Predicting Areas with High Concentration of the Long‐Term Uninsured and Their Association with Emergency Department Usage by Uninsured Patients in South Carolina

Background: To predict areas with a high concentration of long‐term uninsured (LTU) and Emergency Department (ED) usage by uninsured patients in South Carolina. Methods: Ameri-can Community Survey data was used to predict the concentration of LTU at the ZIP Code Tabulation Area (ZCTA) level. In a multivariate regression model, the LTU concentration was then mod-eled to predict ED visits by uninsured patients. ED data came from the restricted South Carolina Patient Encounter data with patients’ billing zip codes. A simulation was conducted to predict changes in the ED visit numbers and rates by uninsured patients if the LTU concentration was reduced to a lower level. Results: Overall, there was a positive relationship between ED visit rates by the uninsured patients and areas with higher concentrations of LTU. Our simulation model pre-dicted that if the LTU concentration for each ZCTA was reduced to the lowest quintile, the ED visit rates by the uninsured would decrease significantly. The greatest reduction in the number of ED visits by the uninsured over a two‐year period was for the following primary diagnoses: abdominal pain (15,751 visits), cellulitis and abscess (11,260 visits) and diseases for the teeth and supporting structures (10,525 visits). Conclusions: The provision of primary healthcare services to the LTU could help cut back inappropriate uses of ED resources and healthcare costs.