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Article

Association Between Socioeconomic Status and E-Cigarette Use in Baltimore High Schools: Comparison of Girls and Boys

by
Payam Sheikhattari
1,
Rifath Ara Alam Barsha
2,
Chidubem Egboluche
1 and
Shervin Assari
3,*
1
School of Community Health and Policy, Morgan State University, Baltimore, MD 21251, USA
2
John D. Bower School of Population Health, University of Mississippi Medical Center, Jackson, MS 39216, USA
3
Department of Psychiatry, Charles R Drew University of Medicine and Science, Los Angeles, CA 90059, USA
*
Author to whom correspondence should be addressed.
Women 2025, 5(3), 33; https://doi.org/10.3390/women5030033
Submission received: 30 May 2025 / Revised: 28 July 2025 / Accepted: 14 August 2025 / Published: 17 September 2025
Versions Notes

Abstract

Background: Higher socioeconomic status (SES) is generally associated with lower engagement in health-risk behaviors, in part due to increased access to health information, preventive resources, and supportive environments. However, emerging evidence suggests that this protective pattern may not extend uniformly to all forms of substance use, including adolescent e-cigarette use, and may vary by gender. For instance, some studies have found higher rates of e-cigarette use among adolescents from higher SES backgrounds. Aim: This study examined whether the associations between family SES and tobacco use differ between girls and boys. We also explored whether these associations vary by age group. Methods: A cross-sectional survey was conducted among students (age 14–20) attending public high schools in Baltimore City. Family SES was assessed using three indicators: parental education, parental employment, and household income. Tobacco use was measured using self-reported past use of e-cigarettes and conventional cigarettes. Demographic covariates included age, sex, race/ethnicity, and household composition. Separate logistic regression models were estimated for each tobacco use outcome, adjusting for covariates. To examine subgroup differences, analyses were stratified by gender and age. Results: Higher parental education was associated with lower odds of e-cigarette use, but no SES indicators were significantly associated with conventional cigarette use. Subgroup analyses showed that the protective association of parental education against e-cigarette use was evident among girls but not boys and among older but not younger adolescents. Conclusions: These findings differ from previous studies that reported a positive association between SES and adolescent e-cigarette use. In this predominantly low-income, urban sample, higher parental education appeared to be protective for girls but not for boys. These results suggest that SES influences on tobacco use may be context- and subgroup-specific. Further research is needed to better understand how sociodemographic and contextual factors shape adolescent tobacco use behaviors.

1. Introduction

E-cigarette use has increased rapidly in recent years and now exceeds the prevalence of conventional cigarette smoking among youth populations [1]. While not risk-free, nicotine vapes (i.e., e-cigarettes) are generally considered to be substantially less harmful than combustible cigarettes—particularly for adults who smoke. This relative advantage is supported by differences in the chemical composition of e-cigarette vapor compared to cigarette smoke, endorsements by public health agencies such as the CDC for adult smokers unable to quit using other methods, and epidemiological evidence to date [2,3]. However, despite their potential as a harm reduction tool for adult smokers, e-cigarette use among adolescents has become a growing public health concern [4,5,6,7]. For example, previous studies have documented several health risks associated with e-cigarette use among adolescents [8,9,10]. Respiratory issues such as chronic cough, wheezing, and asthma exacerbations have been reported at higher rates among youth who vape compared to non-users [11,12]. In terms of developmental risks, e-cigarette use during adolescence—a critical period for brain maturation—has been linked to impaired cognitive function, reduced attention span, and increased vulnerability to mood disorders such as depression and anxiety [13,14]. Such potential risk is particularly concerning given that nicotine exposure during adolescence can disrupt the development of neural circuits involved in learning, memory, and impulse control [15]. Their popularity has been fueled by flavored products, sleek and concealable device designs, and targeted marketing—factors that contribute to early initiation and sustained use among high school students [16]. Thus, while e-cigarettes may pose fewer health risks than conventional cigarettes for adults, their widespread use among adolescents raises serious concerns due to potential developmental and behavioral harms [8,17].
In public health research, higher socioeconomic status (SES) is generally associated with reduced engagement in health-risk behaviors [18,19,20,21,22,23]. According to frameworks such as the social determinants of health and fundamental cause theory [24,25], greater socioeconomic resources typically provide individuals with improved access to health-promoting environments, enhanced capacity to avoid harmful exposures, and better outcomes overall. Based on these frameworks, adolescents from higher SES backgrounds are often expected to engage less in risky behaviors, including tobacco and e-cigarette use [26].
However, emerging evidence suggests that the relationship between SES and youth e-cigarette use is more complex than previously assumed. Several studies have identified a paradoxical pattern, where adolescents from socioeconomically advantaged households report higher rates of e-cigarette use compared to their less advantaged peers [23,27,28,29]. This challenges conventional expectations and implies that the protective effects of SES may not be consistent across all contexts or populations [30,31,32]. The dynamics of vaping behavior appear to be shaped not only by individual or household characteristics but also by broader social, economic, and environmental conditions.
Local context is particularly important in understanding adolescent health behaviors, especially within urban areas characterized by concentrated disadvantage [33]. Baltimore City provides a compelling case for examining such contextual effects. The city is marked by long-standing socioeconomic inequities, structural racism, and significant health disparities [34,35,36,37,38,39,40,41]. Many public high schools in Baltimore are situated in historically segregated neighborhoods and serve predominantly low-income Black youth [42,43,44,45]. These students often navigate a distinct set of environmental exposures, social norms, and stressors that may influence patterns of tobacco use differently than in more affluent or suburban settings. Despite heightened concern about youth vaping, little research has explored how family SES relates to e-cigarette use in this specific urban context.
Gender differences may also play a critical role in shaping how socioeconomic factors influence adolescent substance use. In general, boys are more likely than girls to engage in a range of risk behaviors, including tobacco and substance use, though these gaps have narrowed over time [46,47]. Research also suggests that the social and psychological pathways linking SES to health behaviors may differ by gender. For example, boys and girls may respond differently to stressors related to socioeconomic disadvantage or may be influenced by distinct peer norms, family expectations, or coping strategies [48,49]. Some studies have found that the protective effects of parental education and income are stronger for girls, while others report greater SES-related disparities among boys [50,51]. These gendered patterns highlight the importance of disaggregated analyses to uncover subgroup-specific risks and protective factors. Yet, few studies have examined whether the association between family SES and e-cigarette use differs for girls and boys, particularly in urban, low-income settings.

