We chose not to impute missing data on the outcome or main independent variables and thus excluded 12.8% of the observations in the remaining sample . Finally, we excluded missing data on covariates due to the small proportion of missing. Our final analytic sample included 953,445 observations . First, we examined gender identity and race/ethnicity differences in sociodemographic characteristics via descriptive statistics, and calculated bivariate chi-squares accounting for school clustering. Next, we calculated prevalence estimates of vaping frequency by genderidentity stratified by race/ethnicity. Because our outcome variable met the proportional odds assumption for our independent variables , we used generalized linear mixed models for an ordinal outcome to examine bivariate odds ratios and multi-variable adjusted odds ratios and 95% confidence intervals estimating associations of gender identity and race/ethnicity with vaping. To examine the joint relationship between gender identity and race/ethnicity in vaping frequency,we formally tested gender identity-byrace/ethnicity statistical interaction , adjusting for covariates. Because we aimed to examine the relationship between two independent variables and an outcome,28 we estimated two models: one quantifying relationships between gender identity and vaping within race/ethnicity strata and another quantifying relationships between race/ethnicity and vaping within gender identity strata. Generalized linear mixed models included random intercepts at level two to account for correlations among adolescents nested in schools. Models were fitted by maximum likelihood with Laplace approximation in SAS version 9.4. The San Diego State University Institutional Review Board deemed our analysis of publicly available, de-identified data, exempt from review.
Most adolescents in the analytic sample were cisgender ; 0.92% were transgender, and 1.73% were unsure of their gender identity. Table 1 provides sociodemographic characteristics of the sample overall and by gender identity. Participants were diverse in terms of their race/ethnicity,plant benches with Latinx adolescents making up more than half of the sample , followed by white , Asian , multiracial , Black , Native Hawaiian or Pacific Islander , and American Indian or Alaskan Native adolescents. Chi-square tests revealed statistically significant associations between independent variables and grade, parental education, and sexual orientation . The prevalence of any past 30-day vaping for the full sample was 8.6%. As a point of comparison, the prevalence of any past 30-day combustible cigarette smoking was 1.7% for the full sample. Table 2 presents the distributions of vaping frequency in the past 30-days by gender identity and bivariate associations between gender identity and vaping frequency, each within race/ethnicity strata. For each race/ethnicity stratum, transgender adolescents evidenced greater odds of more days vaping relative to their cisgender peers . Associations for adolescents unsure of their gender identity were less consistent, with only Latinx, Asian, and Black adolescents evidencing greater odds of more days vaping than their cisgender peers of the same race/ethnicity . All race/ethnicity by gender identity interactions were significant for transgender adolescents of color relative to cisgender white adolescents , and four out of six interactions were significant for adolescents of color unsure of their gender identity relative to cisgender white adolescents . Thus, AORs and 95% CIs of vaping in relation to gender identity within race/ethnicity strata, and race/ethnicity within gender identity strata are presented in Tables 3 and 4, respectively. Table 3 presents the AORs of vaping frequency for transgender adolescents and adolescents unsure of their gender identity relative to their cisgender peers of the same race/ethnicity.
Transgender adolescents evidenced greater odds of more frequent vaping in the past 30-days compared to cisgender adolescents across each race/ethnicity stratum . AORs ranged from 1.20 among transgender white adolescents to 6.05 among transgender Black adolescents relative to their cisgender peers of the same race/ethnicity. Patterns were less consistent for adolescents unsure of their gender identity. Compared to cisgender adolescents of the same race/ethnicity, Asian, Latinx, and Black adolescents unsure of their gender identity evidenced 1.34, 1.43, and 3.28 times greater odds of more frequent vaping, respectively. In contrast, white adolescents unsure of their gender identity evidenced lower odds than cisgender white adolescents. Table 4 presents the AORs of vaping frequency for Latinx, American Indian or Alaskan Native, Asian, Black, Native Hawaiian or Pacific Islander, and multiracial adolescents for each gender identity stratum relative to white adolescents of the same gender identity. Cisgender Latinx, Asian, Black, and multiracial adolescents evidenced lower odds of more frequent vaping relative to cisgender white adolescents. A reverse pattern appeared for transgender adolescents of color, however, such that transgender Latinx, American Indian or Alaskan Native, Black, Native Hawaiian or Pacific Islander, and multiracial adolescents evidenced greater adjusted odds of more frequent vaping relative to transgender white adolescents. For adolescents unsure of their gender identity, patterns were less consistent. Whereas Latinx, Black, and Native Hawaiian or Pacific Islander adolescents unsure of their gender identity evidenced greater odds of more days vaping relative to white adolescents unsure of their gender identity, Asian adolescents unsure of their gender identity evidenced lower odds of more days vaping than white adolescents unsure of their gender identity.
