Evidence map›Paper›PMID 34592539›Full record

ArticleInternational journal of medical informatics2021

Using a mixed methods approach to identify public perception of vaping risks and overall health outcomes on Twitter during the 2019 EVALI outbreak.

Erin Kasson, Avineet Kumar Singh, Ming Huang, Dezhi Wu, Patricia Cavazos-Rehg

Abstract read
In one paragraph

Article in International journal of medical informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

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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

5 authors.

Erin KassonDepartment of Psychiatry, Washington University School of Medicine, 660 S Euclid Ave, St. Louis, MO, USA.
Avineet Kumar SinghDepartment of Computer Science and Engineering, University of South Carolina, Columbia, SC, USA.
Ming HuangDepartment of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, USA.
Dezhi WuDepartment of Integrated Information Technology, University of South Carolina, Columbia, SC, USA. Electronic address: dezhiwu@cec.sc.edu.
Patricia Cavazos-RehgDepartment of Psychiatry, Washington University School of Medicine, 660 S Euclid Ave, St. Louis, MO, USA.

Funding

Leveraging Social Media for Substance Use Behavioral Insight K02DA043657 · NIDA · WASHINGTON UNIVERSITY · PI CAVAZOS-REHG, PATRICIA A · 2017 to 2021
$591k
NIDA NIH HHS K02 DA043657
6 · The paper itself

Abstract

introductionVaping product use (i.e., e-cigarettes) has been rising since 2000 in the United States. Negative health outcomes associated with vaping products have created public uncertainty and debates on social media platforms. This study explores the feasibility of using social media as a surveillance tool to identify relevant posts and at-risk vaping users.

methodsUsing an interdisciplinary method that leverages natural language processing and manual content analysis, we extracted and analyzed 794,620 vaping-related tweets on Twitter. After observing significant increases in vaping-related tweets in July, August, and September 2019, additional human coding was completed on a subset of these tweets to better understand primary themes of vaping-related discussions on Twitter during this time frame.

resultsWe found significant increases in tweets related to negative health outcomes such as acute lung injury and respiratory issues during the outbreak of e-cigarette/vaping associated lung injury (EVALI) in the fall of 2019. Positive sentiment toward vaping remained high, even across the peak of this outbreak in July, August, and September. Tweets mentioning the public perceptions of youth risk were concerning, as were increases in marketing and marijuana-related tweets during this time. DISCUSSION: The preliminary results of this study suggest the feasibility of using Twitter as a means of surveillance for public health crises, and themes found in this research could aid in specifying those groups or populations at risk on Twitter. As such, we plan to build automatic detection algorithms to identify these unique vaping users to connect them with a digital intervention in the future.

Indexed as

Electronic Nicotine Delivery SystemsLung InjurySocial MediaVapingAdolescentDisease OutbreaksHumansOutcome Assessment, Health CarePublic OpinionUnited StatesContent analysise-cigaretteEVALIPublic health surveillanceSentiment analysisSocial mediaText miningTobacco useTweetsTwitterVapingYouth

Identifiers

PMID34592539
PMCPMC8497442

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

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.