2. Study Objective

This study examined whether the associations between family socioeconomic status (SES) and the use of e-cigarettes and conventional cigarettes differed between girls and boys among high school students in Baltimore, MD, USA. We hypothesized that higher SES may not offer uniform protection against tobacco use for boys and girls in this urban context, and that these associations might differ from patterns observed in national or higher-income samples. Understanding such local and subgroup-specific dynamics is essential for developing more effective and contextually tailored prevention strategies for urban youth. We also explored whether these associations varied by age group.

3. Methods

This was a cross-sectional survey of high school students in Baltimore City, MD, USA, in 2024 and 2025.

3.1. Participants and Procedure

Participants were high school students enrolled in public schools in Baltimore City, MD, USA. Data were collected through an anonymous, self-administered survey distributed during lunch periods and classroom visits. The survey gathered information on demographic characteristics and tobacco use behaviors. All public high schools in Baltimore City were contacted and invited to participate in the study. Schools that accepted the invitation were included in the survey. Within participating schools, students who expressed interest and returned signed parental consent forms were enrolled. Parental consent was obtained prior to survey administration, ensuring that parents were fully informed about their child’s participation.

3.2. Measures

Socioeconomic Status (SES): SES was assessed using self-reported parental education, parental employment status, and number of parents in the household. Participants were asked what the highest level of education is completed by their Parent/Guardian 1 and Parent/Guardian 2. We coded it as 1 = Less than high school diploma; 2 = High school graduation; 3 = Some college; 4 = College graduation; 5 = Graduate degree or higher. Participants were asked about the current employment status of your Parent/Guardian 1 and Parent/Guardian 2. The variable used a dichotomous measure (0 = No; 1 = Yes). Number of parents in the household was measured as a proxy of family structure. This variable was coded as a dichotomous variable 1 = two parent household and 0 = any other condition.
Demographic Confounders: Age, sex/gender, and race were collected as covariates.
Tobacco Use: Participants reported past-30-day use of both e-cigarettes and conventional cigarettes. They were asked, “During the past 30 days, on how many days did you use electronic cigarettes?” Those who responded “0 days” were coded as 0 = “No,” and all other responses were coded as 1 = “Yes.” Similarly, participants were asked, “During the past 30 days, on how many days did you smoke a cigarette?” Those who responded “0 days” were coded as 0 = “No,” and all other responses were coded as 1 = “Yes”.

3.3. Statistical Analysis

All analyses were conducted using Stata 19.0 [52,53,54,55]. Logistic regression models were used to assess the associations between SES indicators (household income, parental education, and employment status) and both e-cigarette and conventional cigarette use. Models were adjusted for demographic variables, including age, sex/gender, race, and family structure. Odds ratios (ORs) and 95% confidence intervals (CIs) were reported. We also ran models stratified by sex/gender and age. A p-value of <0.05 was considered statistically significant.

