Evidence map›Paper›PMID 34081612›Full record

Observational studyJournal of medical Internet research2021

Emergency Physician Twitter Use in the COVID-19 Pandemic as a Potential Predictor of Impending Surge: Retrospective Observational Study.

Colton Margus, Natasha Brown, Attila J Hertelendy, Michelle R Safferman, Alexander Hart, Gregory R Ciottone

Abstract readObservational Study
In one paragraph

Observational study in Journal of medical Internet research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

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  10. COVID-19 Literature Published in Emergency Medicine Journals in 2020.The western journal of emergency medicine · 2022
    Observational
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Colton MargusDivision of Disaster Medicine, Department of Emergency Medicine, Beth Israel Deaconess Medical Center, Boston, MA, United States.ORCID 0000-0003-2704-4428
Natasha BrownDivision of Disaster Medicine, Department of Emergency Medicine, Beth Israel Deaconess Medical Center, Boston, MA, United States.ORCID 0000-0002-1750-5641
Attila J HertelendyDivision of Disaster Medicine, Department of Emergency Medicine, Beth Israel Deaconess Medical Center, Boston, MA, United States.ORCID 0000-0001-6174-0289
Michelle R SaffermanDepartment of Emergency Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States.ORCID 0000-0002-9967-2733
Alexander HartDivision of Disaster Medicine, Department of Emergency Medicine, Beth Israel Deaconess Medical Center, Boston, MA, United States.ORCID 0000-0002-0910-2316
Gregory R CiottoneDivision of Disaster Medicine, Department of Emergency Medicine, Beth Israel Deaconess Medical Center, Boston, MA, United States.ORCID 0000-0002-3313-1379

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe early conversations on social media by emergency physicians offer a window into the ongoing response to the COVID-19 pandemic.

objectiveThis 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.

methodsFollowers 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.

resultsA 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.

conclusionsCOVID-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.

Indexed as

CommunicationEmergency MedicinePhysiciansCOVID-19COVID-19 VaccinesForecastingHospitalizationHumansLatent Class AnalysisLongitudinal StudiesPandemicsRetrospective StudiesSARS-CoV-2Social MediaVaccinationCOVID-19 VaccinesCOVID-19COVID-19 pandemiccrisis standards of caredisaster medicineemergency medicineinfodemiologyinternetlatent Dirichlet allocationphysician wellnesssentiment analysissocial mediasurge capacitytopic modelingTwitter

Identifiers

PMID34081612
PMCPMC8281822

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.