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Background Necrotizing fasciitis (NF) is a rare but fatal infectious disease that causes economic burdens on patient and the healthcare system. We investigated the incidence of necrotizing fasciitis (NF) and the seasonal variation of necrotizing fasciitis in Korea. Methods We analyzed a nationwide claims database from the Korean Health Insurance Review and Assessment Service from 2011 to 2017. For case definition, we used two different methods. First, patients who hospitalized with NF diagnosis code and received surgical intervention (NF code method) were defined as NF. Second, patients hospitalized with sepsis codes accompanying surgical intervention codes were defined as NF (sepsis code method). The annual incidence rate per 100,000 population of NF was calculated using the number of identified NF cases as numerator and age- and sex-specific midyear population as the denominator. Poisson regression models were used to assess the relationship of crude incidence rates to year, age, and sex. A multivariate Poisson regression model was used to investigate variations in trends in the monthly NF cases. Results The overall average annual incidence rate of NF during 2012–2017 was 0.86/100,000 by NF code method and 1.47/100,000 by the sepsis code method. The incidence of NF increased with age and 2.5 times higher in males than females across all age groups. Two-thirds of episodes occurred in diabetes patients. The incidence of NF occurred the most during summer. A multivariate Poisson regression model using national meteorological variables suggested that higher mean temperature of and larger numbers of NF cases during a prior month increased NF cases. Conclusion The possibility of NF should be suspected for the cases for an elderly man with diabetes in summer. From a national management perspective, the prior information on the number of NF incidences and the mean temperature can help predict NF outbreak. Disclosures All authors: No reported disclosures.

446. Epidemiology of Necrotizing Fasciitis in Korea: A Nationwide Study Using Claims Data