4. Results

Table 1 presents the descriptive characteristics of the study sample (n = 603). The mean age of participants was 16.1 years (SD = 1.5). Age ranged from 14 to 20 years. The majority were female (53.7%) and identified as Black (88.1%). Approximately 9.8% of students reported current e-cigarette use, while 6.1% reported current use of conventional cigarettes. About 85.4% of the students had at least one parent who was employed, and only 37.2% reported living in a two-parent household.
Among the 603 participants, the majority (n = 525, 87.1%) were non-users of both e-cigarettes and conventional cigarettes. Dual use of both products was reported by 3.0% (n = 18) of participants, while 6.8% (n = 41) reported exclusive use of e-cigarettes, and 3.1% (n = 19) reported exclusive use of conventional cigarettes.
Table 2 presents the bivariate correlations among the study variables. Current conventional cigarette use was significantly positively correlated with current e-cigarette use (r = 0.33, p < 0.001). Parent or guardian’s educational attainment (r = −0.12, p < 0.01) and living in a two-parent household (r = −0.08, p < 0.05) were significantly negatively correlated with current e-cigarette use. No other variables showed a significant correlation with current e-cigarette use.
The results of the unadjusted and adjusted logistic regression analyses for current e-cigarette use are presented in Table 3. Higher parental education was significantly associated with the lower odds of current e-cigarette use in both the unadjusted and adjusted models. In the adjusted model, compared to youth whose parents had less than a high school diploma, those whose parents had some college education had 64% lower odds of current e-cigarette use (OR = 0.36, p < 0.05). Similarly, youth with parents who had completed college had 80% lower odds (OR = 0.20, p < 0.05), and those whose parents held a graduate degree or higher had 67% lower odds (OR = 0.33, p < 0.05). No statistically significant association was found between other variables and current e-cigarette use.
The results of the unadjusted and adjusted logistic regression analyses for current conventional cigarette use are presented in Table 4. None of the variables showed significant association with current conventional cigarette use.
Table 5 presents the unadjusted and adjusted logistic regression results for current e-cigarette use, stratified by gender. Among female participants, having a parent or guardian with a graduate degree or higher was significantly associated with lower odds of e-cigarette use in both the unadjusted (OR = 0.24, p < 0.05) and adjusted models (OR = 0.22, p < 0.05). Other levels of parental education showed similar trends toward lower odds of use, though these associations were not statistically significant. Age, race, parent employment, and household structure were not significantly associated with e-cigarette use among females. Among male participants, none of the predictors were significantly associated with current e-cigarette use in either unadjusted or adjusted models.
Table 6 presents the unadjusted and adjusted logistic regression results for current e-cigarette use, stratified by age group (≤16 years and >16 years). Among participants aged over 16 years, compared to those whose parents had less than a high school diploma, students whose parents had a high school education (adjusted OR = 0.24, p < 0.05) or a college degree (adjusted OR = 0.10, p < 0.05) were significantly less likely to report current e-cigarette use. No significant associations were observed for gender, race, parent employment, or household structure in this age group. Among participants aged 16 years or younger, no variables showed statistically significant associations with e-cigarette use in the adjusted model.
The results of the unadjusted and adjusted logistic regression analyses for current conventional cigarette use stratified by gender are presented in Table 7. Among male participants, age was significantly associated with increased odds of cigarette use in the unadjusted model (OR = 2.52, p < 0.01) and remained significant after adjustment (OR = 2.50, p < 0.01). Among female participants, age was not significantly associated with cigarette use. No other variables showed statistically significant associations with cigarette use in either males or females in unadjusted and adjusted models.
Table 8 presents the unadjusted and adjusted logistic regression results for current conventional cigarette use, stratified by age group (≤16 and >16 years). Among participants aged 16 years or younger, male gender was significantly associated with lower odds of cigarette use in the adjusted model (OR = 0.27, p < 0.05). Additionally, Black participants in this age group had significantly lower odds of cigarette use compared to White participants (adjusted OR = 0.12, p < 0.05). Parent or guardian educational attainment at the high school level was significantly associated with higher odds of cigarette use (adjusted OR = 10.75, p < 0.05), although this estimate had a wide confidence interval (95% CI: 1.09–106.05). Parent employment showed a protective association in the unadjusted model (OR = 0.32, p < 0.05), but this association was no longer significant in the adjusted model. Among participants aged over 16 years, no statistically significant associations were found between cigarette use and study variables in either unadjusted or adjusted models.

