Secondary Logo

Journal Logo

Original Research Articles: Original Clinical Research Report

Dissemination of Anesthesia Information During the Coronavirus Disease 2019 Pandemic Through Twitter: An Infodemiology Study

Gai, Nan MD*; So, Delvin MSc; Siddiqui, Asad MD*; Steinberg, Benjamin E. MD, PhD*,‡

Author Information
doi: 10.1213/ANE.0000000000005602
  • Free

Abstract

KEY POINTS

  • Question: How has Twitter been used to share anesthesia-related information during the coronavirus disease 2019 (COVID-19) pandemic?
  • Findings: Twitter has been used throughout the pandemic to share anesthesiology-related content, representing 0.01% of all COVID-19 user-generated messages published on Twitter (tweets), with an activity trend in parallel to the US COVID-19 death toll.
  • Meaning: Twitter is a relevant medium through which anesthesiology content is disseminated, and further research is required to determine the true benefits and limitations of this social media platform.

Social media are web-based platforms that allow sharing of user-generated content. Twitter (https://twitter.com) is a popular social media platform with 186 million active daily users.1 Users can share user-generated message published on Twitter (“tweets”) up to 280 characters in length that can also include images and hyperlinks. The use of social media among physicians has increased,2–4 including the utilization of Twitter to disseminate information, promote scientific meetings and publications, and conduct research.5–7

The coronavirus disease 2019 (COVID-19) pandemic has required rapid dissemination of information globally, and medical knowledge has been shared over Twitter.8,9 Translation of research and guidelines into clinical practice often undergoes a time lag.10 Traditional peer-review publication processes may not be fast enough to keep pace with the ever-changing knowledge landscape during a pandemic. However, social media allows for immediate information dissemination. Twitter is free and publicly accessible worldwide. However, misinformation can propagate over social media, creating a parallel infodemic (an overabundance of information that occurs during an epidemic).11 Misinformation can spread given the easy and instantaneous ability to publish tweets, which may further propagate through echo chambers of people following others with similar views.5

The risks of COVID-19 to patients and health care providers have necessitated significant and rapid changes to all aspects of anesthetic practice.12,13 The extent of the discourse relevant to anesthesiologists shared on Twitter has yet to be described. Studying how anesthesia-related information has been discussed during COVID-19 can further delineate the effectiveness and limitations of Twitter as a tool for information dissemination among health care providers, highlight the anesthesiology topics that captured public attention, and inform us of how to better utilize this platform in a professional capacity.

In this study, we aimed to perform an infodemiology study describing the global Twitter conversation surrounding COVID-19 over the course of the pandemic as it relates to anesthesiology topics. Infodemiology has been defined as the “science of distribution and determinants of information in an electronic medium, specifically the Internet, or in a population, with the ultimate aim to inform public health and public policy”.14 We focused on Twitter given its broad popularity as a platform for health care–related discussions.2,5,15 Our objectives were to describe: (1) how Twitter is being used to disseminate anesthesia-related content during the COVID-19 pandemic, (2) how online Twitter conversations relate to the trends of the pandemic, and (3) ways to optimize the use of Twitter by anesthesiologists.

METHODS

Our study protocol and data analysis plan were approved by The Hospital for Sick Children Research Ethics Board (REB# 1000071860) before study initiation, and written informed consent was waived as all data we were using were publicly accessible. This study follows the STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) recommendations for observational studies.16 This was a cross-sectional study of tweets related to anesthesiology practice and COVID-19 tweeted between January 21 and October 13, 2020.

A publicly available COVID-19 Twitter dataset was used for this study (https://github.com/delvinso/covid19_one_hundred_million_unique_tweets). This dataset consisted of prospectively collected tweets related to COVID-19 beginning January 17, 2020, using coronavirus-related keywords (“coronavirus,” “wuhan,” “wearamask,” and “covid”). This collection was achieved through a custom R program (R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/) reliant on the retweets package17 to access Twitter’s search application programming interface and does not include any retweets. Tweets were collected on a daily basis.

