Tobacco Burden Among Older Adults
While the prevalence of combusted cigarette (CC) smoking among all other age groups of U.S. adults decreased between 2005 and 2020, the prevalence for those age ≥55 years remained stagnant.1,2 Indeed, 2021 estimates from the National Health Interview Survey (NHIS) suggest current smoking is as prevalent among adults ages ≥65 years (9.4%; 95% Confidence interval = 8.4, 10.5) as it is among adults ages 18–34 years (9.6%; 95% CI = 8.4, 11.0).3 Smoking in older adults carries burdensome health consequences: Among adults ≥65 years who report current cigarette smoking, 25.7% report smoking-related cancer and 46.9% report another smoking-related chronic disease.4 Furthermore, according to 2017 NHIS data, past-year quit attempts are less prevalent among adults 45–64 years (49.6%) and ≥65 years (47.2%) compared to adults 25–44 years (59.8%) as is the prevalence of quit interest (53.7%, 68.7%, and 72.7%, respectively).5 Older adults struggle with smoking cessation: Only 5% of adults ≥65 years who smoke successfully quit in the past year.4 The tobacco industry has played a role in these rates; they aggressively marketed “light” and “low-tar” cigarettes towards older adults while discouraging quitting and providing cigarettes at senior clubs and nursing homes.6
In 2013, Williams et al7 compiled commonly used criteria for identifying smoking populations that should be considered tobacco use disparities groups. Collectively, the above data indicate older adults meet these criteria7 as they have (1) stagnant smoking prevalence, (2) increased tobacco-related disease burden, (3) low quit prevalence, and (4) have been targeted by the tobacco industry. However, relatively little attention has been paid to this at-risk smoking population, and the U.S. Food and Drug Administration’s Center for Tobacco Products currently does not recognize older adults as a priority smoking population.8 Indeed, in a recent commentary, McAfee et al9 argue that public health officials are “ignoring our elders”.
Potential of ECs and Other Non-combusted Nicotine Products to Reduce Tobacco Burden Among Older Adults Uninterested in Quitting
Although complete cessation from all nicotine products is a primary public health goal, for the reasons cited above, it may not be achievable for many older adults who smoke. As such, harm reduction approaches that move people away from combusted products and the associated exposure to toxic smoke and towards non-combusted products such as e-cigarettes (EC) may be beneficial for older adults who are unable or unwilling to stop using nicotine. However, current data on EC use trends suggest less interest in and use of ECs among older adults. As of 2021, EC use prevalence is 2.9% (95% CI = 2.3, 3.5) and 0.9% (95% CI = 0.6, 1.2) among people 50–64 years and ≥65 years compared to 10%, among people aged 18–34 years.3 While not speaking directly to age differences in switching to ECs, this lack of adoption of ECs among older adults may contribute to stagnant CC smoking rates in that population. Consistent with this, a SimSmoke modeling study estimated no 6-year reduction in smoking prevalence attributable to EC use for adults over 45 but a 43–53% and 11–20% decreased prevalence of smoking attributable to EC use among adults aged 18–24 years and 25–44 years, respectively.10
Harm Misperceptions Among Older Adults Who Smoke
Misperceptions regarding the risks of nicotine may contribute to low uptake of ECs among older adults who smoke. A recent study using data from the Population Assessment of Tobacco and Health Survey published in Nicotine & Tobacco Research assessed nicotine misperceptions among adults who smoke.11 The authors found that 61.2% of adults incorrectly believed nicotine causes cancer, and people over 55 years were significantly more likely to have this misperception (PR: 2.54 for ages 55–64 years; PR: 2.53 for ages 65–74 years; PR: 2.31 for ages ≥75 years) than people ages 18–24 years. This nicotine misperception might make older adults more likely to think that CCs and ECs are equally harmful and thus switching completely from CCs to ECs will not change health risks.
Health communication approaches can be used to correct misperceptions about nicotine as well as the relative harms of CCs versus ECs, leading to harm reduction. However, most current state and federal tobacco control campaigns target youth or young adults.9 It is essential to focus some future tobacco control communications on the beliefs and values that are important and relevant to older adults, such as prolonging independence and spending more life years with their families. We propose that this is best informed by a comprehensive approach to health communication based on the Health Belief Model (HBM),12–14 which posits that a person’s health behavior is shaped by the following six constructs: Perceived susceptibility to negative outcomes, perceived severity of those negative outcomes, perceived benefits of the behavior, perceived barriers to adopting that behavior, cues to take action, and self-efficacy in the ability to adopt the behavior.