5. Discussion

In this study, we compared female and male high school students in Baltimore City—a community shaped by a long history of structural racism, economic disadvantage, and persistent health inequities [24,41]—we found that higher parental education was associated with a lower likelihood of e-cigarette use. This finding aligns with broader literature on the social determinants of health, which consistently highlights parental education as a key protective factor linked to better health outcomes and reduced engagement in risk behaviors among youth [24,25,26]. Parental education likely reflects access to health-promoting resources, greater health literacy within the household, and increased capacity to buffer youth from environmental risks [25]. Within our sample, higher parental education appeared to function as a critical protective factor against adolescent e-cigarette use.
Interestingly, our findings differ from studies conducted in more affluent or suburban settings, where higher SES—particularly higher parental education and income—has sometimes been linked to increased youth vaping [2,23]. In those contexts, adolescents from higher SES backgrounds may have greater access to disposable income, encounter stronger peer influences, and be more exposed to pro-vaping marketing, all of which can make vaping appear socially acceptable or low-risk [30,31,32]. By contrast, our findings suggest that these associations are not universal and may vary significantly depending on the social and economic context.
Our findings contribute to a growing body of literature suggesting that gender may moderate the association between socioeconomic status (SES) and adolescent substance use. Consistent with prior research, boys, in general, are more likely than girls to engage in risk behaviors such as tobacco and substance use, although these gender gaps have narrowed in recent years [46,47]. However, the underlying mechanisms linking SES to substance use may differ by gender. For instance, boys and girls may experience and respond to socioeconomic disadvantage through different psychosocial pathways, including variations in peer influence, parental expectations, and coping behaviors [48,49]. Some studies suggest that the protective effects of parental education and income are more pronounced among girls, while others have found greater SES-related disparities among boys [50,51]. Our study adds to this literature by showing that in a predominantly low-income, urban sample, higher parental education appeared protective against e-cigarette use for girls but not boys. These findings underscore the need for disaggregated analyses by gender to better understand subgroup-specific patterns and to inform more targeted and equitable intervention strategies. Despite increasing concern about youth vaping, few studies have explored these gendered dynamics in low-resource urban settings such as Baltimore.
Baltimore City presents a unique setting, characterized by high levels of poverty, concentrated disadvantage, and entrenched racial and economic disparities [34,35,36,37,38,39,40,41]. In this urban environment, the influence of parental education may operate differently than in more privileged contexts. Among students from predominantly low-income families, higher parental education may signal household stability, stronger parental monitoring, and role modeling that discourages tobacco use. In such settings, even modest differences in educational attainment may have a sizable impact on youth behaviors, especially when other protective resources are limited.
We also observed heterogeneity in the protective effects of SES by age and gender. Specifically, higher parental education was more strongly associated with reduced e-cigarette use among female and older students. This pattern may reflect developmental and social differences in how adolescents respond to parental guidance and perceive health risks [56,57]. Older adolescents may have more mature cognitive capacities that help them process health information and make informed choices. Similarly, female students may be more responsive to parental monitoring or more cautious in their health-related decisions. These findings underscore the importance of considering intersectional subgroups when assessing the role of SES in youth substance use prevention.
Additionally, not all SES indicators were equally predictive of e-cigarette use. While higher parental education was associated with lower odds of use, other SES markers—such as parental employment status and family structure—did not show consistent associations. This supports the idea that SES is a multidimensional construct and that not all components exert the same influence on adolescent health behaviors [52,53,54,55]. Parental education may capture enduring advantages related to communication, parenting practices, and health modeling, which are not necessarily reflected in more transient measures like income or employment.
Our results also point to potential differences in the social patterning of tobacco products. While higher SES was protective against e-cigarette use, we did not observe a similar protective effect for conventional cigarette use. This is somewhat unexpected, as national data typically show lower rates of traditional tobacco use among youth from higher SES backgrounds. It is possible that in urban contexts like Baltimore, conventional smoking is shaped by broader environmental or cultural norms that override household-level SES influences. Alternatively, the stigma associated with cigarette smoking may now be more evenly distributed across socioeconomic groups, reducing the visibility of SES-based differences. These findings emphasize the importance of examining different tobacco products separately, rather than assuming uniform risk patterns.
We do not claim that our results are representative of, or generalizable to, the broader adolescent population in Baltimore. This study was not designed to capture the regional epidemiology of e-cigarette use, as our sampling approach was not systematic. Schools and students were selected based on convenience, which introduces the potential for selection (or participation) bias. Consequently, the socioeconomic and demographic characteristics of our sample may differ from those of the general adolescent population in the city. If such discrepancies are minimal, selection bias may be limited; however, in the more likely scenario that differences exist, the findings should be interpreted with caution.
One possible explanation for the protective association between higher SES and lower e-cigarette use observed in our study is that families with higher parental education or income may transmit different norms and expectations around health behaviors. Higher SES parents may be less likely to engage in risky behaviors themselves and may, directly or indirectly, communicate disapproval of such behaviors to their children. This influence may operate through perceived parental norms, where adolescents internalize their parents’ values, expectations, and attitudes about substance use. When youth perceive that their parents strongly disapprove of tobacco or e-cigarette use, they may be less likely to initiate these behaviors themselves. These socialization processes—shaped by parental modeling, communication, and monitoring—may be more pronounced in higher SES families, contributing to lower rates of e-cigarette use among their children.

5.1. Implications

These findings have implications for tobacco prevention efforts. While national campaigns often target low-SES youth based on elevated risk, our study suggests that bolstering parental education in high-poverty urban settings may be a particularly effective strategy. School-based interventions that engage families and promote educational attainment could provide indirect benefits for adolescent health, including lower e-cigarette use.

