Twelve-hour before Driving Prevalence of Alcohol and Drug Use among Heavy Vehicle Drivers in South East of Iran Using Network Scale Up

Background: Heavy vehicle drivers spend a great deal of time away from their families. This issue and other difficulties around their job may increase risky behaviors among them. The current study aims to investigate the prevalence of opium drugs, stimulants, cannabis, and alcohol use 12 hours before driving among heavy vehicle drivers. Methods: We selected two sites that were in charge of medical examination of drivers and recruited 363 drivers of heavy vehicles (trucks, trailers, and buses). We asked drivers about total number of drivers they knew and number of drivers who experienced use of different types of drugs. The data were analyzed using Network Scale Up Method (NSUM). Findings: Mean of age and job experience was 43.28 ± 10.04 years and 16.07 ± 9.67 years, respectively. The highest and lowest prevalence of drug use related to opium-based drugs at 12.8% to 14.0% and simulants at 1.97% to 2.84%, respectively. The prevalence of alcohol use 12 hours before driving was 4%. Conclusion: 12-hour before driving prevalence of opium-based drugs among drivers was high. This might put them in higher risk of road accidents. There is a need to design appropriate educational programs for them

A review of methods to estimate the visibility factor for bias correction in network scale-up studies

Network scale-up is an indirect size estimation method, in which participants are questioned on sensitive behaviors of their social network members. Therefore, the visibility of the behavior affects the replies and estimates. Many attempts to estimate visibility have been made. The aims of this study were to review the main methods used to address visibility and to provide a summary of reported visibility factors (VFs) across populations. We systematically searched relevant databases and Google. In total, 15 studies and reports that calculated VFs were found. VF calculation studies have been applied in 9 countries, mostly in East Asia and Eastern Europe. The methods applied were expert opinion, comparison of NSU with another method, the game of contacts, social respect, and the coming-out rate. The VF has been calculated for heavy drug users, people who inject drugs (PWID), female sex workers (FSWs) and their clients, male who have sex with male (MSM), alcohol and methamphetamine users, and those who have experienced extra-/pre-marital sex and abortion. The VF varied from 1.4% in Japan to 52.0% in China for MSM; from 34.0% in Ukraine to 111.0% in China for FSWs; and from 12.0% among Iranian students to 57.0% in Ukraine for PWID. Our review revealed that VF estimates were heterogeneous, and were not available for most settings, in particular the Middle East and North Africa region, except Iran. More concrete methodologies to estimate the VF are required

Clinical Risk Factors of Need for Intensive Care Unit Admission of COVID-19 Patients; a Cross-sectional Study

Introduction: It could be beneficial to accelerate the hospitalization of patients with the identified clinical risk factors of intensive care unit (ICU) admission, in order to control and reduce COVID-19-related mortality. This study aimed to determine the clinical risk factors associated with ICU hospitalization of COVID-19 patients. Methods: The current research was a cross-sectional study. The study recruited 7182 patients who had positive PCR tests between February 23, 2020, and September 7, 2021 and were admitted to Afzalipour Hospital in Kerman, Iran, for at least 24 hours. Their demographic characteristics, underlying diseases, and clinical parameters were collected. In order to analyze the relationship between the studied variables and ICU admission, multiple logistic regression model, classification tree, and support vector machine were used.  Results: It was found that 14.7 percent (1056 patients) of the study participants were admitted to ICU. The patients’ average age was 51.25±21 years, and 52.8% of them were male. In the study, some factors such as decreasing oxygen saturation level (OR=0.954, 95%CI: 0.944-0.964), age (OR=1.007, 95%CI: 1.004-1.011), respiratory distress (OR=1.658, 95%CI: 1.410-1.951), reduced level of consciousness (OR=2.487, 95%CI: 1.721-3.596), hypertension (OR=1.249, 95%CI: 1.042-1.496), chronic pulmonary disease (OR=1.250, 95%CI: 1.006-1.554), heart diseases (OR=1.250, 95%CI: 1.009-1.548), chronic kidney disease (OR=1.515, 95%CI: 1.111-2.066), cancer (OR=1.682, 95%CI: 1.130-2.505), seizures (OR=3.428, 95%CI: 1.615-7.274), and gender (OR=1.179, 95%CI: 1.028-1.352) were found to significantly affect ICU admissions. Conclusions: As evidenced by the obtained results, blood oxygen saturation level, the patient's age, and their level of consciousness are crucial for ICU admission

