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. Author manuscript; available in PMC: 2026 Mar 13.
Published in final edited form as: Prev Med. 2018 May 12;113:109–115. doi: 10.1016/j.ypmed.2018.05.014

Sexual and gender minority cigarette smoking disparities: An analysis of 2016 Behavioral Risk Factor Surveillance System data☆

Leah Hoffman a,*, Janine Delahanty a, Sarah E Johnson a, Xiaoquan Zhao a,b
PMCID: PMC12980546  NIHMSID: NIHMS2144473  PMID: 29763683

Abstract

We examined the association between lesbian, gay, bisexual, and transgender (LGBT) identity, cigarette and e-cigarette use, and potential risk factors in the United States. Using data from 198,057 adults in 26 states in the 2016 Behavioral Risk Factor Surveillance System (BRFSS), we estimated the prevalence of cigarette use, e-cigarette use, and potential risk factors by gender identity and sexual identity. Overall and sex-stratified bivariate and multivariate logistic regressions examined whether the relationship between sexual and gender identity and cigarette and e-cigarette use persisted after adjusting for demographics, socio-economic status, and other unhealthy behaviors. After adjusting for covariates, gender minority identity was no longer associated with increased likelihood of currently smoking cigarettes and ever use of e-cigarettes. Sexual minority identity continued to be significant after adjusting for covariates, indicating that sexual identity disparities in cigarette and e-cigarette use are not fully explained by these factors. Findings varied by identity. Compared to their straight peers, likelihood of tobacco product use among LGB individuals varied between sexes, by product, and by sexual identity (gay/lesbian versus bisexual). More research is needed to understand the mechanisms that influence diverse patterns of cigarette and e-cigarette use among sexual and gender minority adults.

Keywords: Sexual minorities, Transgender individuals, Tobacco, Smoking, Public health

1. Introduction

Lesbian, gay, bisexual, and transgender (LGBT) populations are more likely to use tobacco compared to those who are not LGBT (King et al., 2012). Previous research has largely focused on sexual minority adults in particular; those who identify as lesbian, gay, or bisexual, or have another non-straight identity (LGB). This research indicates that tobacco use is higher among LGB adults compared to straight adults. However, sexual identity disparities in tobacco use differ by sex and product (Agaku et al., 2014; Lee et al., 2009; Johnson et al., 2016; Max et al., 2016; Phillips et al., 2017; Hu et al., 2016; Weaver et al., 2016; Pericot-Valverde et al., 2017; Sharapova et al., 2018; Ortiz et al., 2017; Emory et al., 2016; Majeed et al., 2017; Gonzales and Henning-Smith, 2017a; Jamal et al., 2016; Jamal et al., 2018). Literature is mixed on the influence of gender minority status; transgender adults also report elevated rates of smoking (Shires and Jaffee, 2016; Buchting and Emory, 2017; Meyer et al., 2017) relative to their non-gender minority (i.e., cisgender) counterparts, though research to date has largely been limited by small sample sizes.

Tobacco use disparities may be influenced by LGBT-targeted tobacco marketing, a strategy with origins dating back to the early nineties (Dilley et al., 2008; Smith and Malone, 2003; Stevens et al., 2004; Smith et al., 2008). Other factors also contribute to LGBT tobacco use disparities. Sexual minority identity is associated with a higher overall mortality rate between ages 18 to 59 (Cochran et al., 2016), worse overall health (Cochran et al., 2016; Branstrom et al., 2016; Elliott et al., 2015), elevated rates of depression and other mental health issues (Gonzales and Henning-Smith, 2017a; Cochran et al., 2016; Conron et al., 2010; Blosnich et al., 2013; McLaughlin et al., 2012; Grant et al., 2010), illicit drug use and substance abuse (Conron et al., 2010; Grant et al., 2010; Corliss et al., 2010), and alcohol use and binge drinking (Gonzales and Henning-Smith, 2017a; Cochran et al., 2016; Conron et al., 2010); all factors also associated with tobacco use (Shires and Jaffee, 2016; Blosnich et al., 2013; McLaughlin et al., 2012). Transgender identity is also associated with healthcare barriers and poor general, physical, and mental health (Meyer et al., 2017; Motwani and Fatehchehr, 2017; Gonzales and Henning-Smith, 2017b; Herman et al., 2017). This constellation of factors is attributed, in part, to the stress associated with possessing a devalued, minority status, including an increased likelihood of experiencing stigmatization, discrimination (Cochran et al., 2016; Grant et al., 2010; Blondeel et al., 2016; Reisner et al., 2016), and homelessness (McLaughlin et al., 2012)—collectively known as minority stress (Gruskin et al., 2008) (Hatzenbuehler et al., 2014). As more data become available, analyses of this tobacco disparity are becoming more complex, adjusting for socio-demographics and socio-economic status (SES) (Max et al., 2016; Ortiz et al., 2017; Emory et al., 2016) as well as other factors such as health status, and use of other tobacco products (Weaver et al., 2016; Pericot-Valverde et al., 2017; Majeed et al., 2017); however, most have not yet included unhealthy behaviors and minority stress-related factors.

