Commentary on Conde <i>et al</i>.: Addressing evidence gaps on the impact of vaping among young people
Bibliographic record
Abstract
Repetition of poor-quality studies assessing vaping as a ‘gateway’ into smoking among young people (aged < 30 years) may fuel vaping misconceptions and highly restrictive policies. Little is known about young peoples’ vaping for smoking prevention/cessation. Addressing this gap is critical, because the earlier someone stops smoking the better their health outcomes. In their recent article, Conde et al. [1] introduce the concept of interactive evidence and gap maps (EGMs) to assess vaping (e-cigarettes). In health research, repetition of poor-quality studies is, unfortunately, common, and Conde et al. demonstrate the utility of stepping back to identify gaps and what is needed to fill them. Conde et al. map the evidence exploring the relationship between vaping and subsequent smoking among young people (aged < 30 years). They found that the evidence to date clusters around vaping and subsequent initiation of smoking or current smoking (i.e. ‘gateway hypothesis’), with little attention given to the harm reduction potential of vaping among young people, particularly those from disadvantaged groups. They also found that most studies were from a few high-income countries. Of the 134 studies mapped, 106 assessed vaping as an exposure and current, or initiation of, smoking as an outcome. Reviews previously published in Addiction [2] and elsewhere [3, 4] have discussed the limitations of such ‘gateway’ studies, including inadequate adjustment for confounders, reliance upon self-report measures of infrequent use (e.g. ever use) without biochemical verification and high attrition. These limitations mean it is difficult to establish meaningful associations or causality. Evidence also suggests that the association between starting vaping and starting smoking works both ways [5] and that both behaviours share genetic aetiology [6], which is more consistent with a common liability rather than a causal association. Rather than more studies in this area, researchers could focus their attention elsewhere. Vaping poses only a fraction of the health harms of smoking, and there is now a substantial evidence base for vaping for tobacco harm reduction among adults [7]. Vaping nicotine can help adults to quit or reduce their smoking [8], and this effect is greater among adults with no initial plans to quit smoking [9]. Qualitative work also suggests some ‘accidental quitting’ or ‘sliding’ into tobacco abstinence among adults who try vaping [10]. Young adults have historically underutilized evidence-based cessation treatments for smoking [11], and quit rates are low among this age group [12]. However, at the population level, since disposable vapes have come onto the market in Great Britain smoking declines have been most pronounced among young adults, a group with the largest increases in vaping [13]. Vaping could therefore be a ‘gateway out’ of smoking although, as Conde et al. show, there are few studies specifically assessing vaping for smoking cessation, reduction or prevention among young adults. Addressing this gap is crucial, because the earlier someone stops smoking the better their health outcomes [14]. Conde et al. also highlight an evidence gap with respect to vaping among young people from more disadvantaged groups of society. Tobacco smoking is a leading cause of health inequalities, causing at least 50% of the difference in life expectancy between the least and most affluent in the United Kingdom, Canada and the United States [15]. In the United Kingdom, smoking is more common among people with lower levels of education and income [16] and those with mental ill-health [17]. Research assessing vaping for tobacco harm reduction among these specific groups of young people is therefore critical for intervening early and reducing life-long inequalities and ill-health. Conde et al. further highlight that the vast majority of research assessing associations between vaping and smoking among young adults is from the United States, the United Kingdom and Canada. Several countries (e.g. India, Singapore, Australia) have restricted nicotine-containing e-cigarettes to a greater extent than cigarettes or have banned vaping entirely, with youth vaping and ‘gateway’ concerns often underpinning such decisions. However, care must be taken not to generalize findings globally from a handful of western high-income countries, particularly given different product markets, cultures and historical approaches to tobacco control and harm reduction. Addressing research gaps with respect to vaping among young people is important because it might help to tackle pervasive misperceptions. Only 16% of adults who smoke in England accurately believe that vaping is less harmful than smoking, down from 41% in 2014 [18]. Despite their limitations [2], ‘gateway’ studies are often reported in the media as evidence that vaping can increase smoking among young people, potentially fuelling broader misconceptions that could deter people who smoke from switching to vaping [7]. As someone who published the first study in Great Britain assessing the association between trying vaping and trying smoking, subject to the limitations noted above and also finding that the association worked both ways [5], I have seen selective media reporting first-hand [19]. High-quality research among young people (particularly from disadvantaged groups) that assesses vaping for smoking prevention and cessation may update the narrative around vaping among this age group, with implications for equity across the life-span. K.E. is the recipient of Fellowship funding from the Society for the Study of Addiction (SSA). No other conflicts of interest to declare. No data described.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.155 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.013 | 0.005 |
| Research integrity | 0.062 | 0.063 |
| Insufficient payload (model declined to judge) | 0.015 | 0.012 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".