Tweet Now, See You In the ED Later? Examining the Association Between Alcohol-related Tweets and Emergency Care Visits.

Abstract

Alcohol use is a major and unpredictable driver of emergency department (ED) visits. Regional Twitter activity correlates ecologically with behavioral outcomes. No such correlation has been established in real time. The objective was to examine the correlation between real-time, alcohol-related tweets and alcohol-related ED visits. We developed and piloted a set of 11 keywords that identified tweets related to alcohol use. In-state tweets were identified using self-declared profile information or geographic coordinates. Using Datasift, a third-party vendor, a random sample of 1% of eligible tweets containing the keywords and originating in state were downloaded (including tweet date/time) over 3 discrete weeks in 3 different months. In the same time frame, we examined visits to an urban, high-volume, Level I trauma center that receives > 25% of the emergency care volume in the state. Alcohol-related ED visits were defined as visits with a chief complaint of alcohol use, positive blood alcohol, or alcohol-related ICD-9 code. Spearman's correlation coefficient was used to examine the hourly correlation between alcohol-related tweets, alcohol-related ED visits, and all ED visits. A total of 7,820 tweets (representing 782,000 in-state alcohol-related tweets during the 3 weeks) were identified. Concurrently, 404 ED visits met criteria for being alcohol-related versus 2939 non-alcohol-related ED visits. There was a statistically significant relationship between hourly alcohol-related tweet volume and number of alcohol-related ED visits (rs = 0.31, p < 0.00001), but not between hourly alcohol-related tweet volume and number of non-alcohol-related ED visits (rs = -0.07, p = 0.11). In a single state, a statistically significant relationship was observed between the hourly number of alcohol-related tweets and the hourly number of alcohol-related ED visits. Real-time Twitter monitoring may help predict alcohol-related surges in ED visits. Future studies should include larger numbers of EDs and natural language processing.