (SD 30.18) and 71.19 (SD 17.92) per year, respectively. The best models for the pattern of car occupant, motorcyclist, and pedestrian injuries were the ARIMA (1, 0, 0), SARIMA (1, 0, 2) (1, 0, 0)12, and SARIMA (1, 1, 1) (0, 0, 1)12, respectively. The motorcyclist and pedestrian injuries showed a seasonal pattern and the peak was during summer (August). The minimum frequency for the motorcyclist and pedestrian injuries were observed during the late autumn and early winter (December and January). CONCLUSION: Our findings revealed that the observed motorcyclist and pedestrian injuries had a seasonal pattern that was explained by air temperature changes overtime. These findings call the need for close monitoring of the accidents during the high-risk periods in order to control and decrease the rate of the injuries.