A retrospective cohort study, using the electronic medical records of Kaiser Permanente Northern California (2011)(2012)(2013)(2014)(2015), included 560 robotic and 6785 conventional laparoscopic cases with 1836 "complex" patients (25%). The average operative time was 152 minutes (robotic) vs 157 minutes (conventional) laparoscopic hysterectomy. Complex surgical cases averaged 190 minutes and noncomplex cases averaged 144 minutes. For women with complex disease, the robotic approach, when used by a higher-volume surgeon, may be associated with shorter operative time and slightly less blood loss, but not with lower risk of complications.

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.

This 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.

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.

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