Evidence map›Paper›PMID 39556417›Full record

ArticleJMIR infodemiology2024

Association Between X/Twitter and Prescribing Behavior During the COVID-19 Pandemic: Retrospective Ecological Study.

Scott A Helgeson, Rohan M Mudgalkar, Keith A Jacobs, Augustine S Lee, Devang Sanghavi, Pablo Moreno Franco, Ian S Brooks, National COVID Cohort Collaborative (N3C)

Abstract read
In one paragraph

Article in JMIR infodemiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Observational
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Scott A HelgesonDepartment of Pulmonary and Critical Care Medicine, Mayo Clinic, Jacksonville, FL, United States.ORCID https://orcid.org/0000-0001-7590-2293
Rohan M MudgalkarSchool of Information Sciences, Center for Health Informatics, University of Illinois at Urbana-Champaign, Ubana-Champaign, IL, United States.ORCID https://orcid.org/0009-0000-4932-0206
Keith A JacobsSchool of Information Sciences, Center for Health Informatics, University of Illinois at Urbana-Champaign, Ubana-Champaign, IL, United States.ORCID https://orcid.org/0009-0007-7215-7225
Augustine S LeeDepartment of Pulmonary and Critical Care Medicine, Mayo Clinic, Jacksonville, FL, United States.ORCID https://orcid.org/0000-0001-8018-5145
Devang SanghaviDepartment of Critical Care Medicine, Mayo Clinic, Jacksonville, FL, United States.ORCID https://orcid.org/0000-0003-4742-8963
Pablo Moreno FrancoDepartment of Transplant Medicine, Mayo Clinic, Jacksonville, FL, United States.ORCID https://orcid.org/0000-0001-9146-5599
Ian S BrooksSchool of Information Sciences, Center for Health Informatics, University of Illinois at Urbana-Champaign, Ubana-Champaign, IL, United States.ORCID https://orcid.org/0000-0002-3793-3635
National COVID Cohort Collaborative (N3C)see Acknowledgments, .

Funding

CD2H - The National COVID Cohort Collaborative (N3C) IDeA CTR CollaborationU24TR002306 · NCATS · UNIVERSITY OF COLORADO DENVER · PI CHUTE, CHRISTOPHER G, EICHMANN, DAVID A. · 2017 to 2021
$28.9M
NCATS NIH HHS U24 TR002306
6 · The paper itself

Abstract

backgroundSocial 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.

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

methodsIn 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.

resultsA 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.

conclusionsDuring 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.

Indexed as

COVID-19HydroxychloroquinePractice Patterns, Physicians'Social MediaCOVID-19 Drug TreatmentFemaleHumansMaleMiddle AgedPandemicsRetrospective StudiesSARS-CoV-2United StatesHydroxychloroquineCOVID-19disinformationdrugshealthcare utilizationhydroxychloroquineinfectiousinfodemicmedicationmisinformationpandemicpharmaceuticalspharmacologypharmacotherapypharmacyprescriptionrespiratorySARS-CoV-2sentimentsocial mediaTwitterX

Identifiers

PMID39556417
PMCPMC11612580

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.