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Invited Commentary
Public Health
August 25, 2021

Ongoing Difficulty of Characterizing Nicotine Product Risks

Author Affiliations
  • 1Department of Environmental Medicine, New York University Langone School of Medicine, New York
JAMA Netw Open. 2021;4(8):e2119888. doi:10.1001/jamanetworkopen.2021.19888

Smoking rates in the US have reached historic lows, particularly among youths. Decades of antismoking campaigns and legislation restricting smoking in public spaces have not only made it logistically more difficult to smoke but also have cultivated a societal stigma surrounding smoking, a practice that had once epitomized glamour. This waning enthusiasm for smoking, while encouraging, has opened a niche for new products attempting to satisfy a nicotine addiction in consumers throughout the world. e-Cigarettes, which only entered the US market in 2007, have skyrocketed to become the most widely used nicotine product among youths.1 Indeed, this exponential adoption of e-cigarettes risks the development of lifelong nicotine addiction among a new generation of nonsmoking adolescents and young adults. To gauge the association between e-cigarettes and adolescent risk, Sun et al2 developed a novel metric to quantify product use: nicotine product days (NPDs). By applying product-specific risk weights to NPDs, the authors were able to compare the relative risk of e-cigarettes with that of other dominant nicotine products across 2 decades of National Youth Tobacco Survey data.

As the tobacco landscape continues to evolve, characterizing exposure to—and relative risk from—nicotine products, particularly e-cigarettes, has become increasingly difficult and nuanced. Unlike combustible cigarettes, which are typically consumed in a single setting, e-cigarette users can go minutes to hours between puffs until a need or opportunity presents itself. Furthermore, e-cigarettes are entirely customizable, from nicotine content and flavor to device power settings that influence the resultant aerosol volume. The sleek design of e-cigarettes, coupled with an absence of the noxious smell associated with smoking, has led to students vaping in places where cigarette smoking would normally be prohibited, including classrooms and school bathrooms.3 After nearly half a century of studying combustible cigarettes, a product with little variation in composition and consumption, the lack of e-cigarette uniformity has stymied the tobacco research community’s efforts to characterize vaping-specific constituents and consumer behaviors. Consequently, the scientific community has yet to arrive at a consensus on the health risks of e-cigarettes, both independently and in association with the now well-established risks from smoking.

When product use frequencies are held constant, NPDs allow for side-by-side comparisons of nicotine products, even when they differ in potential toxic effects. For example, if one wished to compare vaping with smoking, and cigarettes were assigned a higher weighted risk, one could calculate how many additional e-cigarette NPDs would be needed to equal the risk incurred from 1 year of daily cigarette smoking. Thus, NPDs provide a substantial improvement compared with other models, which often use product frequency as a proxy for risk (with the underlying assumption being that all nicotine products are equally toxic).

Although NPDs constitute a clever approach to quantifying nicotine product risk, their applicability may be restricted by certain underlying assumptions. For example, NPDs assign nicotine products into 1 of 3 categories: combustibles (ie, cigarettes, hookahs, and cigars), smokeless tobacco (ie, chewing tobacco, snuff, and dip), and e-cigarettes. At first glance, these designations seem reasonable. However, grouping products based solely on a superficial shared feature (eg, if the product is vaped) implies that all e-cigarettes pose equal risk. However, a growing body of literature now implicates multiple culprits—including constituent toxic effects, exposure frequency, and product-specific behaviors—that cumulatively contribute to the risk profile of a given nicotine product.

Consider, for example, e-liquids (the material that is vaporized and subsequently inhaled). Although most e-liquids contain nicotine, the type of nicotine can vary: some nicotine is protonated (colloquially referred to as nicotine salt), some is freebase. Protonated nicotine is more bioavailable than freebase nicotine and therefore more rapidly metabolized.4 As a result, people who vape nicotine salts may need to reach for their e-cigarette more often. A prevailing tenet of toxicology, “the dose makes the poison,” describes the relationship of most chemicals and their adverse effects, where increased exposure correlates with greater risk when all other variables are held constant. If this adage holds true for e-cigarettes, something as simple as nicotine formulation could be sufficient to augment risk. Thus, compensatory puffing to address a rapid decrease in blood nicotine could increase toxicant exposure.

