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10.1111/acem.12983 http://scihub22266oqcxt.onion/10.1111/acem.12983 C5375168!5375168 !27062454     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\27062454 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Acad+Emerg+Med 2016 ; 23 (7 ): 831-4 Nephropedia Template TP {{tp|p=27062454 |t=2016. Tweet Now, See You In the ED Later? Examining the Association Between Alcohol-related Tweets and Emergency Care Visits |pdf=|usr=}}{{27062454 }} gab.com Text Tweet Now, See You In the ED Later Examining the Association Between Alcohol-related Tweets and Emergency Care Visits JO: Acad Emerg Med http://www.kidney.de/pget.php?&tw=1&pmid=27062454 #FOAvip 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. Twit Text FOAVip Tweet Now, See You In the ED Later Examining the Association Between Alcohol-related Tweets and Emergency Care Visits ????Acad Emerg Med http://www.kidney.de/pget.php?&tw=1&pmid=27062454 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=27062454 #FOAvip English Wikipedia <ref>{{Cite pmid|27062454 }}</ref> Tweet Now, See You In the ED Later? Examining the Association Between Alcohol-related Tweets and Emergency Care Visits #MMPMID27062454 Ranney ML ; Chang B ; Freeman JR ; Norris B ; Silverberg M ; Choo EK Acad Emerg Med 2016[Jul]; 23 (7 ): 831-4 PMID27062454 show ga 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. |*Alcohol Drinking [MESH] |*Emergency Medical Services [MESH] |*Social Media [MESH] |Adult [MESH] |Emergency Service, Hospital/*statistics & numerical data [MESH] |Forecasting [MESH] |Humans [MESH]Tweet Now, See You In the ED Later? Examining the Association Between (epmc).pdf DeepDyve Pubget Overpricing