Use of the Health Belief Model for Harm Reduction Among Older Adults Who Smoke
In our adapted HBM (Figure 1), older adults may be less likely to switch completely from CCs to ECs due to beliefs and misperceptions regarding the risks of continued CC smoking, the risks of nicotine use, and the relative risk of CCs, ECs, and other tobacco and/or nicotine-containing products. For instance, thinking “the damage (from smoking) is already done” is a common maladaptive belief held by older adults that can be a barrier to quitting smoking,15 and could also impede a complete switch to ECs. Moreover, beliefs that nicotine is the cancer-causing agent in cigarettes may deter people from switching completely to ECs.11 Beliefs about the harms of ECs and perceptions that ECs are only for younger people may further dissuade older adults from switching. Finally, low self-efficacy (eg, “one can do little to change”) may lead to a lack of interest in switching completely to ECs. In addition to these negative factors, our HBM suggests that age-relevant education about the potential benefits of switching completely from CCs to ECs can modify beliefs and lead to increased intentions to switch.
Figure 1.
A Novel Health Belief Model of completely switching from combusted cigarettes to e-cigarettes among older adults who smoke.
Future Directions in Health Communications for Older Adults Who Smoke
More research is needed among older adults who smoke to better understand their knowledge, beliefs, and perceptions regarding nicotine-containing products and potential barriers to switching completely to ECs. This will allow for the development of targeted health communications, as have been used extensively in the prevention of smoking initiation among adolescents by using messaging framed around relevant and important beliefs.16–18 However, health communication interventions that work for young people may not be as successful in older adults.19 Once tailored persuasive messaging is developed according to the unique health beliefs of older adults who smoke, research can evaluate the effects of messaging on perceptions and substitutability of ECs for CCs in this population, including research utilizing behavioral economic methods.20 Other future directions include EC switching studies in older adults to further evaluate barriers and examine age-relevant health outcomes (eg, cognition, endothelial function) and analyzing and addressing the unique considerations for different modified risk tobacco products, such as very low nicotine cigarettes, heated tobacco products, and snus.
While the HBM emphasizes variables at the individual level that can be targeted via health communication, this model may need to be supplemented when further considering intervention development for behavior change by accounting for additional population-level factors. Older adults initiated cigarette use in smoking normative times—that is, before the 1964 Surgeon General’s Report, indoor smoking laws, or media bans—and may be resistant to completely switching from CCs to ECs. In addition to their different worldview on smoking’s place in society, other unique considerations in this population include being more likely to have multiple complex medical illnesses, decreased familiarity with evolving products and novel technology (which could hinder the use of many harm-reducing products like ECs), a social context where peers use CCs but not ECs, and misperceptions due to media coverage of adverse EC events (eg, EC and vaping associated lung injury, ECs causing fires, etc.). Continued research is necessary to identify strategies for completely switching to ECs in older adults who are unwilling or unable to quit using nicotine, which can inform age-relevant interventions, and ultimately, improved health for this tobacco use disparities group.
Acknowledgments
The authors wish to acknowledge Dr. Patrick Smith for his feedback on components of the adapted Health Belief Model.
Contributor Information
Dana Rubenstein, Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC 27705, USA.
Rachel L Denlinger-Apte, Department of Social Sciences and Health Policy, Division of Public Health Sciences, Wake Forest University School of Medicine, Winston-Salem, NC 27157, USA.
Jennifer Cornacchione Ross, Department of Social Sciences and Health Policy, Division of Public Health Sciences, Wake Forest University School of Medicine, Winston-Salem, NC 27157, USA.
F Joseph McClernon, Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC 27705, USA.
Funding
Research reported in this publication was supported by the National Center For Advancing Translational Sciences of the National Institutes of Health under Award Number TL1 TR002555. This work was also funded by R03CA252767 (RDA). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Declaration of Interests
No conflicts were declared.
Data Availability
No data were analyzed.