5.2. Limitations and Contributions

This study has several limitations. First, cross-sectional design limits our ability to draw causal conclusions. Second, all variables were based on self-reported data, which may be subject to recall bias and social desirability bias—particularly for sensitive behaviors such as tobacco use. Third, because probability sampling was not used and several variables were measured with single items, the reliability and precision of our estimates may be limited. As this was a small, school-based survey rather than a large-scale epidemiological study designed to account for sampling error, findings should be interpreted with caution. Additionally, the sample was restricted to public high school students in Baltimore City, which may limit the generalizability of results to other geographic or demographic populations. Finally, we did not include other potentially important SES-related factors, such as household income, wealth or neighborhood characteristics, which may also influence adolescent tobacco use. There is a possibility that various SES indicators would operate differently in their association for electronic cigarette of adolescents. Additionally, we did not assess the use of alternative nicotine delivery products—such as nicotine pouches or heated tobacco devices—which may account for a substantial portion (up to 20%) of nicotine consumption among adolescents in this age group. Finally, the study did not distinguish between occasional or experimental use and daily or near-daily use of either product, which may indicate habituation or nicotine dependence. Thus; a major limitation of the survey methodology is the lack of detail regarding patterns of use.
In addition, all the data presented in this study reflect current product use, without accounting for any prior use history. This lack of longitudinal or retrospective data limits our ability to distinguish between recent initiators, intermittent users, and long-term habitual users. Understanding patterns of initiation, cessation, relapse, and switching between products is critical for interpreting use behaviors and assessing associated risks. Without information on prior use, we cannot determine whether current use represents a continuation of past behavior or a new onset, nor can we evaluate potential trajectories of use over time. Future research should consider incorporating measures of product use history to more accurately characterize usage patterns and to support stronger inferences about behavioral transitions and risk profiles.
While the current design offers insights relevant to community-level public health practice, it may not fully meet the standards expected for academic epidemiological research. The study employed non-probability sampling, which may limit the generalizability of the findings to broader populations. Additionally, the measures used—although practical and accessible—were not drawn exclusively from widely validated psychological instruments, potentially limiting the reliability and comparability of the constructs assessed. Future research should consider using standardized and psychometrically validated scales to strengthen measurement rigor. Moreover, employing probability-based sampling methods and incorporating a more comprehensive set of variables—such as socioeconomic context, stress exposure, and protective factors—would provide a fuller understanding of the community’s health dynamics and allow for more robust epidemiological inference.
Despite these limitations, our study still offers valuable insights into patterns of e-cigarette use among adolescents in a predominantly low-income, urban school setting. While not intended to represent the broader Baltimore population, our findings highlight the importance of examining tobacco-related behaviors within specific community contexts that are often underrepresented in national surveys. By focusing on a sample of students from public high schools in Baltimore City, we contribute to a more nuanced understanding of how family SES may relate to e-cigarette use in historically underserved populations. These insights can inform locally relevant prevention and intervention strategies, even as we recognize the need for future studies with more rigorous sampling designs to validate and extend our findings.

5.3. Future Research

Future research should employ longitudinal data to compare girls and boys in order to better understand how the influence of SES on e-cigarette use unfolds over time and whether any causal relationships can be inferred. Comparative studies across diverse urban settings would also be valuable for assessing the consistency and generalizability of these findings. Additionally, qualitative or mixed-methods research could offer deeper insights into how parental education may differentially shape boys’ and girls’ perceptions, access, and decision-making related to tobacco use.

6. Conclusions

In contrast to studies showing higher e-cigarette use among high-SES youth, our findings suggest that in Baltimore City, higher parental education is linked to lower use in girls not boys. These results reinforce the importance of parental education as a key social determinant of youth health and underscore the role of context in shaping behavioral outcomes. Tailoring prevention efforts to local social realities will be essential for designing equitable and effective public health interventions.

Author Contributions

Conceptualization: S.A. and P.S.; analysis—first draft: C.E.; revision: S.A., P.S., R.A.A.B. and C.E.; supervision: P.S.; funding: P.S.; approval of the final draft: S.A., P.S., R.A.A.B., and C.E. All authors have read and agreed to the published version of the manuscript.

Funding

This research received financial support from the National Institute on Minority Health and Health Disparities (collaborative agreement U54MD013376, and grants R24MD000217 & R24MD002803. Shervin Assari is supported by funds provided by The Regents of the University of California, Tobacco-Related Diseases Research Program, Grant Number no T32IR5355. The opinions, findings, and conclusions herein are those of the authors and do not necessarily represent the funders. The funders were not involved in the study design; collection, analysis, or interpretation of data; the writing of this article; or the decision to submit it for publication.

Institutional Review Board Statement

This study was approved by the Institutional Review Board (IRB) at Morgan State University (MSU) and Baltimore City Public School (BCPS). Informed consent and assent were obtained from all participants as appropriate. Data were collected anonymously and maintained in a confidential manner. All analyses were conducted using fully de-identified data.

Data Availability Statement

Data are available upon request.

Acknowledgments

This research received financial support from the National Institute on Minority Health and Health Disparities (collaborative agreement U54MD013376, and grants R24MD000217 & R24MD002803. Assari is supported by Funds provided by The Regents of the University of California, Tobacco-Related Diseases Research Program, Grant Number no T32IR5355.

