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The Islamic Republic of Iran reported its first COVID-19 cases by 19th February 2020, since then it has become one of the most affected countries, with more than 73,000 cases and 4,585 deaths to this date. Spatial modeling could be used to approach an understanding of structural and sociodemographic factors that have impacted COVID-19 spread at a province-level in Iran. Therefore, in the present paper, we developed a spatial statistical approach to describe how COVID-19 cases are spatially distributed and to identify significant spatial clusters of cases and how socioeconomic and climatic features of Iranian provinces might predict the number of cases. The analyses are applied to cumulative cases of the disease from February 19th to March 18th. They correspond to obtaining maps associated with quartiles for rates of COVID-19 cases smoothed through a Bayesian technique and relative risks, the calculation of global (Moran's I) and local indicators of spatial autocorrelation (LISA), both univariate and bivariate, to derive significant clustering, and the fit of a multivariate spatial lag model considering a set of variables potentially affecting the presence of the disease. We identified a cluster of provinces with significantly higher rates of COVID-19 cases around Tehran (p-value< 0.05), indicating that the COVID-19 spread within Iran was spatially correlated. Urbanized, highly connected provinces with older population structures and higher average temperatures were the most susceptible to present a higher number of COVID-19 cases (p-value < 0.05). Interestingly, literacy is a factor that is associated with a decrease in the number of cases (p-value < 0.05), which might be directly related to health literacy and compliance with public health measures. These features indicate that social distancing, protecting older adults, and vulnerable populations, as well as promoting health literacy, might be useful to reduce SARS-CoV-2 spread in Iran. One limitation of our analysis is that the most updated information we found concerning socioeconomic and climatic features is not for 2020, or even for a same year, so that the obtained associations should be interpreted with caution. Our approach could be applied to model COVID-19 outbreaks in other countries with similar characteristics or in case of an upturn in COVID-19 within Iran. Author summary: Iran was among the first countries reporting a rapid increase in the number of COVID-19 cases. Spatial epidemiology is useful to study the spatial distribution of a disease and to identify factors associated with the number of cases of such disease. By applying these methods, we aimed to identify whether there are clusters of regions in Iran with high or low number of COVID-19 cases and the association of different factors with these numbers, considering spatial relationships and maps representing these associations. Interestingly, we found regions of high number of cases and that more COVID-19 cases were present in provinces with more urbanization, aging population, number of physicians, efficient communications, and greater average temperatures, whereas less COVID-19 cases were present in provinces with more literacy. This study allowed us to understand the spatial behavior of the disease and the importance of having adequate health policies, literacy campaigns, and disseminating health information to the population. [ABSTRACT FROM AUTHOR]

Introduction: The COVID-19 pandemic posed significant risks to children worldwide. This study aimed to assess the COVID-19 protection status of children and explored the relationship between household socio-economic status and COVID-19 morbidity and preventive measures, including vaccination and mask-wearing, in two cities in Iran. Method: A population-based cross-sectional study was conducted from July to October 2022 among 7 to 18-year-old children and their families in Tehran and Karaj. A total of 3,022 samples were selected using stratified multistage cluster sampling. Data were collected through interviews with children and adults, using questionnaires and was analyzed with Stata software version 14. Results: The analysis focused on 2,878 children with a median age of 12. Over half (54%) reported that the pandemic negatively affected their family's financial status, with 45% describing its impact on children's needs as negative or very negative. Just under 50% of respondents consistently wore masks during the study period, and around 54% had received at least one dose of the COVID-19 vaccine. Reasons for not getting vaccinated included concerns about side effects, ineligibility for the target age group, and overcrowding at vaccination sites. The odds of not getting vaccinated were significantly lower for children aged 15–18, with boys more likely to refuse vaccination than girls. Conclusion: The financial impact of the pandemic in Iran affected families' ability to meet their children's needs. Moreover, low vaccination acceptance rates increased children's vulnerability to health problems and contributed to COVID-19 infections. Efforts should be made to increase vaccination acceptance, particularly among immigrant populations. [ABSTRACT FROM AUTHOR]

During the last months of the coronavirus pandemic, with all those public restrictions and health interventions, the transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) appears now to have been raised in some countries around the world. Iran was one of those first countries facing the second wave of coronavirus, due to the lack of appropriate public restrictions because of economic problems the country is facing. The clinical and demographic characteristics of severe cases and non-severe cases of Coronavirus Disease (COVID-19) in 192 patients in Tehran, Iran, between June 16 and July 11, 2020, were investigated. The patients were divided into severe cases (n = 82) and non-severe cases (n = 110). Demographic and clinical characteristics were compared between the two study clusters. The mean age was 54.6 ± 17.2 years, and the most common presenting symptom was persistent cough (81.8%) and fever (79.7%). The logistic regression model revealed that age, BMI, and affected family members were statistically associated with severity. Patients with complicated conditions of disorders faced more hospitalization days and medical care than the average statistical data. As the coronavirus spike in the case and death reports from June 2020, we observed the rise in the incidence of severe cases, where 42.7% (82/192) of cases have resulted in severe conditions. Our findings also suggested that the effect of IFB (Betamethasone) was more valid than the other alternative drugs such as LPV/r and IVIg. [ABSTRACT FROM AUTHOR]