The Behavioral Risk Factor Surveillance System (BRFSS) study is one of the first large-scale studies in the U.S. to include gender identity and sexual identity measures, providing an important opportunity to build on what is known about sexual and gender minority tobacco use. The current study seeks to use BRFSS data to compare cigarette and e-cigarette use prevalence between (1) sexual minority and straight and (2) transgender and cisgender adults overall and segmented by sex, adjusted for factors associated with tobacco use including those related to minority stress, demographics, and socioeconomic status (SES). Tobacco use rates differ when comparing LGB adults with their straight peers of the same sex (Johnson et al., 2016; Cochran et al., 2016; Blosnich et al., 2013; Fallin et al., 2015; Lindley et al., 2012), which supports stratifying by sex when examining disparities among LGBT individuals. For each sex group, sexual minority and transgender cigarette and e-cigarette use are compared against use by their straight and cisgender peers, using a model with the above-noted constellation of factors to examine if disparities persist after adjusting for factors found in extant research.

2. Methods

The Centers for Disease Control and Prevention (CDC), in partnership with state, territory, and commonwealth health departments, conducts the BRFSS annually among the noninstitutionalized adult population who provided informed consent nationally using a cross-sectional, random-digit-dial (RDD) design that includes both landline and cell phone numbers. In 2016, a total of 486,303 interviews were conducted. The dataset and accompanying information on the study design, questionnaires, and weighting methodology are publicly available online (Centers for Disease Control and Prevention, 2014). Twenty-six states opted to include the BRFSS Sexual and Gender identity optional module as part of their survey in 2016. From this group, 7214 individuals were excluded because they selected “don’t know/not sure”, refused to answer, or selected “other” for any of these three measures; resulting in a study sample of 205,271 respondents with information on their sex, sexual identity, and gender identity. Though this sample is not nationally representative, it is one of the larger datasets available that includes both sexual and gender identity, and therefore provides new opportunities for insight.

2.1. Measures

Gender identity was assessed with two items. The first asked, “Do you consider yourself to be transgender?”. Respondents answering “Yes” to the first question were then asked, “Do you consider yourself to be 1. Male-to-female, 2. Female-to-male, or 3. Gender non-conforming?”. Response options to the second question included: “Yes, Transgender, Male-to-female” (n = 324); “Yes, Transgender, Female-to-male” (n = 237); “Yes, Transgender, Gender non-conforming” (n = 147); or “No”. Other response options “Don’t know/Not sure”; and “Refused” were excluded from the sample.

Sexual identity was assessed by having respondents select from a list of sexual identities including: “Straight,” “Lesbian or gay,” and “Bisexual,”. Sexual minority identity includes those who identified as lesbian, gay, or bisexual. Other response options: “Other,” “Don’t know/Not sure,” and “Refused” had been excluded from the sample.

The measure we refer to as sex was assessed based on self-report to the question, “Are you….?” with the options “Male”, “Female”, or Refused. Those who had refused to answer were excluded from the sample.

Smoking behavior was assessed in two ways: (1) ever smoking, defined as having smoked at least 100 cigarettes in one’s lifetime; and (2) current cigarette smoking, defined as ever smoking and now smoking every day or some days. Ever e-cigarette use was indicated by answering yes to the following question, “Have you ever used an e-cigarette or other electronic ‘vaping’ product, even just one time, in your life?” Among ever e-cigarette users, current use was indicated by reporting using e-cigarettes every day or some days.

Binge drinking was defined as having consumed at least 4+ alcoholic drinks among female participants and 5+ drinks among male participants on one occasion in the past 30 days. Those who answered “Yes” to the question “Has a doctor, nurse, or other health professional ever told you that you have a depressive disorder, including depression, major depression, dysthymia, or minor depression?” were coded as ever having a diagnosed depressive disorder. Another unhealthy behavior, lack of exercise, was defined as answering “No” to the question “During the past month, other than your regular job, did you participate in any physical activities or exercises such as running, calisthenics, golf, gardening, or walking for exercise?”.

