#CDCGrandRounds and #VitalSigns : A Twitter Analysis

BACKGROUND: The CDC hosts monthly panel presentations titled 'Public Health Grand Rounds' and publishes monthly reports known as Vital Signs. Hashtags #CDCGrandRounds and #VitalSigns were used to promote them on Twitter. Objectives: This study quantified the effect of hashtag count, mention count, and URL count and attaching visual cues to #CDCGrandRounds or #VitalSigns tweets on their retweet frequency. METHODS: Through Twitter Search Application Programming Interface, original tweets containing the hashtag #CDCGrandRounds (n = 6,966; April 21, 2011-October 25, 2016) and the hashtag #VitalSigns (n = 15,015; March 19, 2013-October 31, 2016) were retrieved respectively. Negative binomial regression models were applied to each corpus to estimate the associations between retweet frequency and three predictors (hashtag count, mention count, and URL link count). Each corpus was sub-set into cycles (#CDCGrandRounds: n = 58, #VitalSigns: n = 42). We manually coded the 30 tweets with the highest number of retweets for each cycle, whether it contained visual cues (images or videos). Univariable negative binomial regression models were applied to compute the prevalence ratio (PR) of retweet frequency for each cycle, between tweets with and without visual cues. FINDINGS: URL links increased retweet frequency in both corpora; effects of hashtag count and mention count differed between the two corpora. Of the 58 #CDCGrandRounds cycles, 29 were found to have statistically significantly different retweet frequencies between tweets with and without visual cues. Of these 29 cycles, one had a PR estimate < 1; twenty-four, PR > 1 but < 3; and four, PR > 3. Of the 42 #VitalSigns cycles, 19 were statistically significant. Of these 19 cycles, six were PR > 1 and < 3; and thirteen, PR > 3. Conclusions: The increase of retweet frequency through attaching visual cues varied across cycles for original tweets with #CDCGrandRounds and #VitalSigns. Future research is needed to determine the optimal choice of visual cues to maximize the influence of public health tweets

#Globalhealth Twitter Conversations on #Malaria, #HIV, #TB, #NCDS, and #NTDS: a Cross-Sectional Analysis

BackgroundAdvocates use the hashtag #GlobalHealth on Twitter to draw users' attention to prominent themes on global health, to harness their support, and to advocate for change.ObjectivesWe aimed to describe #GlobalHealth tweets pertinent to given major health issues.MethodsTweets containing the hashtag #GlobalHealth (N = 157,951) from January 1, 2014, to April 30, 2015, were purchased from GNIP Inc. We extracted 5 subcorpora of tweets, each with 1 of 5 co-occurring disease-specific hashtags (#Malaria, #HIV, #TB, #NCDS, and #NTDS) for further analysis. Unsupervised machine learning was applied to each subcorpus to categorize the tweets by their underlying topics and obtain the representative tweets of each topic. The topics were grouped into 1 of 4 themes (advocacy; epidemiological information; prevention, control, and treatment; societal impact) or miscellaneous. Manual categorization of most frequent users was performed. Time zones of users were analyzed.FindingsIn the entire #GlobalHealth corpus (N = 157,951), there were 40,266 unique users, 85,168 retweets, and 13,107 unique co-occurring hashtags. Of the 13,087 tweets across the 5 subcorpora with co-occurring hashtag #malaria (n = 3640), #HIV (n = 3557), #NCDS (noncommunicable diseases; n = 2373), #TB (tuberculosis; n = 1781), and #NTDS (neglected tropical diseases; n = 1736), the most prevalent theme was prevention, control, and treatment (4339, 33.16%), followed by advocacy (3706, 28.32%), epidemiological information (1803, 13.78%), and societal impact (1617, 12.36%). Among the top 10 users who tweeted the highest number of tweets in the #GlobalHealth corpus, 5 were individual professionals, 3 were news media, and 2 were organizations advocating for global health. The most common users' time zone was Eastern Time (United States and Canada).ConclusionsThis study highlighted the specific #GlobalHealth Twitter conversations pertinent to malaria, HIV, tuberculosis, noncommunicable diseases, and neglected tropical diseases. These conversations reflect the priorities of advocates, funders, policymakers, and practitioners of global health on these high-burden diseases as they presented their views and information on Twitter to their followers