A stepwise filtering approach was taken to identify tweets meeting inclusion criteria (Figure 1). Tweets were first limited to the English language. Anesthesia-related tweets were identified by filtering the COVID-19 Twitter dataset for text relevant to various aspects of anesthesia practice. This was done using an inclusive approach, including search strings related to general and subspecialty anesthetic practice, airway management, perioperative medicine, intraoperative care, regional anesthesia techniques, and acute and chronic pain (Supplemental Digital Content 1, Search Filters, http://links.lww.com/AA/D547) by consensus between all anesthesiologist authors based on guidance from practice guidelines and recommendations.12,13,18 The keywords used, although not exhaustive of all possible words potentially associated with the practice of anesthesiology, were judged to be generally inclusive of most anesthetic practice and on initial screening captured tweets that contained more specific technical terms. Only tweets that matched the search strings in the main tweet text were included; the main tweet text did not include handles (ie, usernames) or hyperlinks.

Figure 1.
Figure 1.:
STROBE diagram of included tweets. *includes additional permutations of these strings, for example, for intubate. Alternatively, the matched strings may be conjugated forms such as “intubated, intubating, intubates, intubation.” COVID indicates coronavirus disease; STROBE, STrengthening the Reporting of OBservational studies in Epidemiology.

A quality control measure was applied to manually review approximately 10% of all initially included tweets for the first 7 months to confirm relevance to anesthesiologists (sampled at 10% per day to be representative of fluctuating daily tweet activity). This was performed by practicing consultant anesthesiologists. A tweet was considered to be related to anesthesia practice if it described a topic that might be relevant to an anesthesiologist reading the text or may have informed their practice. Tweets that also described public and patient perceptions of anesthesiologists or their experiences with anesthetic care were included. A random sample of 5432 tweets was manually reviewed to confirm the adequacy of the inclusion filters. Based on this manual review, 159 (2.9%) of reviewed tweets were judged as irrelevant to anesthesia or pain management discussions, and further exclusion filters were created as a result. After consensus based on the manual review, we excluded tweets that only matched the terms “intubate,” “extubate,” or “pain” and no other search strings (Supplemental Digital Content 1, Search Filters, http://links.lww.com/AA/D547). Tweets that only mentioned words such as “intubate” or “extubate” reflected tallies of number of patients intubated or stories of persons who were intubated in intensive care units (ICUs) and did not specifically reflect anesthetic topics. Similarly, the term “pain” is used very often in general conversation that was not judged to be relevant for physicians who treat acute or chronic pain. The aim of these exclusions was to ensure that our tweet collection was more directly relevant to anesthesiologists using Twitter as a communication platform.

The first tweet meeting final inclusion criteria appeared on January 21. Within the study period of January 21 to October 13, 2020, a total of 23,270 tweets were ultimately included for review. October 13 was chosen as the end date as several new search keywords were added to the public dataset on October 14. The STROBE flow diagram is shown in Figure 1.

Statistical Analysis

Using descriptive statistics, the dataset of tweets meeting inclusion criteria was reviewed for tweet characteristics (hashtags, retweet count, location data, links, and attachments) and user account characteristics (verified status, self-identified anesthesiologist, self-identified anesthesia-related journal, self-identified anesthesia-related association or society, and location data). A hashtag is a word or phrase preceded by the # symbol that functions as a label to search for all tweets including a specific hashtag.19 A retweet is a repeat post of a tweet as a way to disseminate the original tweet. Verified status conveyed by Twitter is a badge of authenticity ensuring that the account truly represents the person or organization to which they claim affiliation.20 Such accounts appear on Twitter with a blue checkmark icon next to their username. Self-reported anesthesia-related associations or societies included accounts whose name or description identified them as an organization (professional association, society, governing or certifying body) related to anesthesiology.

Additionally, tweets were filtered for words related to specific topics of interest that were likely to impact or inform anesthesiologists of COVID-19–related practice (airway management techniques, personal protective equipment [PPE], ventilators, COVID-19 testing, and pain management). Tweet activity was also reviewed over time to show how it changed during the course of the pandemic. Based on the geographic data of included users, relevant COVID-19 country-level data were visually compared to our tweet trends.