Demographic and socio-economic data included age (18–24, 25–34, 35–44, 45–54, 55–64, or 65+); race and ethnicity; education level (high school or equivalent or less, some college or post high school certificates, or college or more); employment (being unemployed or being employed, and being a student, retired, or a homemaker); and annual household income (< $15,000, $15,000– < $25,000, $25,000– < $35,000, $35,000– < $50,000, or ≥$50,000).

2.2. Statistical analyses

The analysis was conducted in IBM© SPSS© Statistics Version 24.0 among the subsample who were asked gender and sexual identity module questions and provided a response other than “other” or “don’t know/not sure”. The analysis was conducted using BRFSS national weights. We described the analytical sample by cigarette smoking, e-cigarette use, sexual and gender identity, demographics, socio-economic status, and other unhealthly behaviors (e.g., alcohol binging, lack of exercise). We segmented the sample to facilitate two comparisons: sexual minorities versus straight adults and those who were transgender versus their cisgender peers. We conducted bivariate comparisons and multivariate logistic regressions with lifetime (100+) and current cigarette smoking as well as ever and current e-cigarette use as the dependent variables to test for tobacco use disparities by sexual and gender minority status overall and by sex (females and males).

3. Results

3.1. Demographics, socio-economic status, cigarette, and e-cigarette use

Sample characteristics are described in Table 1. Sexual minorities represent 3.26% (n = 6450) and transgender adults 0.36% (n = 708) of the analytical sample. More sexual minorities reported being transgender compared to straight adults and more transgender adults reported being sexual minorities compared to cisgender adults. Depressive disorder diagnosis was higher among sexual minorities and transgender adults in comparison to their peers. Binge drinking was more common among sexual minorities than those who are straight. More sexual minority (versus straight) and cisgender (versus transgender) adults reported exercising.

Table 1.

Comparison by sexual identity and gender identity among U.S. adults for tobacco use, demographics, and socio-economic status, BRFSS 2016.

