Unplanned Closure of Public Schools in Michigan, 2015-2016: Cross-Sectional Study on Rurality and Digital Data Harvesting.

BACKGROUND: For pandemic preparedness, researchers used online systematic searches to track unplanned school closures (USCs). We determine if Twitter provides complementary data. METHODS: Twitter handles of Michigan public schools and school districts were identified. All tweets associated with these handles were downloaded. USC-related tweets were identified using 5 keywords. Descriptive statistics and multivariable logistic regression were performed in R. RESULTS: Among 3469 Michigan public schools, 2003 maintained their own active Twitter accounts or belonged to school districts with active Twitter accounts. Of these 2003 schools, in 2015-2016 school year, at least 1 USC announcement was identified for 349 schools via the current method only, 678 schools via Twitter only, and 562 schools via both methods. No USC announcements were identified for 414 schools. Rural schools were less likely than city schools to have active Twitter coverage (adjusted relative risk [adjRR] = 0.3956, 95% confidence interval [CI] 0.3312-0.4671), and to announce USCs on Twitter (adjRR = 0.5692, 95% CI 0.4645-0.6823), but more likely to have USCs identified by the current method (adjRR = 1.4545, 95% CI 1.3545-1.5490). CONCLUSIONS: Each method identified USCs that were missed by the other. Our results suggested that identifying USCs on Twitter is complementary to the current method.

Contents, Followers, and Retweets of the Centers for Disease Control and Prevention's Office of Advanced Molecular Detection (@CDC_AMD) Twitter Profile: Cross-Sectional Study.

BACKGROUND: The Office of Advanced Molecular Detection (OAMD), Centers for Disease Control and Prevention (CDC), manages a Twitter profile (@CDC_AMD). To our knowledge, no prior study has analyzed a CDC Twitter handle's entire contents and all followers. OBJECTIVE: This study aimed to describe the contents and followers of the Twitter profile @CDC_AMD and to assess if attaching photos or videos to tweets posted by @CDC_AMD would increase retweet frequency. METHODS: Data of @CDC_AMD were retrieved on November 21, 2016. All followers (N=809) were manually categorized. All tweets (N=768) were manually coded for contents and whether photos or videos were attached. Retweet count for each tweet was recorded. Negative binomial regression models were applied to both the original and the retweet corpora. RESULTS: Among the 809 followers, 26.0% (210/809) were individual health professionals, 11.6% (94/809) nongovernmental organizations, 3.3% (27/809) government agencies' accounts, 3.3% (27/809) accounts of media organizations and journalists, and 0.9% (7/809) academic journals, with 54.9% (444/809) categorized as miscellaneous. A total of 46.9% (360/768) of @CDC_AMD's tweets referred to the Office's website and their current research; 17.6% (135/768) referred to their scientists' publications. Moreover, 80% (69/86) of tweets retweeted by @CDC_AMD fell into the miscellaneous category. In addition, 43.4% (333/768) of the tweets contained photos or videos, whereas the remaining 56.6% (435/768) did not. Attaching photos or videos to original @CDC_AMD tweets increases the number of retweets by 37% (probability ratio=1.37, 95% CI 1.13-1.67, P=.002). Content topics did not explain or modify this association. CONCLUSIONS: This study confirms CDC health communicators' experience that original tweets created by @CDC_AMD Twitter profile sharing images or videos (or their links) received more retweets. The current policy of attaching images to tweets should be encouraged.

#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.

Lyme Disease and YouTube TM: A Cross-Sectional Study of Video Contents.

OBJECTIVES: Lyme disease is the most common tick-borne disease. People seek health information on Lyme disease from YouTubeTM videos. In this study, we investigated if the contents of Lyme disease-related YouTubeTM videos varied by their sources. METHODS: Most viewed English YouTubeTM videos (n = 100) were identified and manually coded for contents and sources. RESULTS: Within the sample, 40 videos were consumer-generated, 31 were internet-based news, 16 were professional, and 13 were TV news. Compared with consumer-generated videos, TV news videos were more likely to mention celebrities (odds ratio [OR], 10.57; 95% confidence interval [CI], 2.13-52.58), prevention of Lyme disease through wearing protective clothing (OR, 5.63; 95% CI, 1.23-25.76), and spraying insecticides (OR, 7.71; 95% CI, 1.52-39.05). CONCLUSION: A majority of the most popular Lyme disease-related YouTube TM videos were not created by public health professionals. Responsible reporting and creative video-making facilitate Lyme disease education. Partnership with YouTubeTM celebrities to co-develop educational videos may be a future direction.

Zika-Virus-Related Photo Sharing on Pinterest and Instagram.

OBJECTIVE: Pinterest (San Francisco, CA) and Instagram (Menlo Park, CA) are 2 popular photo-sharing social media platforms among young individuals. We assessed differences between Instagram and Pinterest in relaying photographic information regarding Zika virus. Specifically, we investigated whether the percentage of Zika-virus-related photos with Spanish or Portuguese texts embedded therein was higher for Instagram than for Pinterest and whether the contents of Zika-virus-related photos shared on Pinterest were different from those shared on Instagram. METHODS: We retrieved and manually coded 616 Pinterest (key words: "zika" AND "virus") and 616 Instagram (hashtag: #zikavirus) photos. RESULTS: Among the manually coded samples, 47% (290/616) of Pinterest photos and 23% (144/616) of Instagram photos were relevant to Zika virus. Words were embedded in 57% (164/290) of relevant Pinterest photos and all 144 relevant Instagram photos. Among the photos with embedded words, photos in Spanish or Portuguese were more prevalent on Instagram (77/144, 53%) than on Pinterest (14/164, 9%). There were more Zika-virus-related photos on Instagram than on Pinterest pertinent to Zika virus prevention (59/144, 41%, versus 41/290, 14%; P<0.0001), the effects of Zika virus on pregnancy (27/144, 19%, versus 32/290, 11%; P=0.04), and Zika-virus-associated deaths (4/144, 2%, versus 0/290, 0%; P=0.01). CONCLUSIONS: Pinterest and Instagram are similar platforms for Zika virus prevention communication. (Disaster Med Public Health Preparedness. 2017;11:656-659).