Descriptive statistics were obtained for these parameters. Comparisons between the retweeted and nonretweeted groups were made using χ2 testing. Comparisons between daily tweet counts on weekends and weekdays were made using the Mann-Whitney U test. For all comparisons, a P value <.05 was considered statistically significant. Data analyses were performed using R Studio version 1.2.5033.

RESULTS

Summary Characteristics of Tweet and Account Characteristics

Summary characteristics of tweet and user data are presented in Table 1. The initial COVID-19 database contained 241,732,881 tweets between January 21 and October 13, 2020. A total of 23,270 tweets (0.01% of all COVID tweets) met inclusion criteria and were included in this study. These tweets were generated by 15,770 accounts. Eight hundred ninety-five accounts (5.7% of all users) were of verified status. Twitter offers 2 variables for reporting location data: the location as tagged by each tweet (only 4.2% of tweets included this data) and the location self-described by each user. Because there are no limits on what users can enter, many enter fictional locations or phrases. In our dataset, the majority of accounts (10,194 [64.6%]) included location data that could be confidently attributed to a specific country. Just over half of users with location data (5288 [51.9%]) self-reported as American (Table 1).

Table 1. - Summary Characteristics of Tweets and Users Included for Analysis
Characteristics Tweets Proportion of all Tweets (%) Accounts Proportion of all accounts (%)
Total 23,270 NA 15,770 NA
Verified account 1260 5.4 895 5.7
Identifiable location displayed by user 15,204 65.3 10,194 64.6
Account is from the United States 7944 34.1 5288 33.5
Anesthesiologist 1882 8.1 749 4.8
Anesthesia and/or Pain Journal 320 1.4 20 0.1
Anesthesia and/or Pain Association or Society 486 2.1 62 0.4
Tweets containing at least one hashtag 7853 33.8 NA NA
Tweets containing at least one hyperlink 12,152 52.2 NA NA
Tweets containing an attachment 5116 22.0 NA NA
Tweets about airway management 3304 14.2 2408 15.3
Tweets about PPE 2445 10.5 2026 12.8
Tweets about ventilators 1213 5.2 1066 6.8
Tweets about COVID testing 2912 12.5 2599 16.5
Tweets about pain 529 2.3 295 1.9
Abbreviations: COVID, coronavirus disease; NA, not applicable; PPE, personal protective equipment.

A minority of tweets (7853; 33.8%) used at least one hashtag. The most frequently used COVID-19 hashtags were #covid19 (2477 tweets), #coronavirus (1625 tweets), and #covid (997 tweets). Among the non-COVID hashtags, the most frequently used hashtags were #anesthesia (662 tweets), #anesthesiology (384 tweets), and #anaesthesia (294 tweets). Figure 2 shows the relative frequencies of non-COVID hashtags used. A minority of tweets (33%) were retweeted. For context, in the unfiltered English-language COVID-19 dataset, 24% of all tweets were retweeted at least once. Conversely, 887 (74%) of 1193 tweets originating from verified accounts were retweeted. Verified status was found to be significantly associated with being retweeted (χ2 = 955.98; df = 1, P < .001).

Figure 2.
Figure 2.:
Word cloud of the most popular non-COVID-19 hashtags used in included tweets. Size and color of text are proportional to relative hashtag frequency. Larger text reflects higher frequency. Colors from highest to lowest frequency: red, orange, purple, green, blue, gray. COVID-19 indicates coronavirus disease 2019.