Variables Participant characteristics % (95% CI)
LGB Straight Transgender Cisgender
n = 6450 n = 191,607 n = 708 n = 197,349
Ever smoked 100+ cigarettes 47.5 (45.5–49.5) 41.4 (41.0–41.7) 44.6 (38.5–50.9) 41.6 (41.2–42.0)
Current cigarette smoker 21.8 (20.2–23.4) 14.6 (14.3–14.8) 21.0 (16.3–26.6) 14.8 (14.6–15.1)
Ever used e-cigarettes 36.5 (34.6–38.5) 18.1 (17.8–18.4) 26.0 (20.7–32.1) 18.8 (18.5–19.1)
Current e-cigarette user 22.3 (19.6–25.3) 19.7 (19.0–20.4) 27.8 (18.5–39.4) 19.8 (19.1–20.6)
Sexual ID
 Straight 0.0 100.0 70.5 (64.4–76.0) 96.3 (96.2–96.5)
 Gay/lesbian 48.0 (46.1–50.0) 0.0 13.2 (2.3–9.2) 1.8 (1.7–1.9)
 Bisexual 52.0 (50.0–53.9) 0.0 16.3 (12.2–21.4) 1.9 (1.8–2.0)
Gender ID
 Transgender 2.8 (2.2–3.5) 0.3 (0.2–0.3) 100.0 0.0
 Cisgender 97.2 (96.5–97.8) 99.7 (99.7–99.8) 0.0 100.0
Self-reported sex
 Female 50.0 (48.0–52.0) 53.6 (53.2–54.0) 47.2 (41.0–53.5) 53.5 (53.1–53.9)
 Male 50.0 (48.0–52.0) 46.4 (46.0–46.8) 52.5 (46.5–59.0) 46.5 (46.1–46.9)
Binge drinkera 23.1 (21.5–24.9) 15.2 (14.9–15.5) 15.6 (11.8–20.3) 15.5 (15.2–15.8)
Ever diagnosed with depressive disorder 33.7 (31.8–35.6) 15.8 (15.5–16.1) 31.5 (26.0–37.6) 16.4 (16.2–16.7)
Exercised in the past 30 days 80.3 (78.7–81.9) 76.8 (76.5–77.1) 66.2 (59.6–72.2) 77.0 (76.7–77.3)
Age
 18–24 18.4 (16.9–20.0) 7.3 (7.1–7.5) 16.0 (11.7–21.4) 7.7 (7.5–7.9)
 25–34 22.8 (21.1–24.5) 12.7 (12.4–12.9) 15.9 (11.9–20.9) 13.0 (12.8–13.3)
 35–44 14.1 (12.7–15.6) 13.9 (13.7–14.2) 11.5 (7.7–16.9) 14.0 (13.7–14.2)
 45–54 17.2 (15.8–18.8) 18.3 (18.0–18.6) 16.8 (12.6–22.0) 18.2 (17.9–18.5)
 55–64 14.7 (13.5–16.1) 21.2 (20.9–21.5) 20.7 (15.7–26.8) 21.0 (20.7–21.3)
 65+ 12.8 (11.6–14.1) 26.6 (26.3–26.9) 19.2 (15.5–23.5) 26.1 (25.8–26.4)
Race/ethnicity
 White, Non-Hispanic 65.4 (63.4–67.3) 68.7 (68.3–69.1) 59.4 (52.7–65.7) 68.6 (68.3–69.0)
 Black, Non-Hispanic 9.1 (8.0–10.3) 9.1 (8.9–9.3) 13.3 (8.9–19.4) 9.1 (8.9–9.3)
 Other race, Non-Hispanic 6.0 (5.1–7.1) 5.5 (5.3–5.7) 7.3 (4.8–10.9) 5.5 (5.3–3.7)
 Multiracial, Non-Hispanic 3.7 (3.0–4.6) 1.9 (1.8–2.0) ... 2.0 (1.8–2.1)
 Hispanic 15.7 (14.2–17.5) 14.9 (14.6–15.2) 16.7 (11.9–23.0) 14.9 (14.6–15.2)
Education
 High school diploma, equivalent, or less 28.4 (26.7–30.2) 34.2 (33.8–34.5) 51.0 (44.6–57.3) 33.9 (33.5–34.3)
 Some college or post high school certificates 29.0 (27.2–30.9) 26.5 (26.2–26.9) 29.9 (24.1–36.5) 26.6 (26.3–26.9)
 College or more 42.6 (40.6–44.6) 39.3 (38.9–39.7) 19.1 (14.9–24.1) 39.5 (39.1–39.9)
Employment
 Employed, student, retired, or homemaker 83.7 (82.2–85.1) 88.6 (88.4–88.9) 80.9 (75.5–85.3) 88.5 (88.2–88.7)
 Unemployed or unable to work 16.3 (14.9–17.8) 11.4 (11.1–11.6) 19.1 (14.7–24.5) 11.5 (11.3–11.8)
Income ($)
 < 15,000 12.6 (11.3–14.0) 8.7 (8.4–8.9) 17.3 (12.8–22.8) 8.8 (8.6–9.0)
 15,000– < 25,000 14.6 (13.3–16.0) 12.7 (12.5–13.0) 18.5 (14.5–23.4) 12.8 (12.5–13.1)
 25,000– < 35,000 8.6 (7.6–9.8) 8.3 (8.1–8.5) 11.0 (7.1–16.9) 8.3 (8.1–8.5)
 35,000– < 50,000 12.0 (10.7–13.4) 11.5 (11.3–11.7) 10.3 (7.4–14.1) 11.5 (11.3–11.8)
 50,000+ 40.4 (38.4–42.3) 45.6 (45.2–46.0) 27.6 (22.5–33.4) 45.5 (45.1–45.8)
 Other** 11.9 (10.7–13.2) 13.2 (12.9–13.5) 15.3 (11.1–20.6) 13.1 (12.9–13.4)
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... results are suppressed where the cell value is < 50 or the RSE is ≥ 30%.

a

Binge drinking = sex-defined, males 5+ drinks, females 4+ drinks.

Compared to their straight (41.4%) counterparts, a greater percentage of sexual minorities (47.5%) reported having smoked at least 100 cigarettes in their lifetime. Disparities were notable on current cigarette smoking: 21.8% of sexual minorities compared to only 14.6% of straight adults and 21.0% of transgender versus 14.8% of cisgender individuals were current smokers. Similar disparities were also observed for ever e-cigarette use: 36.5% of sexual minority versus 18.1% of straight adults and 26.0% of transgender adults compared to 18.8% of cisgender adults reported ever using e-cigarettes in their lifetime. There were no significant differences between subgroups for current e-cigarette use. A description of the overall sample, stratified by sex, can be found in Supplementary Table 1.

3.2. Cigarette and e-cigarette use by gender and sexual identity

In bivariate analyses of tobacco use behaviors by gender minority status (not shown), transgender adults were significantly more likely to be current cigarette smokers (Unadjusted Odds Ratio [UOR] 1.53 CI 1.12–2.09) and to ever use e-cigarettes (UOR 1.52 CI 1.13–2.05). Among female adults, there was no significant difference between transgender and cisgender adults and among male adults, this difference was only found for current cigarette smoking (UOR 1.64 CI 1.07–2.52). Gender minority identity was not significantly associated with any of the tobacco use measures after adjusting for covariates (Supplementary Table 2).