Consistent with our hypothesis, we found that gender identity and race/ethnicity significantly interacted in their association in vaping frequency such that transgender adolescents of color were generally more likely to report a higher frequency of vaping compared to cisgender white adolescents. Although less consistent, some groups of adolescents of color who were unsure of their gender identity were also disproportionately more likely to report a higher frequency of vaping compared to cisgender white adolescents. In stratified models, we observed disparities in vaping frequency between transgender and cisgender adolescents within each race/ethnicity stratum as well as in vaping frequency among transgender Latinx, American Indian and Alaskan Native, Black, Native Hawaiian or Pacific Islander, and multiracial relative to their transgender white peers. The largest differences in both stratified models were among transgender Black adolescents who evidenced 6 times the odds of more frequent vaping relative to their cisgender Black peers and nearly 3 times the odds of more frequent vaping relative to their transgender white peers. In the model stratified by gender identity, we observed reversed patterns among cisgender adolescents, with white adolescents evidencing greater odds of more frequent vaping than their cisgender peers of color. Taken together, our findings extend past research documenting vaping and other tobacco use disparities among transgender relative to cisgender youth9-13 to highlight pronounced disparities in vaping frequency among transgender adolescents of color. Our finding of gender identity disparities in vaping frequency among Black adolescents in particular aligns with a recent analysis of data from the 2018-19 Behavioral Risk Factor Surveillance System finding that transgender Black adults were more likely to be current smokers relative to cisgender Black adults.Additionally, our finding that cisgender adolescents of color tended to vape less frequently than their cisgender white peers is in keeping with prior research documenting greater prevalence of vaping among white adolescents compared to their Black and Latinx peers.Our study does not explain the reasons for the observed disparities in vaping frequency; however, structural injustice has been identified as a fundamental cause of health disparities.Structural injustice is enforced via inequitable socio-political and economic systems and norms which differentially influence access to resources and opportunities for groups based on relative societal power, and in turn,rolling bench health behaviors and outcomes.Interpreting our findings through this understanding of structural injustice, gender minority stress,and intersectionality suggests multilevel discrimination and stressors may drive the observed disparities in vaping frequency among transgender adolescents of color. Transgender youth of color face pronounced housing instability, employment precarity, lack of access to healthcare, and violence and victimization,which may lead to vaping as a coping strategy. Qualitative research with racially/ethnically diverse LGBTQ youth smokers have found that participants describe smoking as a way to deal with stress and take back control from or rebel against oppressive systems.Limited supportive resources in schools may also underlie disparities in vaping among transgender adolescents of color. For example, participation in LGBTQ empowerment groups, i.e., Gender and Sexuality Alliances , is associated with lower levels of school-based victimization and greater receptivity to school-based substance use prevention efforts among LGBTQ adolescents.However, there are several limitations to effective engagement of transgender adolescents and adolescents of color within GSAs, including limited considerations of or discussions regarding diverse gender identities and intersections of LGBTQ identities with race, ethnicity, and socioeconomic position among members.If GSAs or other LGBTQspecific resources in schools are not inclusive of or welcoming to youth with diverse genderidentities or race/ethnicities, the potential for these resources to buffer against stress and/or prevent vaping may be inequitably distributed. Additionally, the enduring history of predatory marketing of tobacco and vape products to youth may influence vaping disparities among transgender adolescents of color.
A recent study found LGBTQ adolescents and Black and Latinx adolescents reported higher engagement with online tobacco and e-cigarette marketing compared to their non-LGBTQ and white peers, respectively.A final note about interpreting the study’s findings is warranted. One might conclude that gender identity , as opposed to race/ethnicity , contributes more to disparities in vaping among transgender/unsure adolescents of color because the magnitude of these disparities is larger within race/ethnic groups than across race/ethnic groups. We caution against such an interpretation, as this logic contradicts the notion that systems of power are intersecting and interlocking; thus, identities or social positions cannot be neatly disentangled.Instead, we call attention to the increased vulnerability for higher vaping frequency among transgender adolescents of color with the framework of intersectionality in mind, and the need for future research to examine and intervene on the interlocking systems shaping these disparities.Our study should be considered within the context of its limitations. Our sample consists of adolescents in secondary schools in one U.S. state ; the extent that findings generalize to adolescents in California and more broadly is uncertain. Additionally, there is variability in the terms used by transgender and gender diverse people to describe their gender identity.Thus, our categories may not reflect the diversity of participants’ gender identities orbe culturally sensitive to gender identities among adolescents within particular racial/ethnic groups, such as American Indian or Alaskan Native adolescents who may identify as two-spirit or other gender identities not assessed in the CHKS.A similar concern relates to our measurement of race and ethnicity which we combined as race/ethnicity, leading to categorization of more than half the sample as Latinx . Although this approach to measurement is common, our failure to disentangle ethnicity from race may have masked nuanced disparities among Latinx adolescents who also identify with a specific race , for example, Afro-Latinx adolescents.We were also are unable to determine precisely the substances vaped by participants as the survey did not measure substances vaped , however the CHKS item is preceded by questions about past 30-day smoking and use of smokeless tobacco ; other types of substance use are asked about separately. Finally, we did not test causal mechanisms of the observed vaping disparities. At best, our independent variables of race/ethnicity and gender identity are proxies for the inequitable systems of power that shape health determinants and outcomes.A key strength is our use of a large, diverse, methodologically strong, population-based sample of adolescents in schools. Our study is strengthened by examining vaping disparities with three categories of gender identity and seven categories of race/ethnicity – yielding detailed information for multiple racial/ethnic groups of transgender adolescents and adolescents unsure of their gender identity. Although some of our analytic categories were relatively small , these findings offer insights into vaping disparities for subgroups often left out or obscured in research and highlight their unique health-related needs. Finally, our use of an ordinal model to assess disparities in vaping frequency is a strength, as more frequent vaping may be more harmful than infrequent vaping. Our findings have implications for future research, including the need to examine the multilevel causal mechanisms of adolescent vaping disparities at the intersection of gender identity and race/ethnicity. Explicit examinations of how systems of power intersect to shape disparities are necessary to mitigate inequitable population-level differences in health behaviors and outcomes.Thus, future research on vaping disparities among transgender and other marginalized communities of young people should employ novel and community-engaged approaches that identify and interrogate these systems.In MM-CBPR, researchers collaborate directly with communities to gather and synthesize both qualitative and quantitative data to generate locally valid results and catalyze action for social change and sustainable health improvements.