Conflicts of Interest

The authors declare no conflicts of interest.

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Table 1. Descriptive Statistics of the Study Sample.
Table 1. Descriptive Statistics of the Study Sample.
VariablesTotal (n = 603)
Current E-cigarette Use
No544 (90.2)
Yes59 (9.8)
Conventional Cigarette Use
No566 (93.9)
Yes37 (6.1)
Gender
Female324 (53.7)
Male260 (43.1)
Missing19 (3.2)
Race
Black531 (88.1)
White20 (3.3)
Others46 (7.6)
Missing6 (1.0)
Parent or Guardians Educational Attainment
Less than high school diploma74 (12.3)
High school graduation214 (35.5)
Some college129 (21.4)
College graduation78 (12.9)
Graduate degree or higher104 (17.2)
Missing4 (0.7)
Parent Employment
No85 (14.1)
Yes515 (85.4)
Missing3 (0.5)
Two-parent Household
No377 (62.5)
Yes224 (37.2)
Missing2 (0.3)
Mean (SD)
Age (Years)16.1 (1.5)
Table 2. Correlation Matrix of the Study Variables.
Table 2. Correlation Matrix of the Study Variables.
Variables1234567
1. Current E-cigarette Use (Yes)1.00
2. Current Conventional Cigarette Use (Yes)0.33 ***1.00
3. Age (Years)−0.040.081.00
4. Gender (Male)−0.06−0.030.09 *1.00
5. Parental Educational Attainment−0.12 **−0.08 *−0.080.031.00
6. Parent Employment (Yes)−0.01−0.05−0.13 **0.030.18 ***1.00
7. Two-parent Household (Yes)−0.08 *−0.04−0.070.16 ***0.18 ***0.17 ***1.00
Note. * p < 0.05, ** p < 0.01, *** p < 0.001.
Table 3. Logistic Regression Results for Current E-cigarette Use.
Table 3. Logistic Regression Results for Current E-cigarette Use.
VariablesCurrent E-Cigarette Use
Unadjusted OR (95% CI)Adjusted OR (95% CI)
Age0.92 (0.76, 1.11)0.90 (0.74, 1.10)
Gender
FemaleRef.Ref.
Male0.68 (0.39, 1.20)0.79 (0.44, 1.40)
Race
WhiteRef.Ref.
Black0.60 (0.17, 2.12)0.50 (0.14, 1.86)
Others0.69 (0.15, 3.22)0.53 (0.11, 2.63)
Parent or Guardian’s Educational Attainment
Less than high school diplomaRef.Ref.
High school graduation0.57 (0.28, 1.16)0.60 (0.28, 1.28)
Some college0.36 * (0.15, 0.86)0.36 * (0.14, 0.90)
College graduation0.17 * (0.05, 0.62)0.20 * (0.05, 0.75)
Graduate degree or higher0.31 * (0.12, 0.81)0.33 * (0.12, 0.91)
Parent Employment
NoRef.Ref.
Yes0.91 (0.43, 1.92)1.24 (0.55, 2.80)
Two-parent Household
NoRef.Ref.
Yes0.54 (0.29, 1.00)0.65 (0.34, 1.25)
Note. * p < 0.05. Abbreviation. OR = Odds Ratio; CI = Confidence Interval.
Table 4. Logistic Regression Results for Current Conventional Cigarette Use.
Table 4. Logistic Regression Results for Current Conventional Cigarette Use.
VariablesCurrent Conventional Cigarette Use
Unadjusted OR (95% CI)Adjusted OR (95% CI)
Age1.25 (0.99, 1.58)1.24 (0.97, 1.58)
Gender
FemaleRef.Ref.
Male0.78 (0.39, 1.56)0.78 (0.38, 1.59)
Race
WhiteRef.Ref.
Black0.36 (0.10, 1.30)0.31 (0.08, 1.19)
Others0.26 (0.04, 1.68)0.26 (0.04, 1.78)
Parent or Guardian’s Educational Attainment
Less than high school diplomaRef.Ref.
High school graduation1.34 (0.48, 3.74)1.68 (0.57, 4.99)
Some college0.79 (0.24, 2.59)1.07 (0.31, 3.72)
College graduation0.55 (0.13, 2.40)0.74 (0.16, 3.44)
Graduate degree or higher0.41 (0.09, 1.77)0.60 (0.13, 2.75)
Parent Employment
NoRef.Ref.
Yes0.57 (0.25, 1.30)0.67 (0.28, 1.61)
Two-parent Household
NoRef.Ref.
Yes0.70 (0.34, 1.44)0.73 (0.33, 1.61)
Note.p < 0.05. Abbreviation. OR = Odds Ratio; CI = Confidence Interval.