Acceptance of Online Education by Undergraduate Students During the Covid-19 Pandemic: A Case Study from Kerman, Iran

Background: Online education has become more vastly recognized as a powerful educational tool after the Covid-19 pandemic. It provides educational opportunities that were not previously possible because of time or place restrictions.Objectives: This study investigated the factors influencing students' acceptance of online learning systems during the Covid-19 pandemic.Methods: The study sample comprised 435 students from Kerman University of Medical Sciences. We used the external technology acceptance model (TAM) to determine the acceptance of online education systems by undergraduate students during the Covid-19 pandemic. Partial least square structural equation modeling (PLS-SEM) was used to check the model hypotheses. P-values less than 0.05 were considered statistically significant.Results: In this study, 65% of the participants were men. The mean score for the items in the questionnaire was 53.1±19.3. The constructs of perceived ease of use and perceived usefulness had a significant effect on students' attitudes, and students' attitudes and perceived usefulness strongly influenced their behavior in using the online education system.Conclusion: The results of this study show that the perceived ease of use and perceived usefulness of the online education system indirectly affect students' behavior in using online education. Thus, educational policymakers at universities can emphasize the ease of learning and especially the easy use of mobile phones when choosing an online education system. In addition, the creation and expansion of the necessary infrastructure can facilitate student use of online education

Prevalence and 5-year incidence rate of dyslipidemia and its association with other coronary artery disease risk factors in Iran: Results of the Kerman coronary artery disease risk factors study (Phase 2).

BackgroundDyslipidemia (DL) is an important risk factor of coronary artery disease (CAD). We evaluated DL prevalence and its 5-year incidence rate in southeastern Iran, to assess the severity and growth rate of this CAD risk factor in the region.Materials and methodsThis study was a part of the Kerman CAD Risk Factors Study Phase 2 (2014-2018) among 9996 individuals aged 15-80 years, from whom 2820 individuals had also participated in Phase 1 (2009-2011). In mg/dl, cholesterol ≥240 and/or low-density lipoprotein cholesterol ≥160 and/or high-density lipoprotein cholesterol <40 for men and <50 for women and/or triglyceride >200 were defined as DL.ResultsThe lipid profile of 9911 persons was analyzed. Overall 19.6% had borderline cholesterol and 6.4% suffered from hypercholesterolemia. 56.6% of the population (62.5% of females vs. 48.5% of males) suffer from DL, from whom 73.4% were undiagnosed. Female gender, advanced age, obesity, hypertension, diabetes, anxiety, and depression predicted DL in the study population. The prevalence of DL was significantly lower in Phase 2 (56.6%) compared to Phase 1 (81.4%). The prevalence of undiagnosed DL (UDL) and diagnosed DL (DDL) was 40.7% and 16.2%, respectively. The 5-year incidence rate of DL was 2.58 persons/100 person-years (3.24 in females vs. 2.20 in males).ConclusionAlthough there were promising signs of a reduction in DL and increase in DDL in the last 5 years, a high percentage of the population have DL yet, from whom mostly are undiagnosed. DL was significantly associated with other CAD risk factors. Therefore, the health-care management system should improve its strategies to reduce the health burden of DL

Estimating COVID-19-Related Infections, Deaths, and Hospitalizations in Iran Under Different Physical Distancing and Isolation Scenarios

