This study provides important insights into the predictors of e-cigarette use among individuals with asthma in Scotland. The adjusted analysis revealed that younger age, residence in more socioeconomically deprived areas, and a history of cigarette smoking were the strongest predictors of current e-cigarette use in this population. Notably, age demonstrated an inverse relationship with use, with each increasing age category associated with a 23% reduction in the odds of using e-cigarettes. This age gradient aligns with broader population-level trends, suggesting that younger respondents are more likely to adopt e-cigarettes [17]. Socioeconomic deprivation also emerged as a significant correlate, with those living in more deprived areas showing higher odds of current e-cigarette use. This may reflect both greater tobacco-related harm and the potential appeal of e-cigarettes as a perceived harm reduction tool in resource-constrained settings where access to cessation support is limited [17, 23]. These patterns are consistent with frameworks on the social determinants of health, which emphasise how structural disadvantage shapes access to cessation resources and health behaviours [23]. They also align with behavioural models that highlight how perceived risk, opportunity, and access can influence uptake of alternative nicotine products [17, 23].

The most notable finding was the association between smoking status and e-cigarette use. Compared to never smokers, former smokers had 32 times the odds of using e-cigarettes, and current smokers had nearly 39 times the odds, highlighting smoking history as the dominant correlate of use. This pattern suggests that, among individuals with asthma, current e-cigarette use is concentrated among individuals with a current or past history of tobacco smoking. These findings support previous literature reporting that e-cigarettes are disproportionately used by people trying to quit smoking [24, 25], including those with asthma who may experience worsening respiratory outcomes from continued cigarette use [14, 15]. Interestingly, variables such as sex, educational attainment, and self-rated health were not significantly associated with e-cigarette use after adjusting for other covariates, indicating that smoking status and deprivation levels may overshadow the effects of these factors in this clinical group. These findings were robust to sensitivity analysis excluding dual users, which allowed us to focus specifically on exclusive e-cigarette users. In this restricted sample, the association between former smoking and exclusive e-cigarette use remained strong, reinforcing the interpretation that e-cigarettes may be adopted as an alternative to combustible tobacco following cessation. However, the motivation for continued use cannot be determined from these data.

The adjusted predicted probabilities provide further insight into how e-cigarette use varies across smoking status and age groups among individuals with asthma. The likelihood of current use was substantially higher among current and former smokers than among never smokers, with this pattern evident across all age groups. However, predicted use declined progressively with increasing age, suggesting that younger individuals, particularly those with a history of smoking, are more inclined toward e-cigarette use. For instance, among current smokers aged 16–24, nearly 3 in 10 were predicted to be current e-cigarette users, compared to just 1 in 100 among never smokers of the same age. This may reflect generational differences in attitudes toward e-cigarettes, including greater acceptance, perceived reduced harm, and familiarity with newer nicotine delivery technologies among younger people. In contrast, older individuals may be less receptive to e-cigarette use [26], potentially due to more established cessation behaviours, nicotine dependence level, misperceptions, or a reluctance to adopt unfamiliar alternatives.

Interestingly, the difference in predicted use between current and former smokers narrowed with age, possibly indicating a convergence in behaviour over time, where former smokers become less likely to maintain e-cigarette use as they grow older. These trends suggest that both smoking history and life stage play important roles in shaping e-cigarette use patterns in this clinical population. These trends suggest that both smoking history and life stage play important roles in shaping e-cigarette use patterns in this clinical population. Although the cross-sectional design precludes establishing the temporal sequence of smoking and e-cigarette use, it is unlikely that vaping preceded smoking in this population. Given the clinical risks of tobacco use in asthma and the very low prevalence of e-cigarette use among never smokers, it is more plausible that e-cigarettes are being adopted following cigarette use. The high uptake among former smokers further supports this interpretation, suggesting that some individuals may continue using e-cigarettes after quitting smoking.

These findings have several implications for public health policy and clinical practice. First, interventions to support smoking cessation in people with asthma should be sensitive to age and socioeconomic context. Younger people and those from more deprived backgrounds were more likely to report current e-cigarette use, which may reflect broader structural factors such as differential exposure to nicotine marketing, disparities in access to cessation support, and social patterning of tobacco-related behaviours. Second, given the strong association between smoking history and e-cigarette use, clinicians may consider engaging in informed, non-judgemental discussions with asthma patients who smoke or have smoked—addressing available cessation support, the potential risks and benefits of switching to e-cigarettes, and the importance of eventually quitting nicotine use altogether.

Some evidence suggests that switching from combustible tobacco to e-cigarettes may improve asthma control and quality of life without worsening lung function in the short term [27]. However, caution is warranted. While studies have reported harm reduction potential of e-cigarettes [28,29,30,31,32], their long-term safety in asthma populations remains uncertain. Therefore, public health messaging should remain balanced, acknowledging emerging evidence for smokers with asthma who cannot quit, while continuing to discourage uptake among never smokers and reinforcing the importance of evidence-based cessation strategies [33, 34]. Future research should explore the longitudinal impacts of e-cigarette use in asthma populations, particularly across different age and deprivation groups, to inform equitable and effective policy development.

A key strength of this study is its use of a large, nationally representative dataset spanning multiple years. By pooling data from four waves of the Scottish Health Survey (2017–2021) and applying rigorous statistical adjustment, the study offers robust and temporally stable estimates of associations between sociodemographic factors, smoking status, and current e-cigarette use. The stratified sampling design and high-quality data collection methods reduce the risk of selection and information bias, while the use of marginal effects to generate adjusted predicted probabilities provides a more intuitive understanding of usage patterns across age and smoking history. Additionally, the focus on individuals with asthma, a clinical subgroup often underrepresented in e-cigarette research, fills a critical evidence gap and offers valuable insights for targeted policy and clinical practice. Furthermore, diagnostic checks confirmed the robustness of the multivariable model, with no evidence of problematic multicollinearity across included predictors.

However, the study has several limitations. Its cross-sectional design precludes causal inference and limits the ability to determine temporal relationships between the predictors and e-cigarette use. Self-reported measures of asthma, smoking, and e-cigarette use are subject to recall and social desirability bias, which may affect the accuracy of responses. Furthermore, the analysis did not capture the frequency or intensity of e-cigarette use, dual use patterns, or motivations for vaping, which are important for understanding health implications. Finally, the exclusion of data from 2020, due to concerns about comparability, slightly reduced the overall sample size but does not impact the study’s primary objective of identifying predictors of e-cigarette use. In addition, the extremely low prevalence of e-cigarette use among never smokers may have contributed to inflated odds ratio estimates for former and current smokers due to sparse data bias. While the model was well specified and no separation was detected, these estimates should be interpreted with caution and in the context of wide confidence intervals. Additionally, although the dataset is nationally representative, selection bias may still be present due to differential non-response or exclusion of individuals without complete data, which could limit the generalisability of findings. As this is an exploratory analysis using secondary data, we cannot rule out potential residual confounding.