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Background: Identifying factors that influence the length of hospital stay for suspected stroke patients is crucial for optimizing the utilization of hospital resources. This study aimed to determine the factors associated with the length of hospital stay for suspected stroke patients transferred to Qaem Hospital in Mashhad through emergency services using survival analysis. Methods: In this historical cohort study, general information was gathered for all suspected stroke patients who sought emergency services in Mashhad, the largest city in northeast Iran, from March 21, 2018, to March 20, 2019, and were then transferred to the Emergency Department of Qaem Hospital. Pre-hospital emergency data were integrated with hospital records using the mission ID. The primary outcome assessed in the study was the length of hospital stay, with model implementation carried out using the statistical software Stata. Results: The median hospitalization time until patients' recovery was seven days. Out of the 578 participants, 386 cases (66.8%) recovered, while the remaining 190 cases (33.2%) were censored (83 individuals had died during the study, and 107 individuals had exited the hospital for other reasons). The average age of patients at the time of hospitalization was 71.13±13.01 years. Statistical analysis employing Log-rank and Breslow tests identified a significant difference in hospitalization duration among patients receiving various levels of care and based on their insurance status. During multivariate analysis, the Cox regression model was considered unsuitable due to some variables not meeting the proportional hazards assumption, leading to the utilization of AFT models. Following the evaluation of AFT models, including Log-normal, Log-logistic, Exponential, and Weibull, the log-normal model emerged as the most suitable choice, exhibiting AIC and BIC values of 1273.909 and 1356.740, respectively. Significant variables influencing length of stay included patient admission priority, insurance status, season, and residency status. Conclusion: The study suggests that parametric survival models are effective for analyzing lifetime data. Additionally, in light of the significant variables identified, enhancing facility readiness and resource allocation could facilitate more efficient planning and implementation.

Background and Objectives: The Covid-19 epidemic began in Wuhan, China in the late 2019 and became a global epidemic in March 2020. In this regard, one of the most important indicators of the healthcare systems is the in-hospital mortality rate, which occurs with a time lag of one to two weeks after hospitalization. The aim of this study was to investigate the relative risk of Covid-19 mortality considering this time lag according to the number of daily hospitalizations. Methods: The data included the number of daily hospitalizations and deaths from Covid-19 from 15 May 2020 to 10 February 2021 in Iran, which was obtained from the Github database. A log-linear distributed lag model was used to evaluate the relationship and lag effect between daily hospitalization and relative risk of death. Results: The mean number of daily hospitalizations and deaths were 1342.2 ± 7 731.5 and 190.6 11±118.6 in the study period, respectively. It was found that an increase in the number of daily hospitalizations had a significant relationship with an increase in the relative risk of death on the same day and in the following days. As the number of hospitalizations exceeded 2000 patients per day, the cumulative relative risk of death increased to more than one. Conclusion: The results showed that the number of hospitalizations exceeding 2000 people per day was an alert for the countrychr('39')s healthcare system. Overall, prevention and observance of health protocols in the first level followed by early diagnosis of the disease, improving the hospitals facilities and preparedness of healthcare staff can reduce the relative risk of death in the possible future peaks.

Background and Objectives: Burns are tragic events with considerable mortality rates. This study was conducted to analyze the spatial pattern of burns and identify the factors associated with the burn injuries in Mashhad, Iran. Methods: This retrospective cross-sectional study was conducted in patients hospitalized in Imam Reza Hospital Burn Center, Mashhad, which is the only referral burn center in the northeast of Iran. Spatial generalized linear models (Poisson and negative binomial) were used to model the association between socio-economic characteristics and the incidence of burn injury. Results: A total of 1044 acute burn patients (69.9% male, 30.1% female) were reviewed. Most of the burned patients were in the age group 20-29 years (41.4%). The most common cause of burn injury was flame (64.6%). The local test of spatial autocorrelation confirmed that the burns had cluster pattern in the central areas of the city and the worn-out urban texture was at higher risk of burns. The results of model showed that the percentage of worn-out urban texture (IRR =1.007; 95%CI 1.005-1.008), literacy (IRR =0.98; 95% CI 0.95-0.99), percentage of employed women (IRR =0.96; 95%CI 0.92-0.99), unemployment rate (IRR =0.92; 95%CI 0.85-0.99) and percentage of margination (IRR =1.006; 95%CI 1.005-1.008) were associated with burns. Conclusion: Based on the findings, socio-economic and environmental characteristics are associated with the incidence of burn injuries in different areas. The results of this study could be used to design burn prevention strategies, especially in the high-risk areas and groups.