Background: Iran is one of the first few countries that was hit hard with the coronavirus disease 2019 (COVID-19) pandemic. We aimed to estimate the total number of COVID-19 related infections, deaths, and hospitalizations in Iran under different physical distancing and isolation scenarios. Methods: We developed a susceptible-exposed-infected/infectious-recovered/removed (SEIR) model, parameterized to the COVID-19 pandemic in Iran. We used the model to quantify the magnitude of the outbreak in Iran and assess the effectiveness of isolation and physical distancing under five different scenarios (A: 0% isolation, through E: 40% isolation of all infected cases). We used Monte-Carlo simulation to calculate the 95% uncertainty intervals (UIs). Results: Under scenario A, we estimated 5 196 000 (UI 1 753 000-10 220 000) infections to happen till mid-June with 966 000 (UI 467 800-1 702 000) hospitalizations and 111 000 (UI 53 400-200 000) deaths. Successful implantation of scenario E would reduce the number of infections by 90% (ie, 550 000) and change the epidemic peak from 66 000 on June 9, to 9400 on March 1, 2020. Scenario E also reduces the hospitalizations by 92% (ie, 74 500), and deaths by 93% (ie, 7800). Conclusion: With no approved vaccination or therapy available, we found physical distancing and isolation that include public awareness and case-finding and isolation of 40% of infected people could reduce the burden of COVID-19 in Iran by 90% by mid-June.</p

Estimating COVID-19-Related Infections, Deaths, and Hospitalizations in Iran Under Different Physical Distancing and Isolation Scenarios

Background: Iran is one of the first few countries that was hit hard with the coronavirus disease 2019 (COVID-19) pandemic. We aimed to estimate the total number of COVID-19 related infections, deaths, and hospitalizations in Iran under different physical distancing and isolation scenarios. Methods: We developed a susceptible-exposed-infected/infectious-recovered/removed (SEIR) model, parameterized to the COVID-19 pandemic in Iran. We used the model to quantify the magnitude of the outbreak in Iran and assess the effectiveness of isolation and physical distancing under five different scenarios (A: 0% isolation, through E: 40% isolation of all infected cases). We used Monte-Carlo simulation to calculate the 95% uncertainty intervals (UIs). Results: Under scenario A, we estimated 5 196 000 (UI 1 753 000-10 220 000) infections to happen till mid-June with 966 000 (UI 467 800-1 702 000) hospitalizations and 111 000 (UI 53 400-200 000) deaths. Successful implantation of scenario E would reduce the number of infections by 90% (ie, 550 000) and change the epidemic peak from 66 000 on June 9, to 9400 on March 1, 2020. Scenario E also reduces the hospitalizations by 92% (ie, 74 500), and deaths by 93% (ie, 7800). Conclusion: With no approved vaccination or therapy available, we found physical distancing and isolation that include public awareness and case-finding and isolation of 40% of infected people could reduce the burden of COVID-19 in Iran by 90% by mid-June.</p

Estimating COVID-19-Related Infections, Deaths, and Hospitalizations in Iran Under Different Physical Distancing and Isolation Scenarios.

BackgroundIran is one of the first few countries that was hit hard with the coronavirus disease 2019 (COVID-19) pandemic. We aimed to estimate the total number of COVID-19 related infections, deaths, and hospitalizations in Iran under different physical distancing and isolation scenarios.MethodsWe developed a susceptible-exposed-infected/infectious-recovered/removed (SEIR) model, parameterized to the COVID-19 pandemic in Iran. We used the model to quantify the magnitude of the outbreak in Iran and assess the effectiveness of isolation and physical distancing under five different scenarios (A: 0% isolation, through E: 40% isolation of all infected cases). We used Monte-Carlo simulation to calculate the 95% uncertainty intervals (UIs).ResultsUnder scenario A, we estimated 5 196 000 (UI 1 753 000-10 220 000) infections to happen till mid-June with 966 000 (UI 467 800-1 702 000) hospitalizations and 111 000 (UI 53 400-200 000) deaths. Successful implantation of scenario E would reduce the number of infections by 90% (ie, 550 000) and change the epidemic peak from 66 000 on June 9, to 9400 on March 1, 2020. Scenario E also reduces the hospitalizations by 92% (ie, 74 500), and deaths by 93% (ie, 7800).ConclusionWith no approved vaccination or therapy available, we found physical distancing and isolation that include public awareness and case-finding and isolation of 40% of infected people could reduce the burden of COVID-19 in Iran by 90% by mid-June

Pattern of alcohol consumption in Iran in 2016 (National Level).

Pattern of alcohol consumption in Iran in 2016 (National Level).</p