Seven hundred forty-nine users (4.8%) were self-identified as anesthesiologists based on their name and/or profile description. These anesthesiologists contributed to 1882 tweets (8.1% of all tweets), and 45% of these tweets were retweeted at least once. Most identifiable anesthesiologists were from the United States (552/749, 73.7%). Only one self-identified anesthesiologist in this dataset had a verified account during the study period. Twenty accounts were identifiable as representative of anesthesia or pain journals (Supplemental Digital Content 2, Table 1, http://links.lww.com/AA/D548), one of which was verified. These 20 accounts generated a total of 320 (1.4%) COVID-related tweets. Within this group, the majority of tweets were never retweeted (54%). Among the 10 highest impact factor anesthesia and pain journals, 6 generated tweets meeting inclusion criteria (Anesthesiology, Regional Anesthesia and Pain Medicine, British Journal of Anaesthesia, Anaesthesia, Anesthesia & Analgesia, and Canadian Journal of Anesthesia).21 The majority of the tweets originating from these high-impact journal accounts were frequently retweeted (70%–100% retweet rate) (Supplemental Digital Content 2, Table 1, http://links.lww.com/AA/D548).

Topics of interest specifically searched for among COVID-19 tweets are shown in Table 2 and were categorized into 5 main topics: airway management, PPE, ventilators, COVID-19 testing, and pain. Three topics were discussed in over 10% of all tweets in our dataset: airway management (14.2%), COVID testing (12.5%), and PPE (10.5%).

Table 2. - Tweets Containing Words Matching Specific Topics of Interest
Topic Tweets, N = 23,270 Proportion of total tweets (%)
Topic 1: airway management 3304 14.2
 Intubation 3266 14.0
 Rapid sequence induction 34 0.1
 Bag mask ventilation 60 0.3
Topic 2: PPE group 2445 10.5
 General PPE terms 991 4.3
 Masks 1089 4.7
 PAPR 48 0.2
 N95 respirators 324 1.4
 Neck protection 7 0.0
 Face/eye shields 95 0.4
 Gowns 59 0.3
 Shoe covers 9 0.0
 General barrier 42 0.2
 Intubation boxes/drapes 78 0.3
 Aerosol 424 1.8
 Droplet 80 0.3
Topic 3: ventilators 1213 5.2
Topic 4: COVID testing 2912 12.5
Topic 5: all tweets about pain 529 2.3
Abbreviations: COVID, coronavirus disease; N95, N95 respirator; PAPR, powered air-purifying respirator; PPE, personal protective equipment.

The majority of tweets reviewed (12,152, 52.2%) included at least one hyperlink, with the most popular linked domain being to another tweet (1602 tweets, 13.2%, linked back to Twitter). Among non-Twitter links, the most popular websites referenced were Miami Herald (https://www.miamiherald.com), YouTube (https://www.youtube.com), The American Society of Anesthesiologists (https://www.asahq.org), The National Institutes of Health (https://www.nih.gov), and The Washington Post (https://www.washingtonpost.com) (Supplemental Digital Content 2, Figure, http://links.lww.com/AA/D548). The most frequently linked websites represent news organizations, social media platforms, medical organizations, or scientific publications. A minority of tweets (5116, 22.0%) included a visual attachment with their tweet.

Temporal Profile of Tweets

We next evaluated the temporal profile of tweet activity over this 38-week period from the beginning of the COVID-19 pandemic. The daily tweet activity is shown in Figure 3A. Relevant tweets appeared before the official naming of COVID-19 and the declaration of a pandemic by the World Health Organization (WHO),22 demonstrating that online discourse relevant to anesthesiologists began early on in the outbreak of COVID-19. The highest daily tweet activity (398 tweets in 1 day) was seen on April 8, 2020. This initial peak was approximately coincident with the day of 1 million worldwide cases of COVID-19. Subsequent peaks later in the pandemic occurred on July 28 (274 tweets) and August 27 (327 tweets). Inspection of the tweet content for these later dates showed that tweets on July 28 reflected mainly posts about a party after which 18 anesthesiology trainees were diagnosed with COVD-19. Many of these tweets linked to the news article at Miami Herald. The peak on August 27 (327 tweets for the day) focused largely on comments about the Centers for Disease Control and Prevention modifying testing guidelines for COVID-19, while Dr Anthony Fauci, Director of the National Institute of Allergy and Infectious Disease, was undergoing general anesthesia.23