Table 5. Logistic Regression Results for Current E-cigarette Use Stratified by Gender.
Table 5. Logistic Regression Results for Current E-cigarette Use Stratified by Gender.
VariablesCurrent E-Cigarette Use
FemaleMale
Unadjusted OR (95% CI)Adjusted OR (95% CI)Unadjusted OR (95% CI)Adjusted OR (95% CI)
Age0.91 (0.72, 1.16)0.84 (0.65, 1.08)0.99 (0.72, 1.35)1.00 (0.72, 1.39)
Race
WhiteRef.Ref.Ref.Ref.
Black0.56 (0.12, 2.73)0.34 (0.06, 1.84)0.60 (0.07, 5.13)0.84 (0.09, 7.82)
Others0.64 (0.09, 4.53)0.32 (0.04, 2.58)0.78 (0.06, 10.00)0.96 (0.07, 13.81)
Parent or Guardian’s Educational Attainment
Less than high school diplomaRef.Ref.Ref.Ref.
High school graduation0.79 (0.33, 1.93)0.78 (0.30, 2.02)0.33 (0.09, 1.13)0.33 (0.09, 1.22)
Some college0.39 (0.13, 1.19)0.31 (0.10, 1.01)0.35 (0.09, 1.44)0.38 (0.08, 1.70)
College graduation0.25 (0.05, 1.22)0.23 (0.04, 1.21)0.11 (0.01, 1.01)0.12 (0.01, 1.17)
Graduate degree or higher0.24 * (0.06, 0.95)0.22 * (0.05, 0.91)0.43 (0.10, 1.78)0.49 (0.11, 2.30)
Parent Employment
NoRef.Ref.Ref.Ref.
Yes1.16 (0.43, 3.14)1.68 (0.56, 4.98)0.62 (0.19, 1.95)0.68 (0.19, 2.43)
Two-parent Household
NoRef.Ref.Ref.Ref.
Yes0.50 (0.21, 1.19)0.51 (0.20, 1.26)0.72 (0.29, 1.79)0.83 (0.30, 2.26)
Note. * p < 0.05. Abbreviation. OR = Odds Ratio; CI = Confidence Interval.
Table 6. Logistic Regression Results for Current E-cigarette Use Stratified by Age Group.
Table 6. Logistic Regression Results for Current E-cigarette Use Stratified by Age Group.
VariablesCurrent E-Cigarette Use
Age ≤ 16Age > 16
Unadjusted OR (95% CI)Adjusted OR (95% CI)Unadjusted OR (95% CI)Adjusted OR (95% CI)
Gender
FemaleRef.Ref.Ref.Ref.
Male0.58 (0.29, 1.19)0.60 (0.29, 1.25)0.95 (0.37, 2.43)1.37 (0.50, 3.77)
Race
WhiteRef.Ref.Ref.Ref.
Black0.54 (0.11, 2.62)0.38 (0.07, 2.04)0.69 (0.08, 5.82)0.95 (0.09, 10.01)
Others0.69 (0.11, 4.44)0.44 (0.06, 3.22)0.53 (0.03, 9.71)0.54 (0.02, 11.69)
Parent or Guardian’s Educational Attainment
Less than high school diplomaRef.Ref.Ref.Ref.
High school graduation0.85 (0.34, 2.10)0.92 (0.35, 2.42)0.27 * (0.08, 0.92)0.24 * (0.06, 0.92)
Some college0.41 (0.14, 1.22)0.42 (0.13, 1.32)0.29 (0.07, 1.25)0.25 (0.05, 1.21)
College graduation0.23 (0.05, 1.16)0.25 (0.05, 1.33)0.11 * (0.01, 0.98)0.10 * (0.01, 0.99)
Graduate degree or higher0.28 (0.08, 1.00)0.32 (0.09, 1.23)0.36 (0.08, 1.60)0.33 (0.07, 1.64)
Parent Employment
NoRef.Ref.Ref.Ref.
Yes0.72 (0.28, 1.84)1.08 (0.39, 2.99)1.17 (0.32, 4.19)1.68 (0.42, 6.77)
Two-parent Household
NoRef.Ref.Ref.Ref.
Yes0.56 (0.27, 1.16)0.72 (0.33, 1.57)0.47 (0.15, 1.47)0.56 (0.17, 1.87)
Note. * p < 0.05. Abbreviation. OR = Odds Ratio; CI = Confidence Interval.
Table 7. Logistic Regression Results for Current Conventional Cigarette Use Stratified by Gender.
Table 7. Logistic Regression Results for Current Conventional Cigarette Use Stratified by Gender.
VariablesCurrent Conventional Cigarette Use
FemaleMale
Unadjusted OR (95% CI)Adjusted OR (95% CI)Unadjusted OR (95% CI)Adjusted OR (95% CI)
Age0.98 (0.72, 1.32)0.89 (0.65, 1.21)2.52 ** (1.45, 4.38)2.50 ** (1.43, 4.36)
Race
WhiteRef.Ref.Ref.Ref.
Black0.32 (0.06, 1.58)0.20 (0.03, 1.19)0.39 (0.04, 3.41)0.47 (0.04, 5.16)
Others0.20 (0.02, 2.43)0.12 (0.01, 1.75)0.37 (0.02, 6.72)0.36 (0.02, 8.44)
