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Emergency Physician Twitter Use in the COVID-19 Pandemic as a Potential Predictor of Impending Surge: Retrospective Observational Study
Journal of Medical Internet Research ( IF 5.8 ) Pub Date : 2021-07-14 , DOI: 10.2196/28615
Colton Margus 1, 2 , Natasha Brown 1, 2 , Attila J Hertelendy 1, 3 , Michelle R Safferman 4, 5 , Alexander Hart 1, 2 , Gregory R Ciottone 1, 2
Affiliation  

Background: The early conversations on social media by emergency physicians offer a window into the ongoing response to the COVID-19 pandemic. Objective: This retrospective observational study of emergency physician Twitter use details how the health care crisis has influenced emergency physician discourse online and how this discourse may have use as a harbinger of ensuing surge. Methods: Followers of the three main emergency physician professional organizations were identified using Twitter’s application programming interface. They and their followers were included in the study if they identified explicitly as US-based emergency physicians. Statuses, or tweets, were obtained between January 4, 2020, when the new disease was first reported, and December 14, 2020, when vaccination first began. Original tweets underwent sentiment analysis using the previously validated Valence Aware Dictionary and Sentiment Reasoner (VADER) tool as well as topic modeling using latent Dirichlet allocation unsupervised machine learning. Sentiment and topic trends were then correlated with daily change in new COVID-19 cases and inpatient bed utilization. Results: A total of 3463 emergency physicians produced 334,747 unique English-language tweets during the study period. Out of 3463 participants, 910 (26.3%) stated that they were in training, and 466 of 902 (51.7%) participants who provided their gender identified as men. Overall tweet volume went from a pre-March 2020 mean of 481.9 (SD 72.7) daily tweets to a mean of 1065.5 (SD 257.3) daily tweets thereafter. Parameter and topic number tuning led to 20 tweet topics, with a topic coherence of 0.49. Except for a week in June and 4 days in November, discourse was dominated by the health care system (45,570/334,747, 13.6%). Discussion of pandemic response, epidemiology, and clinical care were jointly found to moderately correlate with COVID-19 hospital bed utilization (Pearson r=0.41), as was the occurrence of “covid,” “coronavirus,” or “pandemic” in tweet texts (r=0.47). Momentum in COVID-19 tweets, as demonstrated by a sustained crossing of 7- and 28-day moving averages, was found to have occurred on an average of 45.0 (SD 12.7) days before peak COVID-19 hospital bed utilization across the country and in the four most contributory states. Conclusions: COVID-19 Twitter discussion among emergency physicians correlates with and may precede the rising of hospital burden. This study, therefore, begins to depict the extent to which the ongoing pandemic has affected the field of emergency medicine discourse online and suggests a potential avenue for understanding predictors of surge.

This is the abstract only. Read the full article on the JMIR site. JMIR is the leading open access journal for eHealth and healthcare in the Internet age.


中文翻译:

急诊医师 Twitter 在 COVID-19 大流行中用作即将发生的浪涌的潜在预测因素:回顾性观察研究

背景:急诊医师在社交媒体上的早期对话为了解对 COVID-19 大流行的持续反应提供了一个窗口。目的:这项对急诊医生 Twitter 使用情况的回顾性观察研究详细说明了医疗危机如何影响在线急诊医生的话语,以及这种话语如何用作随后激增的预兆。方法:使用 Twitter 的应用程序编程接口确定三个主要急诊医师专业组织的追随者。如果他们明确认定为美国急诊医师,他们和他们的追随者就会被纳入研究。状态或推文是在 2020 年 1 月 4 日首次报告新疾病时和 2020 年 12 月 14 日首次开始接种疫苗时获得的。原始推文使用先前验证的 Valence Aware Dictionary 和 Sentiment Reasoner (VADER) 工具以及使用潜在 Dirichlet 分配无监督机器学习的主题建模进行了情感分析。然后将情绪和话题趋势与新的 COVID-19 病例的每日变化和住院病床利用率相关联。结果:在研究期间,共有 3463 名急诊医师发布了 334,747 条独特的英语推文。在 3463 名参与者中,910 人 (26.3%) 表示他们正在接受培训,在提供性别的 902 名参与者中,有 466 人 (51.7%) 被确定为男性。总体推文量从 2020 年 3 月前的平均每日 481.9 条(SD 72.7)条推文变为此后的平均每天 1065.5 条(SD 257.3)条推文。参数和主题编号调整导致 20 个推文主题,主题一致性为 0.49。除 6 月的一周和 11 月的 4 天外,其他话题均以医疗保健系统为主(45,570/334,747,13.6%)。共同发现对大流行应对、流行病学和临床护理的讨论与 COVID-19 病床利用率(Pearson r = 0.41)中度相关,推文中“covid”、“coronavirus”或“pandemic”的出现也是如此(r = 0.47)。正如 7 天和 28 天移动平均线的持续交叉所证明的那样,COVID-19 推文的势头在全国 COVID-19 医院病床利用率达到峰值之前的平均 45.0 (SD 12.7) 天发生,并且在四个贡献最大的州。结论:急诊医师之间的 COVID-19 Twitter 讨论与医院负担的增加相关并且可能先于医院负担的增加。因此,本研究,

这只是摘要。阅读 JMIR 网站上的完整文章。JMIR 是互联网时代电子健康和医疗保健领域领先的开放获取期刊。
更新日期:2021-07-14
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