References
- 1. Jamal A, King BA, Neff LJ, et al. Current cigarette smoking among adults—United States, 2005–2015. Morb Mortal Wkly Rep. 2016;65(44):1205–1211. [DOI] [PubMed] [Google Scholar]
- 2. Gaffney A, Himmelstein DU, Woolhandler S.. Smoking prevalence during the COVID-19 pandemic in the United States. Ann Am Thorac Soc. 2022;19(6):1065–1068. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3. Control CfD. National Health Interview Study: Biannual Early Release tables, Centers for Disease Control. 2022. [Google Scholar]
- 4. Henley SJ, Asman K, Momin B, et al. Smoking cessation behaviors among older U.S. adults. Prev Med Rep. 2019;16:100978. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. General OotUSS. Smoking Cessation: A Report of the Surgeon General. Washington, DC: U.S. Department of Health and Human Services; 2020. [PubMed] [Google Scholar]
- 6. Cataldo JK, Malone RE.. False promises: the tobacco industry, “low tar” cigarettes, and older smokers. J Am Geriatr Soc. 2008;56(9):1716–1723. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7. Williams JM, Steinberg ML, Griffiths KG, Cooperman N.. Smokers with behavioral health comorbidity should be designated a tobacco use disparity group. Am J Public Health. 2013;103(9):1549–1555. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8. U.S. Food and Drug Administration. Research Priorities. FDA Web site. 2019. https://www.fda.gov/tobacco-products/research/research-priorities [Google Scholar]
- 9. McAfee T, Malone RE, Cataldo J.. Ignoring our elders: tobacco control’s forgotten health equity issue. Tob Control. 2021;30(5):479–480. [DOI] [PubMed] [Google Scholar]
- 10. Levy DT, Sánchez-Romero LM, Travis N, et al. US nicotine vaping product simsmoke simulation model: the effect of vaping and tobacco control policies on smoking prevalence and smoking-attributable deaths. Int J Environ Res Public Health. 2021;18(9):4876. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. Weiger C, Moran MB, Kennedy RD, Limaye R, Cohen J.. Beliefs and characteristics associated with believing nicotine causes cancer: a descriptive analysis to inform corrective message content and priority audiences. Nicotine Tob Res. 2022;24(8):1264-1272. doi:10.1093/ntr/ntac060 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12. Glanz K, Rimer BK, Viswanath K. Health behavior and health education: Theory, research, and practice, 4th ed. San Francisco, CA, US: Jossey-Bass; 2008. [Google Scholar]
- 13. Carpenter CJ. A meta-analysis of the effectiveness of health belief model variables in predicting behavior. Health Commun. 2010;25(8):661–669. [DOI] [PubMed] [Google Scholar]
- 14. Janz NK, Becker MH.. The Health Belief Model: a decade later. Health Educ Q. 1984;11(1):1–47. [DOI] [PubMed] [Google Scholar]
- 15. Kerr S, Watson H, Tolson D, Lough M, Brown M.. Smoking after the age of 65 years: a qualitative exploration of older current and former smokers’ views on smoking, stopping smoking, and smoking cessation resources and services. Health Soc Care Community. 2006;14(6):572–582. [DOI] [PubMed] [Google Scholar]
- 16. Brennan E, Gibson LA, Kybert-Momjian A, Liu J, Hornik RC.. Promising themes for antismoking campaigns targeting youth and young adults. Tob Regul Sci. 2017;3(1):29–46. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17. Sangalang A, Volinsky AC, Liu J, et al. Identifying potential campaign themes to prevent youth initiation of e-cigarettes. Am J Prev Med. 2019;56(2 Suppl 1):S65–S75. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18. Vallone D, Cantrell J, Bennett M, et al. Evidence of the impact of the truth finishit campaign. Nicotine Tob Res. 2018;20(5):543–551. [DOI] [PubMed] [Google Scholar]
- 19. Keating DM. Extending efforts to move cigarette and e-cigarette beliefs: message exposure and belief structures. J Health Commun. 2018;23(10–11):956–966. [DOI] [PubMed] [Google Scholar]
- 20. Tidey JW, Cassidy RN, Miller ME, Smith TT.. Behavioral economic laboratory research in tobacco regulatory science. Tob Regul Sci. 2016;2(4):440–451. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
No data were analyzed.