Although defining the inherent risk of individual nicotine products is important, so too is understanding how consumption patterns are associated with risk (eg, changes in frequency to satisfy nicotine cravings). Models of nicotine exposure and/or risk often reflect isolated exposure scenarios rather than the more complex patterns of use commonly observed in nicotine-dependent populations. For example, most youths who consume nicotine use 2 or more distinct nicotine products (ie, they are dual users or polyusers). Incorporating polynicotine use into risk models is critical; not only can different nicotine products pose unique health risks, but addiction risk increases with the number of nicotine products used.5

Moreover, polynicotine dependence is inversely correlated with quit attempts.6 This finding suggests that polynicotine users could be amplifying the rate at which their exposures may be associated with product-related harms. This is particularly important when modeling polynicotine consumers who use both combustible cigarettes and e-cigarettes. Indeed, more severe health consequences have been observed in polyusers compared with those who solely smoke cigarettes.7 In fact, several studies have suggested a potential interaction between nicotine products.7-10

Currently, a comprehensive toxic effect profile for newer nicotine products, such as e-cigarettes, remains poorly characterized. To demonstrate the utility of their new metric to evaluate relative vaping risk, Sun et al2 calculated a range of e-cigarette NPDs based on several possible risk weightings (ranging from 0.1 to 1.0). One benefit associated with such a sliding scale is the ability to contextualize the risk of a particular nicotine product for a discrete end point. For example, the cancer risk from vaping may be lower than the increased susceptibility to infection among e-cigarette users. Thus, modifying NPD risk weightings would permit for the assessment of risk with specific nicotine products for any number of health outcomes (eg, respiratory, immunologic, or cardiovascular).

Current dogma in the tobacco research community holds that when it comes to nicotine products, cigarettes have always posed the greatest health risks and continue to do so. There is no question that cigarettes harm multiple organ systems, reduce life expectancy, and increase morbidity and mortality. However, our understanding of nicotine product risks continues to evolve. Increasingly, researchers are finding evidence of potential harms associated with new and emerging nicotine products that have never been associated with cigarettes.

Thus, it might be prudent to reframe how we think about nicotine product risk. Given that risk is associated with constituents, frequency, and exposure duration, NPDs are a useful template for mapping a product’s potential range of risk along a continuum. For example, the risk spectrum extremes (0.1 and 1.0) represent the minimum and maximum risks associated with a particular nicotine product. Perhaps the inclusion of a risk weight greater than 1.0 might also be helpful in reflecting any potentially exaggerated risks associated with polynicotine use.

An important caveat: risk models are most effectively honed when input data become more detailed and relevant. Many population-based tobacco surveys do not yet query factors important to toxic effects and risk, including within-day product frequency, nicotine dose and species, or cumulative lifetime product exposures. As surveys improve the granularity of data collected on nicotine product use and exposures, metrics such as NPDs can continue to be refined, which will ultimately serve to improve our understanding of the relative risks associated with new and emerging nicotine products.

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Article Information

Published: August 25, 2021. doi:10.1001/jamanetworkopen.2021.19888

Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2021 Karey E. JAMA Network Open.

Corresponding Author: Emma Karey, PhD, Department of Environmental Medicine, New York University Langone School of Medicine, 341 E 25th St, New York, NY 10010 (emma.karey@nyulangone.org).

Conflict of Interest Disclosures: None reported.

References
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Sun  R, Mendez  D, Warner  KE.  Trends in nicotine product use among US adolescents, 1999-2020.   JAMA Netw Open. 2021;4(8):e2118788. doi:10.1001/jamanetworkopen.2021.18788Google Scholar
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