In a bivariate comparison by sexual identity (not shown), bisexual adults were significantly more likely than straight adults to ever smoke cigarettes (UOR 1.33 CI 1.19–1.49), currently smoke cigarettes (UOR 1.78 CI 1.57–2.02), ever use e-cigarettes (UOR 3.05 CI 2.72–3.42), and currently use e-cigarettes (UOR 1.31 CI 1.07–1.61). In multivariate analyses, bisexual identity was significant for all tested tobacco use behaviors (Supplementary Table 2): ever smoking cigarettes (AOR 1.57 CI 1.38–1.78), current smoking (AOR 1.28 CI 1.10–1.48), and ever and current e-cigarette use (AOR 1.54 CI 1.35–1.76) (AOR 1.25 CI 1.00–1.24).

Lesbian and gay adults were significantly more likely than straight adults to ever smoke cigarettes (UOR 1.24 CI 1.10–1.39), currently smoke cigarettes (UOR 1.48 CI 1.28–1.71), and ever use e-cigarettes (UOR 2.17 CI 1.91–2.46), but there was no significant difference for current e-cigarette use in bivariate analyses. In multivariate analysis (Supplementary Table 2), gay/lesbian identity was associated with increased likelihood of ever and current cigarette smoking (AOR 1.34 CI 1.18–1.53) (AOR 1.42 CI 1.20–1.69) and ever e-cigarette use (AOR 1.77 CI 1.53–2.05).

3.3. Cigarette and e-cigarette use among females

In bivariate analyses (not shown), gay and lesbian females were more likely than straight females to ever smoke cigarettes (UOR 1.71 CI 1.41–2.06), currently smoke cigarettes (UOR 1.59 CI 1.25–2.03), and ever use e-cigarettes (UOR 2.66 CI 2.15–3.28). Similarly, compared to straight females, bisexual females were also more likely to ever smoke cigarettes (UOR 1.66 CI 1.44–1.90), currently smoke cigarettes (UOR 2.29 CI 1.96–2.68), and ever use e-cigarettes (UOR 4.08 CI 3.54–4.70). There was no significant difference by sexual identity for current e-cigarette use.

In Table 2, these factors were tested among female adults, adjusted for relevant covariates. Gay/lesbian, and bisexual females were significantly more likely than straight females to ever smoke cigarettes (Gay/Lesbian AOR 1.95 CI 1.57–2.41, Bisexual AOR 1.97 CI 1.69–2.30), ever use e-cigarettes (Gay/Lesbian AOR 2.20 CI 1.72–2.80, Bisexual AOR 1.86 CI 1.57–2.19), or currently smoke cigarettes (Gay/Lesbian AOR 1.52 CI 1.14–2.04, Bisexual AOR 1.62 CI 1.34–1.95), but there was no significant difference in terms of current e-cigarette use by sexual identity.

Table 2.

Logistic regression models for cigarette and e-cigarette use among U.S. adult females, BRFSS 2016.

