RESUMO
Background/Purpose: The COVID-19 pandemic affects social and psychological resources. Healthcare workers, especially dental personnel, are more at risk for mental issues due to anxiety, pressure, and frustration. This study assessed mental health outcomes during the COVID-19 epidemic among Iranian dental care providers, focusing on insomnia, anxiety, depression, and posttraumatic stress disorder (PTSD). Methods: In this multicenter cross-sectional survey, the Insomnia Severity Index, the Hospital Anxiety and Depression Scale, and the Global Psychotrauma Screening were masured. Six hundred thirty-eight dental care providers (dental specialists, general dentists, dental hygienists, dental assistants, and dental students) from different parts of Iran (Tehran, Shiraz, Tabriz, and Mashhad) were investigated by the stratified sampling method. The univariate analysis was incorporated as independent in binary logistic regression models to analyze the data. In this study, the significance level was set at 0.05. Results: Among all the participants, 42.8% were dental students or residents, 21.9% were general or specialist dentists, 18.7% were dental assistants, and 16.6% were nonclinicians. The prevalence of insomnia, anxiety, and depression was 31.3%, 40.8%, and 54.9%, respectively. The frequency of participants in the low, moderate, and high levels of PTSD resulting from LCA 56.6%, 33.7%, and 9.7%, respectively. Conclusions: This study found a significant frequency of mental health issues among Iranian dentists. Females, participants whose relatives have COVID-19, and those with a higher workload were more likely to develop mental health symptoms. As mental problems among dental professionals might affect the quality of patient care, diagnostic, supportive, and therapeutic interventions should be taken.
RESUMO
It was in early December 2019 that the terrible news of the outbreak of new coronavirus disease (Covid-19) was reported by the world media, which appeared in Wuhan, China, and is rapidly spreading to other parts of China and several overseas countries. In the field of infectious diseases, modeling, evaluating, and predicting the rate of disease transmission are very important for epidemic prevention and control. Several preliminary mathematical models for Covid-19 are formulated by various international study groups. In this article, the SEIHR(D) compartmental model is proposed to study this epidemic and the factors affecting it, including vaccination. The proposed model can be used to compute the trajectory of the spread of the disease in different countries. Most importantly, it can be used to predict the impact of different inhibition strategies on the development of Covid-19. A computational approach is applied to solve the offered model utilizing the Galerkin method based on the moving least squares approximation constructed on a set of scattered points as a locally weighted least square polynomial fitting. As the method does not need any background meshes, its algorithm can be easily implemented on computers. Finally, illustrative examples clearly show the reliability and efficiency of the new technique and the obtained results are in good agreement with the known facts about the Covid-19 pandemic.