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

Megan L. Ranney, Brian Chang, Joshua R. Freeman, Brian Norris, Mark Silverberg, Esther Choo, Mark B. Mycyk

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

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 (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). 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.

Original languageEnglish (US)
Pages (from-to)831-834
Number of pages4
JournalAcademic Emergency Medicine
Volume23
Issue number7
DOIs
StatePublished - Jul 1 2016
Externally publishedYes

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Emergency Medical Services
Hospital Emergency Service
Alcohols
International Classification of Diseases
Natural Language Processing
Trauma Centers

ASJC Scopus subject areas

  • Emergency Medicine

Cite this

Tweet Now, See You In the ED Later? Examining the Association Between Alcohol-related Tweets and Emergency Care Visits. / Ranney, Megan L.; Chang, Brian; Freeman, Joshua R.; Norris, Brian; Silverberg, Mark; Choo, Esther; Mycyk, Mark B.

In: Academic Emergency Medicine, Vol. 23, No. 7, 01.07.2016, p. 831-834.

Research output: Contribution to journalArticle

Ranney, Megan L. ; Chang, Brian ; Freeman, Joshua R. ; Norris, Brian ; Silverberg, Mark ; Choo, Esther ; Mycyk, Mark B. / Tweet Now, See You In the ED Later? Examining the Association Between Alcohol-related Tweets and Emergency Care Visits. In: Academic Emergency Medicine. 2016 ; Vol. 23, No. 7. pp. 831-834.
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abstract = "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 (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). 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.",
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AU - Chang, Brian

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AU - Norris, Brian

AU - Silverberg, Mark

AU - Choo, Esther

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AB - 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 (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). 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.

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