Understanding Emergency Department Utilization Patterns in Illinois

ObjectiveTo analyze differences in utilization of Emergency Departments for primary care sensitive conditions by facility and by patient ZIP code.IntroductionSyndromic surveillance has been widely implemented for the collection of Emergency Department (ED) data. EDs may be the only option for seeking care in underserved areas, but they do not represent population-based measures. This analysis provides insight on health-seeking behaviors within the context of the type of care sought.MethodsThe NSSP BioSense database in Adminer was queried for Illinois ED visits that occurred in August 2016, November 2016, February 2017, or May 2017. These months were chosen to account for seasonality and holidays. For each visit, as defined by the BioSense ID, the first listed diagnosis code, defined to be the primary diagnosis, and the latest valid patient ZIP code were determined.Next, an algorithm1 developed by New York University (NYU) which uses diagnosis codes to classify ED visits was applied to each visit's primary diagnosis. With this algorithm, a percentage (possibly zero) of each visit was classified as primary care sensitive (PCS), where the percentage is based on the diagnosis code.The visits were tabulated to find the percentage of visits to each facility or from each ZIP code which were classified as PCS. (Visits whose diagnosis was not matched by the algorithm were excluded.) The relationships between the percentages of PCS visits in each facility or ZIP code and characteristics of the facilities or ZIP codes were then analyzed.Facilities were grouped by Critical Access Hospital (CAH) status2 and by location (within, or not within, a primary care Health Professional Shortage Area (HPSA), as determined using a tool from the U.S. Department of Health and Human Services3). Percentages of PCS visits at different types of facilities were compared using t-tests.Variables reported in the Social Vulnerability Index (SVI)4 at the census tract level were converted to ZIP code-level data using a crosswalk from the U.S. Department of Housing and Urban Development5. An ordinary least squares regression model in which these variables were used to predict the percentage of PCS visits in each ZIP code was fitted. The R package geoR6 was used to fit an additional model which accounted for spatial correlation across ZIP codes. In this model, ZCTA latitude and longitude coordinates from the U.S. Census7 were used as the ZIP codes' locations. Only ZIP codes for which the NYU al