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

Background: 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. Objectives: The objective was to examine the correlation between real-time, alcohol-related tweets and alcohol-related ED visits. Methods: 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. Results: 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 (r_s = 0.31, p < 0.00001), but not between hourly alcohol-related tweet volume and number of non–alcohol-related ED visits (r_s = –0.07, p = 0.11). Conclusion: 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.