Figure 3.
Figure 3.:
Trends in daily COVID-19 tweets over time. A, Daily COVID-19 tweet activity related to anesthesia topics. Major global events annotated in pink as described by World Health Organization timeline.23 Peaks of daily tweet activity: *July 28 (tweets describing anesthesiology trainees who tested positive for COVID-19 after attending a party) and **August 27 (tweets describing CDC guidelines being changed while Dr Anthony Fauci was undergoing general anesthesia). B, Daily tweets relevant to COVID-19 published by accounts self-reported to be anesthesiologists. C, 7-day rolling average of daily tweets related to COVID-19 that also include terms relevant to 1 of 5 topics of interest: airway management, personal protective equipment, ventilators, COVID testing, and pain. CDC indicates Centers for Disease Control and Prevention; COVID-19, coronavirus disease 2019.

Daily COVID-19 tweet activity of self-identified anesthesiologists is shown in Figure 3B. The daily activity trend generally follows that of the overall dataset. Figure 3C shows the 7-day rolling mean of daily tweets mentioning terms related to specific topics of interest. The temporal trend of airway management, PPE, and ventilator tweets follows the overall daily tweet trend, whereas the daily tweet trend for tweets related to COVID-19 testing and pain did not. COVID-19 testing tweets had a dramatic peak in late August because of tweets discussing Dr Fauci undergoing general anesthesia with concurrent changes to COVID-testing guidelines. Pain-related tweets generally showed no significant peaks and remained at a relatively low constant level over the course of the pandemic.

Given that at least half of the included users and tweets were reported as American (Table 1), we compared our tweet trend with US COVID data24 (Figure 4A). Normalized 14-day rolling averages were obtained for daily tweet activity and US daily death rates (Figure 4B). Visually, these trends were remarkably similar, with the daily tweet activity curve preceding US daily death rates by 16 days (Figure 4C).

Figure 4.
Figure 4.:
Comparison of COVID-19 tweet activity and US COVID-19 death counts. A, Daily count numbers. B, Normalized, 14-day rolling average of US daily deaths and COVID-19 tweets. C, The normalized US death count is observed as a 16-day later shift compared to the normalized tweet count. COVID-19 indicates coronavirus disease 2019.

Tweet activity between weekdays and weekends was compared. Daily tweet counts were shown to be nonnormally distributed based on histogram visualization and Shapiro-Wilk normality testing. A Mann-Whitney U test indicated that daily tweets on weekdays (median 75 tweets daily) was significantly higher than on weekends (median 52 tweets daily), P = .004.

DISCUSSION

Disseminating Anesthesiology Content Over Twitter

In this study, we identified a substantial collection of English-language tweets related to anesthesiology throughout the COVID-19 pandemic. This anesthesiology Twitter conversation represented 0.01% of all COVID-19 tweets, which, although low, is noteworthy given the profound global and societal implications of the pandemic.

COVID-19 has been plagued by a parallel infodemic of misinformation, exacerbated by the rapid propagation of information on social media, including misleading and false claims.11 Just over half (52.2%) of tweets contained a link, most frequently to other social media websites, news outlets, anesthesia associations, and scientific publications. Reassuringly, a large proportion of hyperlinks referenced reliable sources. Most tweets (67%) received no amplification via retweets. The majority of tweets originating from the highest impact anesthesiology journals were highly retweeted (Supplemental Digital Content 2, Table 1, http://links.lww.com/AA/D548). In the context of the pandemic, high-quality, peer-reviewed content provided by academic journals is being recognized and propagated through Twitter.

Anesthesiologists have played pivotal roles during the pandemic.25 They have been recognized for their airway management expertise, adapted their practice to additional PPE requirements, and made preparations to convert operating rooms into ICU rooms.26–29 These represent dramatic practice changes that occurred quickly. It would be expected, therefore, for anesthesiologists to turn to social media as a fast and accessible platform for information gathering. The topics of airway management, PPE, and COVID-19 testing were represented in over 10% of included tweets, speaking to the attention these discussions received. Our summary of non-COVID-19 hashtags used in this tweet collection (Figure 2) reflects the varied roles anesthesiologists play within hospital systems and society.

