BackgroundRoad 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.MethodsA 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.ResultsA 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).ConclusionOur 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.
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.
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.
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