Parent or Guardian’s Educational Attainment
Less than high school diplomaRef.Ref.Ref.Ref.
High school graduation1.22 (0.37, 4.05)1.97 (0.53, 7.33)1.94 (0.23, 16.55)1.65 (0.15, 18.71)
Some college0.65 (0.15, 2.72)0.79 (0.17, 3.74)1.53 (0.15, 15.48)1.46 (0.11, 18.91)
College graduation0.63 (0.11, 3.63)1.07 (0.16, 7.04)0.66 (0.04, 11.01)0.49 (0.02, 10.79)
Graduate degree or higher0.20 (0.02, 1.82)0.29 (0.03, 2.87)1.22 (0.11, 14.15)1.36 (0.08, 22.13)
Parent Employment
NoRef.Ref.Ref.Ref.
Yes0.44 (0.16, 1.19)0.49 (0.16, 1.49)0.91 (0.19, 4.23)1.16 (0.22, 6.00)
Two-parent Household
NoRef.Ref.Ref.Ref.
Yes0.34 (0.10, 1.19)0.32 (0.09, 1.20)1.21 (0.41, 3.55)1.24 (0.37, 4.17)
Note. ** p < 0.01. Abbreviation. OR = Odds Ratio; CI = Confidence Interval.
Table 8. Logistic Regression Results for Current Conventional Cigarette Use Stratified by Age Group.
Table 8. Logistic Regression Results for Current Conventional Cigarette Use Stratified by Age Group.
VariablesCurrent Conventional Cigarette Use
Age ≤ 16Age > 16
Unadjusted OR (95% CI)Adjusted OR (95% CI)Unadjusted OR (95% CI)Adjusted OR (95% CI)
Gender
FemaleRef.Ref.Ref.Ref.
Male0.28 (0.08, 1.00)0.27 * (0.07, 0.98)1.51 (0.58, 3.90)1.81 (0.67, 4.93)
Race
WhiteRef.Ref.Ref.Ref.
Black0.22 (0.04, 1.13)0.12 * (0.02, 0.78)0.69 (0.08, 5.82)0.93 (0.10, 8.69)
Others0.16 (0.01, 1.92)0.09 (0.01, 1.42)0.53 (0.03, 9.71)0.61 (0.03, 12.15)
Parent or Guardian’s Educational Attainment
Less than high school diplomaRef.Ref.Ref.Ref.
High school graduation4.78 (0.60, 37.88)10.75 * (1.09, 106.05)0.52 (0.14, 1.93)0.44 (0.11, 1.79)
Some college1.59 (0.16, 15.78)2.49 (0.21, 29.38)0.63 (0.15, 2.76)0.53 (0.11, 2.55)
College graduation1.08 (0.07, 17.77)2.42 (0.12, 48.15)0.37 (0.06, 2.18)0.29 (0.04, 1.94)
Graduate degree or higher0.64 (0.04, 10.53)1.46 (0.08, 28.28)0.38 (0.06, 2.25)0.31 (0.05, 2.05)
Parent Employment
NoRef.Ref.Ref.Ref.
Yes0.32 * (0.11, 0.94)0.33 (0.10, 1.13)1.17 (0.32, 4.19)1.32 (0.35, 5.04)
Two-parent Household
NoRef.Ref.Ref.Ref.
Yes0.59 (0.21, 1.69)0.50 (0.15, 1.75)0.85 (0.31, 2.33)0.88 (0.30, 2.57)
Note. * p < 0.05. Abbreviation. OR = Odds Ratio; CI = Confidence Interval.
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Sheikhattari, P.; Barsha, R.A.A.; Egboluche, C.; Assari, S. Association Between Socioeconomic Status and E-Cigarette Use in Baltimore High Schools: Comparison of Girls and Boys. Women 2025, 5, 33. https://doi.org/10.3390/women5030033

AMA Style

Sheikhattari P, Barsha RAA, Egboluche C, Assari S. Association Between Socioeconomic Status and E-Cigarette Use in Baltimore High Schools: Comparison of Girls and Boys. Women. 2025; 5(3):33. https://doi.org/10.3390/women5030033

Chicago/Turabian Style

Sheikhattari, Payam, Rifath Ara Alam Barsha, Chidubem Egboluche, and Shervin Assari. 2025. "Association Between Socioeconomic Status and E-Cigarette Use in Baltimore High Schools: Comparison of Girls and Boys" Women 5, no. 3: 33. https://doi.org/10.3390/women5030033

APA Style

Sheikhattari, P., Barsha, R. A. A., Egboluche, C., & Assari, S. (2025). Association Between Socioeconomic Status and E-Cigarette Use in Baltimore High Schools: Comparison of Girls and Boys. Women, 5(3), 33. https://doi.org/10.3390/women5030033

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