#CDCGrandRounds and #VitalSigns: A Twitter Analysis

Background: The CDC hosts monthly panel presentations titled ‘Public Health Grand Rounds’ and publishes monthly reports known as Vital Signs. Hashtags #CDCGrandRounds and #VitalSigns were used to promote them on Twitter. Objectives: This study quantified the effect of hashtag count, mention count, and URL count and attaching visual cues to #CDCGrandRounds or #VitalSigns tweets on their retweet frequency. Methods: Through Twitter Search Application Programming Interface, original tweets containing the hashtag #CDCGrandRounds (n = 6,966; April 21, 2011–October 25, 2016) and the hashtag #VitalSigns (n = 15,015; March 19, 2013–October 31, 2016) were retrieved respectively. Negative binomial regression models were applied to each corpus to estimate the associations between retweet frequency and three predictors (hashtag count, mention count, and URL link count). Each corpus was sub-set into cycles (#CDCGrandRounds: n = 58, #VitalSigns: n = 42). We manually coded the 30 tweets with the highest number of retweets for each cycle, whether it contained visual cues (images or videos). Univariable negative binomial regression models were applied to compute the prevalence ratio (PR) of retweet frequency for each cycle, between tweets with and without visual cues. Findings: URL links increased retweet frequency in both corpora; effects of hashtag count and mention count differed between the two corpora. Of the 58 #CDCGrandRounds cycles, 29 were found to have statistically significantly different retweet frequencies between tweets with and without visual cues. Of these 29 cycles, one had a PR estimate 1 but 3. Of the 42 #VitalSigns cycles, 19 were statistically significant. Of these 19 cycles, six were PR > 1 and 3. Conclusions: The increase of retweet frequency through attaching visual cues varied across cycles for original tweets with #CDCGrandRounds and #VitalSigns. Future research is needed to determine the optimal choice of visual cues to maximize the influence of public health tweets

Prevalence and Correlates of Anxiety in Rural Populations of Southeast Georgia

This conference abstract was published in Proceedings of the National Rural Health Association Annual Conference

Prevalence and Correlates of Anxiety in Rural Populations of Southeast Georgia

This conference abstract was published in Proceedings of the National Rural Health Association Annual Conference

Prevalence and Correlates of Anxiety in Rural Populations of Southeast Georgia

This presentation was given at the National Rural Health Association Annual Conference

#CDCGrandRounds and #VitalSigns: A Cross-Sectional Analysis of Twitter Data

Theoretical Background and Research Questions/Hypothesis: The CDC hosts monthly panel presentations with webcast titled ‘Grand Rounds’ since September 2009. CDC also publishes a monthly report known as Vital Signs. The CDC uses two respective hashtags #CDCGrandRounds and #VitalSigns respectively to promote their monthly event and report on Twitter. Our research question is to quantify the effect of attaching images or videos to #CDCGrandRounds or #VitalSigns tweets on their retweet frequency. Methods: Data was retrieved via Twitter Search Application Programming Interface. All original tweets containing the hashtag #CDCGrandRounds dated from April 21, 2011 to October 25, 2016 were retrieved (n=6,966). All original tweets containing the hashtag #VitalSigns dated from March 19, 2013 to October 31, 2016 were retrieved (n=15,015). Each corpus was then sub-set into cycles (#CDCGrandRounds: n=58, #VitalSigns: n= 42). A #CDCGrandRounds cycle is defined as all tweets referring to the pre-specified topic for that particular cycle. A #VitalSigns cycle is defined as the first day of the publication release, which is the first Tuesday of each month, until the day before the next publication is released. Any irrelevant tweets were excluded. We manually coded the 30 tweets with the highest number of retweets for each cycle, as whether it contained a form of media (a still image or a video). Univariable negative binomial regression models were applied to compute the probability ratio of each cycle, with the outcome variable being the retweet frequency and the predictor variable being whether a tweet contains media. Results: Of the 58 #CDCGrandRounds cycles, 29 were found to have statistically significant difference between tweets with and without media. Of these 29 cycles, one had a probability ratio (PR) estimate1 but3. Two cycles were outliers: “Preventing Suicide: A Comprehensive Public Health Approach” (September 2015) with PR = 36.353 (95% CI, 4.869 – 343.845, P1 and3. There were three outliers: “Prescription Painkiller Overdoses” (July 2, 2013) with PR = 33.514 (95% CI, 8.715, 133.357, P Conclusions: The effect of attaching images or photos increasing retweet frequency varied across cycles for original tweets with #CDCGrandRounds and #VitalSigns. Images or photos may or may not increase retweet frequency. Implications for Research and/or Practice: Future research is needed to determine the optimal choice of images or photos attached to a tweet to maximize the influence of public health messages

#GrandRounds and #VitalSigns: A Twitter Analysis

Presented at the 2017 Georgia Southern Research Symposiu

Using Twitter to Track Unplanned School Closures: Georgia Public Schools

Objectives: To aid emergency response, Centers for Disease Control and Prevention (CDC) researchers monitor unplanned school closures (USCs) by conducting online systematic searches (OSS) to identify relevant publicly available reports. We examined the added utility of analyzing Twitter data to improve USC monitoring. Methods: Georgia public school data were obtained from the National Center for Education Statistics. We identified school and district Twitter accounts with 1 or more tweets ever posted (“active”), and their USC-related tweets in the 2015-16 and 2016-17 school years. CDC researchers provided OSS-identified USC reports. Descriptive statistics, univariate, and multivariable logistic regression were computed. Results: A majority (1,864/2,299) of Georgia public schools had, or were in a district with, active Twitter accounts in 2017. Among these schools, 638 were identified with USCs in 2015-16 (Twitter only, 222; OSS only, 2015; both, 201) and 981 in 2016-17 (Twitter only, 178; OSS only, 107; both, 696). The marginal benefit of adding Twitter as a data source was an increase in the number of schools identified with USCs by 53% (222/416) in 2015-16 and 22% (178/803) in 2016-17. Conclusions: Policy-makers may wish to consider the potential value of incorporating Twitter into existing USC monitoring systems