Background: Vaccination is a key intervention to prevent COVID-19. Many vaccines are administered globally, yet there is not much evidence regarding their safety and adverse effects. Iran also faces this challenge, especially as data regarding the Sputnik V vaccine is sparse. Therefore, the aim of this study is to determine the adverse effects of the most commonly used vaccines in Iran. Methods: Using a retrospective cohort study design, 6600 subjects aged 18 years or older who had received two doses of any of the three COVID-19 vaccines (Sinopharm, AstraZeneca, and Sputnik V) were selected using a random sampling method between March and August 2021. Subjects were asked about any adverse effects of the vaccines by trained interviewers via telephone interview. Vaccine-related adverse effects in individuals during the first 72 h and subsequently following both doses of the vaccines were determined. The demographic variables, type of administered vaccine, adverse effects, and history of the previous infection with COVID-19 were collected. Descriptive statistics (mean, standard deviation) and analytical statistics (Chi-squared and Wilcoxon tests) were performed at a 95% significance level using STATA software version 15 (STATA Corp, College Station, TX, USA). Results: From 6600 participants, 4775 responded (response rate = 72.3%). Of the participants, 1460 (30.6%) received the AstraZeneca vaccine, 1564 (32.8%) received the Sinopharm vaccine and 1751 (36.7%) received the Sputnik V vaccine. 2653 participants (55.56%) reported adverse effects after the first dose and 1704 (35.7%) after the second dose. Sputnik V caused the most adverse effects with 1449 (82.7%) vaccine recipients reporting symptoms after the first or second dose, compared with 1030 (70.5%) for AstraZeneca and only 585 (37.4%) for the Sinopharm vaccine. The most common adverse effects after the first dose were fatigue (28.37%), chill/fever (26.86%), and skeletal pain (22.38%). These three adverse effects were the same for the second dose, although their prevalence was lower. Conclusions: In this study, we demonstrate that the Sputnik V vaccine has the highest rate of adverse effects, followed by the AstraZeneca and Sinopharm vaccines. COVID-19 vaccines used in Iran are safe and there were no reports of serious adverse effects.

Background: Surgical Site Infections (SSIs) are among the leading causes of the postoperative complications. This study aimed at investigating the epidemiologic characteristics of orthopedic SSIs and estimating the under-reporting of registries using the capture-recapture method. Methods: This study, which was a registry-based, cross-sectional one, was conducted in six educational hospitals in Tehran during a one-year period, from March, 2017 to March, 2018. The data were collected from two hospital registries (National Nosocomial Infection Surveillance System (NNIS) and Health Information Management database (HIM)). First, all orthopedic SSIs registered in these sources were used to perform capture-recapture (N = 503). Second, 202 samples were randomly selected to assess patients` characteristics. Results: Totally, 76.24% of SSIs were detected post-discharge. Staphylococcus aureus (11.38%) was the most frequently detected bacterium in orthopedic SSIs. The median time between the detection of a SSI and the discharge was 17 days. The results of a study done on 503 SSIs showed that the coverage of NNIS and HIM was 59.95 and 65.17%, respectively. After capture-recapture estimation, it was found that about 221 of orthopedic SSIs were not detected by two sources among six hospitals and the real number of SSIs were estimated to be 623 ± 36.58 (95% CI, 552–695) and under-reporting percentage was 63.32%. Conclusion: To recognize the trends of SSIs mortality and morbidity in national level, it is significant to have access to a registry with minimum underestimated data. Therefore, according to the weak coverage of NNIS and HIM among Iranian hospitals, a plan for promoting the national Infection Prevention and Control (IPC) programs and providing updated protocols is recommended.