Twitter Trends Related to the Pandemic

In our study, the trend of daily total tweets appeared to precede a similar trend of US COVID-19 daily death counts. We found that as COVID-19 cases were surging and disease severity was increasing, discussion within anesthesiology Twitter discourse was increasing in parallel, likely reflecting attempts to share knowledge of a new disease process. This trend is also reflected in the daily numbers of tweets authored by anesthesiologists (Figure 4B) and discussion on topics of airway management, PPE, and ventilators (Figure 4C), with a high peak of activity as the pandemic was initially unfolding in North America.

Optimizing Twitter Use for Anesthesia and Anesthesiologists

Twitter has been shown to be successful in disseminating information during the pandemic.8,9,30 To help users navigate Twitter, we summarized the most frequently used hashtags, which are important for garnering readership (Supplemental Digital Content 2, Table 2, http://links.lww.com/AA/D548). The most popular non-COVID hashtag was #anesthesia (Figure 2). Tweet activity was significantly higher on weekdays than weekends, suggesting that tweets published on a weekday would likely receive more attention.

According to Twitter, verified status “lets people know that an account of public interest is authentic.”20 Such accounts likely command more attention and credibility,31 and have been associated with less misinformation.32 Verified account status was significantly associated with being retweeted, but this is likely confounded by such accounts already possessing larger numbers of followers before verification.20 The combination of higher retweet amplification with less misinformation is important to combat the COVID-19 infodemic.11 Notably, during this study period, only one self-identified anesthesiologist account was verified. Further applications for account verification by prominent anesthesiologists could help amplify their messages.

Limitations and Future Work

Several limitations exist in this study. The dataset relied on 4 COVID-19 terms, and tweets not including these terms would have been missed. Our initial search filters were intended to be as inclusive as possible, but we chose to exclude tweets with sole matches to “intubate,” “extubate,” and “pain” as these terms have become frequently used in nonmedical conversation during this pandemic. This speaks to the enormity of this terrible disease and the high numbers of patients requiring intensive care. We excluded these terms based on our belief that such matches did not add meaningful value for anesthesiologists gathering information on Twitter. In so doing, we likely excluded otherwise relevant tweets that only used such terms. Additionally, our choice of search keywords could have been more exhaustive for more anesthesia-specific terms and may have contributed to missing relevant tweets. We included only English-language tweets, which led to just over half of our sample being from American users. Our study is mostly reflective of the North American experience and can only be cautiously generalized worldwide. The number of anesthesiologist accounts contributing to this conversation was low (749, 4.8% of all accounts). Of these, 552 were likely American, representing a small fraction of anesthesiologists in the United States (estimated at 42,267),33 and suggest that a small proportion of anesthesiologists actively tweet. This is likely an underestimate as we tallied only accounts who self-identified as anesthesiologists and tweeted specifically about anesthesiology and COVID-19. Passive Twitter users not generating tweets would not be counted.

Future social media research should harness advanced computational techniques to further delineate how these platforms are being used by medical personnel and the public. In addition to thematic analyses, these platforms have the potential to gauge sentiment of health care providers during stressful times such as a pandemic. The misinformation infodemic of COVID-19 should emphasize caution to users gathering and sharing information on Twitter. Tweet content is not peer-reviewed. The instantaneous and public nature of messages limited to 280 characters may prevent in-depth and nuanced scientific discourse. High-quality peer-reviewed content should be amplified as much as possible, and further studies to test effective means of disseminating scientific knowledge should be undertaken. Twitter has been touted as a platform for “postpublication peer review.”5,34 The experience of one journal, Anaesthesia, highlighted the steps taken to pivot to e-Learning approaches during the pandemic, including using Twitter to disseminate new publications as well as expanding on discussion points arising from tweets.30

We show that Twitter is a relevant platform for sharing information related to anesthesiology practice during the COVID-19 pandemic. However, the true level of impact and net benefit of using this platform for the anesthesiology community remain unclear. Future work should address these questions as well as develop a research framework to standardize social media-based research.

ACKNOWLEDGMENTS

The authors thank Dr David Faraoni, MD, PhD (Department of Anesthesia and Pain Medicine, The Hospital for Sick Children), for his insightful feedback, advice, and critical revision of this article.