Variables Sample size (n) Ever cigarette smoker n = 45,241 Current cigarette smoker n = 15,418 Ever e-cigarette user n = 15,910 Current e-cigarette user n = 3176
Adjusted odds ratio (95%CI) Adjusted odds ratio (95%CI) Adjusted odds ratio (95%CI) Adjusted odds ratio (95%CI)
Gender ID
 Transgender 325 0.71 (0.49–1.03) 0.87 (0.54–1.41) 0.71 (0.42–1.20) 1.61 (0.78–3.32)
 Cisgender 112,172 Reference Reference Reference Reference
Sexual ID
 Straight 108,770 Reference Reference Reference Reference
 Gay/lesbian 1234 1.95 (1.57–2.41) 1.52 (1.14–2.04) 2.20 (1.72–2.80) 1.32 (0.81–2.14)
 Bisexual 2168 1.97 (1.69–2.30) 1.62 (1.34–1.95) 1.86 (1.57–2.19) 1.25 (0.96–1.63)
Binge drinkinga
 Binge drinker 10,354 2.50 (2.32–2.69) 2.58 (2.35–2.82) 2.58 (2.37–2.80) 1.10 (0.93–1.30)
 Not binge drinker 99,987 Reference Reference Reference Reference
Diagnosed depressive disorder
 Ever diagnosed 23,299 1.72 (1.63–1.82) 1.71 (1.60–1.84) 1.97 (1.84–2.11) 1.27 (1.11–1.46)
 Never diagnosed 88,518 Reference Reference Reference Reference
Exercised in the past 30 days
 Past 30 day exercise 82,302 Reference Reference Reference Reference
 No exercise past 30 days 29,748 1.20 (1.13–1.26) 1.41 (1.32–1.52) 1.20 (1.11–1.29) 0.91 (0.78–1.05)
Age
 18–24 4777 Reference Reference Reference Reference
 25–34 9553 3.39 (2.94–3.92) 2.80 (2.36–3.31) 0.79 (0.69–0.89) 0.96 (0.75–1.22)
 35–44 12,018 4.23 (3.67–4.87) 2.90 (2.45–3.43) 0.50 (0.44–0.57) 1.17 (0.91–1.51)
 45–54 17,982 4.83 (4.20–5.54) 2.80 (2.38–3.29) 0.40 (0.35–0.45) 1.24 (0.97–1.59)
 55–64 25,640 5.79 (5.05–6.63) 2.22 (1.89–2.61) 0.30 (0.26–0.34) 1.42 (1.11–1.81)
 65+ 42,202 5.62 (4.92–6.42) 0.95 (0.80–1.11) 0.12 (0.10–0.13) 1.11 (0.86–1.45)
Race/ethnicity
 White, Non-Hispanic 87,907 Reference Reference Reference Reference
 Black, Non-Hispanic 8632 0.56 (0.52–0.61) 0.67 (0.60–0.75) 0.61 (0.54–0.69) 0.71 (0.56–0.91)
 Other race, Non-Hispanic 4660 0.54 (0.47–0.61) 0.72 (0.60–0.86) 0.65 (0.55–0.77) 0.97 (0.68–1.39)
 Multiracial, Non-Hispanic 2560 1.03 (0.88–1.21) 1.18 (0.94–1.47) 1.45 (1.18–1.77) 1.27 (0.87–1.87)
 Hispanic 7084 0.35 (0.31–0.38) 0.27 (0.23–0.31) 0.45 (0.40–0.52) 0.60 (0.45–0.80)
Education
 High school diploma, equivalent, or less 38,114 1.94 (1.82–2.06) 3.26 (2.96–3.59) 2.19 (2.00–2.39) 1.24 (1.02–1.51)
 Some college or post high school certificates 31,859 1.84 (1.74–1.95) 2.64 (2.40–2.90) 2.09 (1.92–2.27) 1.13 (0.94–1.37)
 College or more 41,974 Reference Reference Reference Reference
Employment
 Employed, student, retired, or homemaker 99,016 Reference Reference Reference Reference
 Unemployed or unable to work 13,156 1.50 (1.39–1.62) 1.51 (1.38–1.64) 1.50 (1.37–1.64) 1.18 (0.99–1.40)
Income ($)
 < 15,000 10,387 1.29 (1.18–1.42) 2.34 (2.08–2.63) 1.45 (1.28–1.63) 1.02 (0.80–1.29)
 15,000– < 25,000 16,612 1.28 (1.19–1.39) 2.27 (2.04–2.53) 1.42 (1.28–1.58) 1.07 (0.88–1.31)
 25,000– < 35,000 10,635 1.21 (1.11–1.32) 2.01 (1.78–2.26) 1.47 (1.31–1.65) 1.19 (0.95–1.50)
 35,000– < 50,000 13,611 1.14 (1.06–1.23) 1.72 (1.54–1.92) 1.36 (1.23–1.51) 1.09 (0.88–1.35)
 50,000+ 43,483 Reference Reference Reference Reference
 Other** 17,444 0.98 (0.91–1.05) 1.46 (1.30–1.63) 1.04 (0.93–1.16) 1.00 (0.78–1.27)
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Model odds ratios adjusted for all other variables in the table.

a

Binge drinking among females = 4+ drinks.

Among females, there was no significant difference by gender minority status in either bivariate or multivariate analyses.

3.4. Cigarette and e-cigarette use among males

In bivariate analyses (not shown), gay males were more likely than straight males to currently smoke cigarettes (UOR 1.33 CI 1.11–1.59) and ever use e-cigarettes (UOR 1.76 CI 1.50–2.07). Compared to straight males, bisexual males were more likely to currently smoke cigarettes (UOR 1.26 CI 1.02–1.56), ever use e-cigarettes (UOR 2.15 CI 1.77–2.61), or currently use e-cigarettes (UOR 1.64 CI 1.15–2.33).