Background: The distribution of population-level real-time reverse transcription-polymerase chain reaction (RT-PCR) cycle threshold (Ct) values as a proxy of viral load may be a useful indicator for predicting COVID-19 dynamics. Objective: The aim of this study was to determine the relationship between the daily trend of average Ct values and COVID-19 dynamics, calculated as the daily number of hospitalized patients with COVID-19, daily number of new positive tests, daily number of COVID-19 deaths, and number of hospitalized patients with COVID-19 by age. We further sought to determine the lag between these data series. Methods: The samples included in this study were collected from March 21, 2021, to December 1, 2021. Daily Ct values of all patients who were referred to the Molecular Diagnostic Laboratory of Iran University of Medical Sciences in Tehran, Iran, for RT-PCR tests were recorded. The daily number of positive tests and the number of hospitalized patients by age group were extracted from the COVID-19 patient information registration system in Tehran province, Iran. An autoregressive integrated moving average (ARIMA) model was constructed for the time series of variables. Cross-correlation analysis was then performed to determine the best lag and correlations between the average daily Ct value and other COVID-19 dynamics–related variables. Finally, the best-selected lag of Ct identified through cross-correlation was incorporated as a covariate into the autoregressive integrated moving average with exogenous variables (ARIMAX) model to calculate the coefficients. Results: Daily average Ct values showed a significant negative correlation (23-day time delay) with the daily number of newly hospitalized patients (P=.02), 30-day time delay with the daily number of new positive tests (P=.02), and daily number of COVID-19 deaths (P=.02). The daily average Ct value with a 30-day delay could impact the daily number of positive tests for COVID-19 (β=–16.87, P<.001) and the daily number of deaths from COVID-19 (β=–1.52, P=.03). There was a significant association between Ct lag (23 days) and the number of COVID-19 hospitalizations (β=–24.12, P=.005). Cross-correlation analysis showed significant time delays in the average Ct values and daily hospitalized patients between 18-59 years (23-day time delay, P=.02) and in patients over 60 years old (23-day time delay, P<.001). No statistically significant relation was detected in the number of daily hospitalized patients under 5 years old (9-day time delay, P=.27) and aged 5-17 years (13-day time delay, P=.39). Conclusions: It is important for surveillance of COVID-19 to find a good indicator that can predict epidemic surges in the community. Our results suggest that the average daily Ct value with a 30-day delay can predict increases in the number of positive confirmed COVID-19 cases, which may be a useful indicator for the health system.

Background: Estimating the net reproductive number of COVID-19 virus can reveal the rate and severity of the disease transmission in the Iranian society to health researchers and practitioners. Also, it signifies the importance of the disease to the community. Thus, the aim of this study was to determine the net reproductive number of COVID-19 in Iran and its different provinces.

Background: Many factors contribute to the spreading of hospital-acquired infections (HAIs). Objective: This study aimed to standardize the HAI rate using prediction models in Iran based on the National Healthcare Safety Network (NHSN) method. Methods: In this study, the Iranian nosocomial infections surveillance system (INIS) was used to gather data on patients with HAIs (126,314 infections). In addition, the hospital statistics and information system (AVAB) was used to collect data on hospital characteristics. First, well-performing hospitals, including 357 hospitals from all over the country, were selected. Data were randomly split into training (70%) and testing (30%) sets. Finally, the standardized infection ratio (SIR) and the corrected SIR were calculated for the HAIs. Results: The mean age of the 100,110 patients with an HAI was 40.02 (SD 23.56) years. The corrected SIRs based on the observed and predicted infections for respiratory tract infections (RTIs), urinary tract infections (UTIs), surgical site infections (SSIs), and bloodstream infections (BSIs) were 0.03 (95% CI 0-0.09), 1.02 (95% CI 0.95-1.09), 0.93 (95% CI 0.85-1.007), and 0.91 (95% CI 0.54-1.28), respectively. Moreover, the corrected SIRs for RTIs in the infectious disease, burn, obstetrics and gynecology, and internal medicine wards; UTIs in the burn, infectious disease, internal medicine, and intensive care unit wards; SSIs in the burn and infectious disease wards; and BSIs in most wards were >1, indicating that more HAIs were observed than expected. Conclusions: The results of this study can help to promote preventive measures based on scientific evidence. They can also lead to the continuous improvement of the monitoring system by collecting and systematically analyzing data on HAIs and encourage the hospitals to better control their infection rates by establishing a benchmarking system.