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The COVID-19 pandemic has exacerbated the significance of burn injuries as a critical public health challenge, demanding the exploration of effective strategies for prevention and care, particularly for individuals with burns. This retrospective observational cohort study aimed to analyze the characteristics of adult burn patients admitted to three reference centers in Iran from October 2020 to October 2023. The study focused on patients and burn wound characteristics, complications and morbidities, and treatment plans. Among the 382 patients aged 20 to 84, our findings suggested that being male decreased the likelihood of COVID-19 infection by about 77 %. Thermal burns accounted for 50.2 % of COVID-19-positive patients and 49.8 % of COVID-19-negative patients. Notably, the risk of COVID-19 infection increased with higher degrees of burn (p = 0.006) in both groups. Among the complications, pneumonia (p = 0.003) and paralytic ileus (p = 0.015) were significantly more common in COVID-19-positive patients. Additionally, COVID-19-positive patients underwent more frequent interventions such as skin grafting (p = 0.035) and surgical debridement (p = 0.030). These findings emphasize the importance of a carefully planned and proactive multidisciplinary management strategy to ensure optimal care for burn patients.

ObjectivesUnderstanding the potential impact of physical characteristics of the urban environment on clinical outcomes on several mental illnesses.Materials and MethodsPhysical features of the urban environment were examined as predictors for affective and non-affective several mental illnesses (SMI), the number and length of psychiatric hospital admissions, and the number of short and long-acting injectable antipsychotic prescriptions. In addition, the urban features with the greatest weight in the predicted model were determined. The data included 28 urban features and 6 clinical variables obtained from 30,210 people with SMI receiving care from the South London and Maudsley NHS Foundation Trust (SLaM) using the Clinical Record Interactive Search (CRIS) tool. Five machine learning regression models were evaluated for the highest prediction accuracy followed by the Self-Organising Map (SOM) to represent the results visually.ResultsThe prevalence of SMI, number and duration of psychiatric hospital admission, and antipsychotic prescribing were greater in urban areas. However, machine learning analysis was unable to accurately predict clinical outcomes using urban environmental data.DiscussionThe urban environment is associated with an increased prevalence of SMI. However, urban features alone cannot explain the variation observed in psychotic disorder prevalence or clinical outcomes measured through psychiatric hospitalisation or exposure to antipsychotic treatments.ConclusionUrban areas are associated with a greater prevalence of SMI but clinical outcomes are likely to depend on a combination of urban and individual patient-level factors. Future mental healthcare service planning should focus on providing appropriate resources to people with SMI in urban environments.