DISCLOSURES

Name: Nan Gai, MD.

Contribution: This author helped conceive and design the study, review and interpret the data, and draft and critically revise the manuscript.

Name: Delvin So, MSc.

Contribution: This author helped conceive and design the study, acquire the data, review and interpret the data, and critically revise the manuscript.

Name: Asad Siddiqui, MD.

Contribution: This author helped conceive and design the study, interpret the data, and critically revise the manuscript.

Name: Benjamin E. Steinberg, MD, PhD.

Contribution: This author helped conceive and design the study, review and interpret the data, and critically revise the manuscript.

This manuscript was handled by: Jean-Francois Pittet, MD.

    REFERENCES

    1. Twitter. Q2 2020 Letter to Shareholders. 2020. Accessed October 28, 2020. https://s22.q4cdn.com/826641620/files/doc_financials/2020/q1/Q1-2020-Shareholder-Letter.pdf
    2. Chan TM, Dzara K, Dimeo SP, Bhalerao A, Maggio LA. Social media in knowledge translation and education for physicians and trainees: a scoping review. Perspect Med Educ. 2020;9:20–30.
    3. Bennett KG, Berlin NL, MacEachern MP, Buchman SR, Preminger BA, Vercler CJ. The ethical and professional use of social media in surgery: a systematic review of the literature. Plast Reconstr Surg. 2018;142:388E–398E.
    4. Pemmaraju N, Thompson MA, Mesa RA, Desai T. Analysis of the use and impact of Twitter during American Society of Clinical Oncology annual meetings from 2011 to 2016: focus on advanced metrics and user trends. J Oncol Pract. 2017;13:e623–e630.
    5. Choo EK, Ranney ML, Chan TM, et al. Twitter as a tool for communication and knowledge exchange in academic medicine: a guide for skeptics and novices. Med Teach. 2015;37:411–416.
    6. Hawkins CM, Hunter M, Kolenic GE, Carlos RC. Social media and peer-reviewed medical journal readership: a randomized prospective controlled trial. J Am Coll Radiol. 2017;14:596–602.
    7. Sinnenberg L, Buttenheim AM, Padrez K, Mancheno C, Ungar L, Merchant RM. Twitter as a tool for health research: a systematic review. Am J Public Health. 2017;107:e1–e8.
    8. Kudchadkar SR, Carroll CL. Using social media for rapid information dissemination in a pandemic: #PedsICU and coronavirus disease 2019. Pediatr Crit Care Med. 2020;21:1–9.
    9. Chan AKM, Nickson CP, Rudolph JW, Lee A, Joynt GM. Social media for rapid knowledge dissemination: early experience from the COVID-19 pandemic. Anaesthesia. 2020;75:1579–1582.
    10. Morris ZS, Wooding S, Grant J. The answer is 17 years, what is the question: understanding time lags in translational research. J R Soc Med. 2011;104:510–520.
    11. Tangcharoensathien V, Calleja N, Nguyen T, et al. Framework for managing the COVID-19 infodemic: methods and results of an online, crowdsourced WHO technical consultation. J Med Internet Res. 2020;22:e19659.
    12. Cook TM, El-Boghdadly K, McGuire B, McNarry AF, Patel A, Higgs A. Consensus guidelines for managing the airway in patients with COVID-19: guidelines from the Difficult Airway Society, the Association of Anaesthetists the Intensive Care Society, the Faculty of Intensive Care Medicine and the Royal College of Anaesthetist. Anaesthesia. 2020;75:785–799.
    13. Wax RS, Christian MD. Practical recommendations for critical care and anesthesiology teams caring for novel coronavirus (2019-nCoV) patients. Can J Anaesth. 2020;67:568–576.
    14. Eysenbach G. Infodemiology and infoveillance: framework for an emerging set of public health informatics methods to analyze search, communication and publication behavior on the Internet. J Med Internet Res. 2009;11:e11.
    15. Pershad Y, Hangge PT, Albadawi H, Oklu R. Social medicine: Twitter in healthcare. J Clin Med. 2018;7:E121.