In the multivariate model in Table 3, gay males were more likely than straight males to currently smoke cigarettes (AOR 1.39 CI 1.12–1.73) or ever use e-cigarettes (AOR 1.56 CI 1.30–1.88), but there was no significant difference in terms of ever smoking cigarettes or current e-cigarette use. Bisexual males were significantly more likely to both ever (AOR 1.26 CI 1.01–1.58) and currently (AOR 1.46 CI 1.00–2.12) use e-cigarettes compared to straight males.

Table 3.

Logistic regression models for cigarette and e-cigarette use among U.S. adult males, BRFSS 2016.

Variables Sample size (n) Ever cigarette smoker n = 42,521 Current cigarette smoker n = 13,915 Ever e-cigarette user n = 15,907 Current e-cigarette user n = 3347
Adjusted odds ratio (95%CI) Adjusted odds ratio (95%CI) Adjusted odds ratio (95%CI) Adjusted odds ratio (95%CI)
Gender ID
 Transgender 383 1.13 (0.72–1.78) 1.14 (0.65–1.99) 1.09 (0.59–2.01) 1.33 (0.64–2.76)
 Cisgender 85,502 Reference Reference Reference Reference
Sexual ID
 Straight 82,837 Reference Reference Reference Reference
 Gay 1813 1.13 (0.95–1.33) 1.39 (1.12–1.73) 1.56 (1.30–1.88) 0.81 (0.58–1.14)
 Bisexual 1235 1.07 (0.87–1.31) 0.90 (0.71–1.14) 1.26 (1.01–1.58) 1.46 (1.00–2.12)
Binge drinkinga
 Binge drinker 15,915 2.24 (2.10–2.39) 2.35 (2.18–2.53) 2.32 (2.16–2.49) 0.90 (0.79–1.03)
 Not binge drinker 68,041 Reference Reference Reference Reference
Diagnosed depressive disorder
 Ever diagnosed 11,013 1.66 (1.54–1.79) 1.66 (1.52–1.81) 1.89 (1.73–2.06) 1.47 (1.26–1.72)
 Never diagnosed 74,563 Reference Reference Reference Reference
Exercised in the past 30 days
 Past 30 day exercise 67,309 Reference Reference Reference Reference
 No exercise past 30 days 18,440 1.20 (1.12–1.28) 1.49 (1.38–1.61) 1.06 (0.97–1.15) 0.97 (0.82–1.14)
Age
 18–24 5637 Reference Reference Reference Reference
 25–34 8955 3.32 (2.94–3.75) 2.15 (1.86–2.48) 0.82 (0.74–0.92) 0.91 (0.76–1.10)
 35–44 10,103 4.12 (3.64–4.65) 2.03 (1.75–2.35) 0.40 (0.36–0.45) 0.89 (0.72–1.09)
 45–54 14,405 3.81 (3.39–4.28) 1.83 (1.59–2.11) 0.27 (0.24–0.30) 0.83 (0.67–1.04)
 55–64 19,716 4.73 (4.21–5.30) 1.50 (1.31–1.72) 0.17 (0.15–0.19) 0.70 (0.57–0.87)
 65+ 27,069 8.15 (7.26–9.15) 0.78 (0.67–0.90) 0.08 (0.07–0.09) 0.60 (0.45–0.79)
Race/ethnicity
 White, Non-Hispanic 66,647 Reference Reference Reference Reference
 Black, Non-Hispanic 5180 0.69 (0.63–0.76) 1.05 (0.93–1.18) 0.79 (0.69–0.90) 0.75 (0.58–0.97)
 Other race, Non-Hispanic 4632 0.78 (0.70–0.87) 0.93 (0.79–1.08) 0.80 (0.70–0.92) 1.07 (0.82–1.39)
 Multiracial, Non-Hispanic 2159 1.06 (0.90–1.24) 1.28 (1.06–1.55) 1.20 (1.00–1.45) 1.22 (0.87–1.71)
 Hispanic 5797 0.58 (0.53–0.63) 0.53 (0.47–0.60) 0.60 (0.54–0.67) 0.60 (0.48–0.75)
Education
 High school diploma, equivalent, or less 30,320 2.67 (2.51–2.85) 3.16 (2.88–3.47) 1.98 (1.82–2.16) 1.26 (1.06–1.51)
 Some college or post high school certificates 21,796 2.00 (1.87–2.13) 2.28 (2.07–2.51) 1.97 (1.81–2.15) 1.28 (1.07–1.54)
 College or more 33,557 Reference Reference Reference Reference
Employment
 Employed, student, retired, or homemaker 76,672 Reference Reference Reference Reference
 Unemployed or unable to work 9213 1.43 (1.31–1.57) 1.51 (1.37–1.66) 1.46 (1.32–1.61) 1.07 (0.88–1.30)
Income ($)
 < 15,000 5833 1.31 (1.17–1.47) 2.14 (1.88–2.44) 1.24 (1.08–1.42) 0.79 (0.61–1.02)
 15,000– < 25,000 10,111 1.35 (1.23–1.47) 2.11 (1.90–2.35) 1.28 (1.15–1.43) 0.91 (0.74–1.12)
 25,000– < 35,000 7301 1.22 (1.11–1.35) 1.63 (1.44–1.84) 1.23 (1.09–1.40) 0.99 (0.77–1.26)
 35,000– < 50,000 11,011 1.26 (1.16–1.36) 1.56 (1.40–1.73) 1.24 (1.12–1.37) 0.94 (0.78–1.15)
 50,000+ 42,008 Reference Reference Reference Reference
 Other** 9621 1.00 (0.92–1.09) 1.37 (1.22–1.54) 0.86 (0.77–0.96) 1.03 (0.83–1.28)
Open in a new tab

