Evidence map›Paper›PMID 33209101›Full record

ArticleTobacco induced diseases2020

E-cigarette use behaviors and device characteristics of daily exclusive e-cigarette users in Maryland: Implications for product toxicity.

Angela Aherrera, Atul Aravindakshan, Stephanie Jarmul, Pablo Olmedo, Rui Chen, Joanna E Cohen, Ana Navas-Acien, Ana M Rule

Abstract read
In one paragraph

Article in Tobacco induced diseases, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 2 pooled it
–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

16 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
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  13. A Machine Learning Approach to Identify Predictors of Frequent Vaping and Vulnerable Californian Youth Subgroups.Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco · 2022
    Article
  14. Electronic Cigarette Terminology: Where Does One Generation End and the Next Begin?Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco · 2022
    Article
  15. Review
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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.

Angela AherreraDepartment of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, United States.
Atul AravindakshanDepartment of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, United States.
Stephanie JarmulDepartment of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, United States.
Pablo OlmedoDepartment of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, United States.
Rui ChenDepartment of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, United States.
Joanna E CohenDepartment of Health, Behavior and Society, Johns Hopkins Bloomberg School of Public Health, Baltimore, United States.
Ana Navas-AcienDepartment of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, United States.
Ana M RuleDepartment of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, United States.

Funding

Multidisciplinary Training Program in Pediatric PulmonaryT32HL072748 · NHLBI · JOHNS HOPKINS UNIVERSITY · PI Joseph Michael Collaco · 2003 to 2026
$6.1M
NHLBI NIH HHS T32 HL072748
6 · The paper itself

Abstract

introductionFew studies to date have characterized daily exclusive e-cigarette users, device characteristics, and use behaviors. This study describes daily e-cigarette user characteristics, and assesses the association between user behaviors and demographics.

methodsFrom 2015-2017, 100 daily exclusive e-cigarette users and 50 non-users were recruited in Maryland, USA. Sociodemographic characteristics, health status, e-cigarette/tobacco use behaviors, device characteristics, and reasons for e-cigarette use were collected by interview. Chi-squared tests (categorical variables), Student's t-test (continuous variables), and linear regressions were used to assess relationships between variables.

resultsMost daily exclusive e-cigarette users were men, White, former smokers, used MODs/tanks, and vaped on average 365 puffs/day (SD: 720). A third of users first vaped within 5 minutes of waking in the morning, and 56% vaped throughout the day. E-liquid consumption ranged from 5-240 mL/week (median: 32.5), with nicotine concentration 0-24 mg/mL (median: 3). E-cigarette users were more likely to report wheezing/whistling and hypertension than controls, although the finding was not statistically significant after adjustment. Less than half planned to quit vaping.

conclusionsDaily e-cigarette users between 2015-2017 most commonly vaped MOD/tank devices. Being male and of lower education was associated with higher usage. Daily users with no intention to quit may be at risk for increased exposure to emissions from e-cigarettes that include inorganic (metals) and organic (e.g. acrolein, formaldehyde) compounds with known toxic effects, particularly to the lung. Further research is needed to characterize the long-term health effects of daily e-cigarette use.

Indexed as

device characteristicse-cigaretteshealth implicationsnicotineuse patterns and behaviors

Identifiers

PMID33209101
PMCPMC7668279

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.