    16. von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP. STROBE Initiative. The strengthening the reporting of observational studies in epidemiology (STROBE) statement: guidelines for reporting observational studies. Lancet. 2007;370:1453–1457.
    17. Kearney M. rtweet: collecting and analyzing Twitter data. J Open Source Softw. 2019;4:1829.
    18. Dobson G, Chow L, Filteau L, et al. Guidelines to the practice of anesthesia—revised edition 2021. Can J Anesth. 2021;68:92–129.
    19. Twitter. How to Use Hashtags. 2020. Accessed November 22, 2020. https://help.twitter.com/en/using-twitter/how-to-use-hashtags
    20. Twitter. About Verified Accounts. 2020. Accessed October 25, 2020 https://help.twitter.com/en/managing-your-account/about-twitter-verified-accounts
    21. Clarivate Web of Science. Web of Science Journal Citation Reports. 2020. Accessed August 4, 2020. https://clarivate.com/webofsciencegroup/web-of-science-journal-citation-reports-2020-infographic
    22. World Health Organization. Timeline of WHO’s Response to COVID-19. 2020. Accessed October 20, 2020. https://www.who.int/news-room/detail/29-06-2020-covidtimeline
    23. Diamond J, Holmes K, Gupta S. Fauci says he was in surgery when task force discussed CDC testing guidelines. CNN. Published August 27, 2020. Accessed May 10, 2021. https://www.cnn.com/2020/08/26/politics/fauci-coronavirus-cdc-testing/index.html.
    24. European Centre for Disease Prevention and Control. Download the Daily Number of New Reported Cases of COVID-19 by Country Worldwide. 2020. Accessed November 11, 2020. https://www.ecdc.europa.eu/en/publications-data/download-todays-data-geographic-distribution-covid-19-cases-worldwide
    25. Chen X, Liu Y, Gong Y, et al. Perioperative management of patients infected with the novel coronavirus: recommendation from the Joint Task Force of the Chinese Society of Anesthesiology and the Chinese Association of Anesthesiologists. Anesthesiology. 2020;132:1307–1316.
    26. Patel GP, Collins JS, Sullivan CL, et al. Management of coronavirus disease 2019 intubation teams. A A Pract. 2020;14:e01263.
    27. Ahmad I, Jeyarajah J, Nair G, et al. A prospective, observational, cohort study of airway management of patients with COVID-19 by specialist tracheal intubation teams. Can J Anesth. 2020;68:1–8.
    28. Centers for Disease Control and Prevention. Interim Infection Prevention and Control Recommendations for Healthcare Personnel During the Coronavirus Disease 2019 (COVID-19) Pandemic. 2020. Accessed October 25, 2020. https://www.cdc.gov/coronavirus/2019-ncov/hcp/infection-control-recommendations.html
    29. American Society of Anesthesiologists; Anesthesia Patient Safety Foundation. APSF/ASA Guidance on Purposing Anesthesia Machines as ICU Ventilators. 2020. Accessed October 28, 2020. https://www.asahq.org/in-the-spotlight/coronavirus-covid-19-information/purposing-anesthesia-machines-for-ventilators
    30. Fawcett WJ, Charlesworth M, Cook TM, Klein AA. Education and scientific dissemination during the COVID-19 pandemic. Anaesthesia. 2020;76:1–4.
    31. Hearn A. Verified: self-presentation, identity management, and selfhood in the age of big data. Pop Commun. 2017;15:62–77.
    32. Kouzy R, Abi Jaoude J, Kraitem A, et al. Coronavirus goes viral: quantifying the COVID-19 misinformation epidemic on twitter. Cureus. 2020;12:e7255.
    33. Association of American Medical Colleges. Physician Specialty Data Report. 2019. Accessed February 4, 2021. https://www.aamc.org/data-reports/workforce/interactive-data/active-physicians-largest-specialties-2019
    34. Mandavilli A. Peer review: trial by Twitter. Nature. 2011;469:286–287.

    Supplemental Digital Content

    Copyright © 2021 International Anesthesia Research Society