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Background: Even though a few years have passed since the coronavirus disease 2019 (COVID-19) outbreak, information regarding certain aspects of the disease, such as post-infection immunity, is still quite limited. This study aimed to evaluate post-infection protection and COVID-19 features among healthcare workers (HCWs), during three successive surges, as well as the rate of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) reinfection, reactivation, re-positivity, and severity., Methods: This cross-sectional population-level observational study was conducted from 20 April 2020 to 18 February 2021. The study population included all HCWs in public or private hospitals in Fars Province, Southern Iran. The infection rate was computed as the number of individuals with positive polymerase chain reaction (PCR) tests divided by the total number of person-days at risk. The re-infection was evaluated after 90 days., Results: A total of 30,546 PCR tests were performed among HCWs, of which 13,749 (61.94% of total HCWs) were positive. Considering the applied 90-day threshold, there were 44 (31.2%) cases of reactivation and relapse, and 97 (68.8% of infected and 1.81% of total HCWs) cases of reinfection among 141 (2.64%) diagnosed cases who experienced a second episode of COVID-19. There was no significant difference in symptoms (P=0.65) or the necessity for ICU admission (P=0.25). The estimated protection against repeated infection after a previous SARS-CoV-2 infection was 94.8% (95% CI=93.6-95.7)., Conclusion: SARS-CoV-2 re-positivity, relapse, and reinfection were rare in the HCW population. After the first episode of infection, an estimated 94.8% protection against recurring infections was achieved. A preprint version of this manuscript is available at DOI:10.21203/rs.3.rs-772662/v1 (https://www.researchsquare.com/article/rs-772662/v1)., Competing Interests: Naeimehossadat Asmarian, as the Editorial Board Member, was not involved in any stage of handling this manuscript. A team of independent experts was formed by the Editorial Board to review the article without her knowledge., (Copyright: © Iranian Journal of Medical Sciences.)

Background: Complementary and Integrative Medicine (CIM) is often taken up by individuals seeking relief from different diseases. This study investigates the prevalence and associated factors of CIM use in patients with COVID-19., Methods: In this telephone-based, cross-sectional study, data on CIM usage were collected from COVID-19 patients from February till June 2020 in Fars province, Iran using a researcher-made checklist. Additionally, we asked about the patients' attitudes toward these treatments., Results: Out of 453 patients diagnosed with COVID-19, 400 (88.30%) responded to our calls and agreed to participate in the study. Among them, 276 patients reported using CIM to treat COVID-19 [prevalence: 69% (95% CI: 64.2 to 73.5)]. The most frequently used herbal medicine among COVID-19 patients was ginger (n = 273, 98.9%), thyme (n = 263, 95.3%), and black cumin (n = 205, 74.3%). Most of these patients were recommended to use herbal medicine by their families and friends (n = 96, 34.8%). Univariable logistic regression revealed that age under 50 years old, residency in urban areas (including the capital of the province and small cities), employment, academic education, and being an outpatient were statistically significant factors resulting in CIM usage. Multivariable logistic regression revealed that CIM use among outpatients was 3.65 times more than among inpatients. In addition, patients under 50 years old used CIM 85% more than older patients. Ultimately, only 9 (3.3%) patients consulted with their doctors regarding these medications. No side effects due to CIM use were reported., Conclusion: Many patients with COVID-19 used CIM, but few consulted with their physicians in this regard. Therefore, physicians should ask their patients about CIM usage, and patients should also report their use of CIM therapies during their medical visits. Furthermore, age and hospitalization status affected CIM use among patients with COVID-19., (© 2022. The Author(s).)

Background: Prioritizing the patients requiring intensive care may decrease the fatality of coronavirus disease-2019 (COVID-19). Aims and objectives: To develop, validate, and compare two models based on machine-learning methods for predicting patients with COVID-19 requiring intensive care. Materials and methods: In 2021, 506 suspected COVID-19 patients, with clinical presentations along with radiographic findings, were laboratory confirmed and included in the study. The primary end-point was patients with COVID-19 requiring intensive care, defined as actual admission to the intensive care unit (ICU). The data were randomly partitioned into training and testing sets (70% and 30%, respectively) without overlapping. A decision-tree algorithm and multivariate logistic regression were performed to develop the models for predicting the cases based on their first 24 hours data. The predictive performance of the models was compared based on the area under the receiver operating characteristic curve (AUC), sensitivity, and accuracy of the models. Results: A 10-fold cross-validation decision-tree model predicted cases requiring intensive care with the AUC, accuracy, and sensitivity of 97%, 98%, and 94.74%, respectively. The same values in the machine-learning logistic regression model were 75%, 85.62%, and 55.26%, respectively. Creatinine, smoking, neutrophil/lymphocyte ratio, temperature, respiratory rate, partial thromboplastin time, white blood cell, Glasgow Coma Scale (GCS), dizziness, international normalized ratio, O2 saturation, C-reactive protein, diastolic blood pressure (DBP), and dry cough were the most important predictors. Conclusion: In an Iranian population, our decision-based machine-learning method offered an advantage over logistic regression for predicting patients requiring intensive care. This method can support clinicians in decision-making, using patients' early data, particularly in low- and middle-income countries where their resources are as limited as Iran. [ABSTRACT FROM AUTHOR]