COVID-19 大流行期间 X/Twitter 与处方行为之间的关系:回顾性生态研究。

IF 3.5 Q1 HEALTH CARE SCIENCES & SERVICES
JMIR infodemiology Pub Date : 2024-11-18 DOI:10.2196/56675
Scott A Helgeson, Rohan M Mudgalkar, Keith A Jacobs, Augustine S Lee, Devang Sanghavi, Pablo Moreno Franco, Ian S Brooks
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引用次数: 0

摘要

背景:社交媒体已成为医疗服务提供者快速分享信息的重要工具。然而,由于社交媒体缺乏内容策划和专业知识,存在误导信息和过早传播未经验证的数据的风险,可能会因错误信息的快速和大规模传播而导致广泛的有害影响:我们旨在以 COVID-19 大流行为背景,确定社交媒体是否与羟氯喹的处方行为有不当关联:在这项回顾性研究中,我们收集了 2020 年 1 月至 12 月期间美国 48 家医院使用羟氯喹的情况。我们使用可访问 X/Twitter 数据的商业聚合器 Brandwatch 收集了来自 X/Twitter 的社交媒体数据,重点关注 "羟氯喹 "和 "Plaquenil "的提及情况。使用 Brandwatch 的情感分析工具对推文进行了情感分类(正面、负面或中性),并按日期对结果进行了分类。使用的羟氯喹处方数据来自 2020 年全国 COVID 队列协作组织。使用格兰杰因果关系和线性回归模型来检验 X/Twitter 提及与处方趋势之间的关系,并使用通过向量自动回归确定的最佳时间滞后:共发现 581 748 名确诊 COVID-19 的患者。COVID-19 阳性病例的日中位数为 1318.5(IQR 1005.75-1940.3)。在出现首例 COVID-19 确诊病例之前,羟氯喹的处方量中位数为每天 559(IQR 339.25-728.25)个新处方。处方量和病例数都出现了周日效应。在 2020 年大流行期间,羟氯喹处方量显著增加,中位数为每天 685.5(IQR 459.75-897.25),比基线增加了 22.6%。峰值出现在 2020 年 4 月 2 日,共有 3411 个处方,增长了 397.6%。2020 年 4 月 5 日,羟氯喹在 X/Twitter 上的提及量达到峰值,为每天 254770 次,而 2020 年 1 月 21 日前的基线为每天 9124 次。在本研究期间,共记录了 3,823,595 条推文,其中正面推文占 10.09%(n=386,115),负面推文占 37.87%(n=1,448,030),中性推文占 52.03%(n=1,989,450)。推文与羟氯喹处方之间因果关系的最佳时间为 1 天。单变量分析表明,所有情绪类型都存在显著关联,其中正面推文的影响最大。多变量分析显示,只有中性和负面推文对次日处方率有显著影响:结论:在 COVID-19 大流行的第一年,X/Twitter 提及与羟氯喹处方数量之间存在显著关联。这项研究表明,X/Twitter 与羟氯喹的处方行为有关。临床医生需要警惕他们可能无意识地接触到社交媒体作为医学知识的来源,而医疗系统和组织在社交媒体平台上分享证据时,需要更加努力地识别专业知识、证据来源和证据质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Association Between X/Twitter and Prescribing Behavior During the COVID-19 Pandemic: Retrospective Ecological Study.

Background: Social media has become a vital tool for health care providers to quickly share information. However, its lack of content curation and expertise poses risks of misinformation and premature dissemination of unvalidated data, potentially leading to widespread harmful effects due to the rapid and large-scale spread of incorrect information.

Objective: We aim to determine whether social media had an undue association with the prescribing behavior of hydroxychloroquine, using the COVID-19 pandemic as the setting.

Methods: In this retrospective study, we gathered the use of hydroxychloroquine in 48 hospitals in the United States between January and December 2020. Social media data from X/Twitter was collected using Brandwatch, a commercial aggregator with access to X/Twitter's data, and focused on mentions of "hydroxychloroquine" and "Plaquenil." Tweets were categorized by sentiment (positive, negative, or neutral) using Brandwatch's sentiment analysis tool, with results classified by date. Hydroxychloroquine prescription data from the National COVID Cohort Collaborative for 2020 was used. Granger causality and linear regression models were used to examine relationships between X/Twitter mentions and prescription trends, using optimum time lags determined via vector auto-regression.

Results: A total of 581,748 patients with confirmed COVID-19 were identified. The median daily number of positive COVID-19 cases was 1318.5 (IQR 1005.75-1940.3). Before the first confirmed COVID-19 case, hydroxychloroquine was prescribed at a median rate of 559 (IQR 339.25-728.25) new prescriptions per day. A day-of-the-week effect was noted in both prescriptions and case counts. During the pandemic in 2020, hydroxychloroquine prescriptions increased significantly, with a median of 685.5 (IQR 459.75-897.25) per day, representing a 22.6% rise from baseline. The peak occurred on April 2, 2020, with 3411 prescriptions, a 397.6% increase. Hydroxychloroquine mentions on X/Twitter peaked at 254,770 per day on April 5, 2020, compared to a baseline of 9124 mentions per day before January 21, 2020. During this study's period, 3,823,595 total tweets were recorded, with 10.09% (n=386,115) positive, 37.87% (n=1,448,030) negative, and 52.03% (n=1,989,450) neutral sentiments. A 1-day lag was identified as the optimal time for causal association between tweets and hydroxychloroquine prescriptions. Univariate analysis showed significant associations across all sentiment types, with the largest impact from positive tweets. Multivariate analysis revealed only neutral and negative tweets significantly affected next-day prescription rates.

Conclusions: During the first year of the COVID-19 pandemic, there was a significant association between X/Twitter mentions and the number of prescriptions of hydroxychloroquine. This study showed that X/Twitter has an association with the prescribing behavior of hydroxychloroquine. Clinicians need to be vigilant about their potential unconscious exposure to social media as a source of medical knowledge, and health systems and organizations need to be more diligent in identifying expertise, source, and quality of evidence when shared on social media platforms.

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