Introduction: Burns can be very destructive, and severely endanger the health and lives of humans. It maybe cause disability and even psychological trauma in individuals. . Such an event can also lead to economic burden on victim’s families and society. The aim of our study is to evaluate epidemiology and outcome of burn patients referring to emergency department. Methods: This is a cross-sectional study was conducted by evaluation of patients’ files and forensic reports of burned patients’ referred to the emergency department (ED) of Akdeniz hospital, Turkey, 2008. Demographic data, the season, place, reason, anatomical sites, total body surface area, degrees, proceeding treatment, and admission time were recorded. Multinomial logistic regression was used to compare frequencies’ differences among single categorized variables. Stepwise logistic regression was applied to develop a predictive model for hospitalization. P<0.05 was defined as a significant level. Results: Two hundred thirty patients were enrolled (53.9% female). The mean of patients' ages was 25.3 ± 22.3 years. The most prevalence of burn were in the 0-6 age group and most of which was hot liquid scalding (71.3%). The most affected parts of the body were the left and right upper extremities. With increasing the severity of triage level (OR=2.2; 95% CI: 1.02-4.66; p=0.046), intentional burn (OR=4.7; 95% CI: 1.03-21.8; p=0.047), referring from other hospitals or clinics (OR=3.4; 95% CI: 1.7-6.6; p=0.001), and percentage of burn (OR=18.1; 95% CI: 5.42-62.6; p<0.001) were independent predictive factor for hospitalization. In addition, odds of hospitalization was lower in patients older than 15 years (OR=0.7; 95% CI: 0.5-0.91; p=0.035). Conclusion: This study revealed the most frequent burns are encountered in the age group of 0-6 years, percentage of <10%, second degree, upper extremities, indoor, and scalding from hot liquids. Increasing ESI severity, intentional burn, referring from other hospitals or clinics, and the percentage of burn were independent predictive factors for hospitalization