Model odds ratios adjusted for all other variables in the table.

a

Binge drinking among males = 5+ drinks.

Bivariate analysis found a significant difference between transgender and cisgender males for current cigarette smoking (UOR 1.64 1.07–2.52), but this was no longer significant after adjusting for covariates. Among males, no other significant differences were found by gender identity in either bivariate or multivariate analyses.

For both males and females (Tables 2–3), binge drinking was associated with ever and current cigarette use and ever e-cigarette use, but not current e-cigarette use. Depression was associated with all tested tobacco use among both males and females. No past 30-day exercise was associated with increased likelihood of cigarette use and ever e-cigarettes use among females, but among males the association was only for cigarette use. Not being non-Hispanic white was largely protective, with the exception of multiracial adults. Lower education and unemployment were associated with cigarette use. Unemployment was associated with ever e-cigarette use overall and among males, but not among females. Having a lower income was associated with cigarette use and ever e-cigarette use, but not current e-cigarette use.

4. Discussion

This is one of the first examinations of the sexual and gender minority tobacco disparity to adjust for binge drinking, lack of exercise, and depression diagnosis in multivariate analysis.This study shows that while transgender adults, compared to those who are cisgender, are significantly more likely to currently smoke cigarettes and ever use e-cigarettes, when adjusted for other factors, there was no longer a significant difference between these two groups, in line with Meyer et al. (2017). This finding may be due to the small transgender sample size or it may suggest that the association is explained by model covariates, such as those related to the other factors including socio-demographics and SES. There was a significant relationship between gender minority identity and current cigarette smoking among males adults, but this relationship was no longer significant after adjusting for other factors. For sexual minorities, the cigarette and e-cigarette use disparities found in bivariate analyses persisted after adjusting for demographics, socioeconomic status, and other factors. This indicates that sexual minorities face cigarette and e-cigarette use disparities, compared to their straight peers, that are not accounted for by the factors included in this study. Together these findings indicate the potential utility for LGBT-specific tobacco public education and other intervention efforts. Future researchers might also consider including factors such as exposure to tobacco marketing in analysis.

Sex-stratified analyses provide an opportunity to better understand the relationship between different identities and tobacco product use; though looking at identity intersections makes analysis more sensitive and complicated. Lesbian/gay females, but not gay males, were more likely than their straight peers to ever smoke cigarettes. However, gay males and gay/lesbian females were both more likely than their straight counterparts to currently smoke cigarettes and ever use e-cigarettes. Among bisexuals, differences by sex are also shown by tobacco use behavior. Bisexual females were more likely than their straight peers to report ever and current cigarette smoking and ever use e-cigarettes, whereas bisexual males were more likely to report ever or current e-cigarette use, compared to straight males. This may indicate that factors related to ever and current cigarette smoking may differ and between cigarette smoking and use of e-cigarettes, a newer product. These findings also contribute to a larger body of research that shows sexual minority tobacco use disparities that are significant among females, but not males. Minority stress and related tobacco use can be compounded by intersections of discrimination by sex, sexual identity, and race. For example, female sexual minorities can face discrimination based on both their sex and sexual identity. Use among bisexuals may different from those who are gay/lesbian as bisexuals can face simultaneous stigma from those who are straight and those who are gay or lesbian. Bisexual females seem to be particularly at risk for negative outcomes (Johnson et al., 2016; Cochran et al., 2016; Blosnich et al., 2013; Corliss et al., 2008). LGBT-focused interventions should consider tailoring product-specific content to meet the needs of LGBT subgroups at higher risk for that product.

Literature links minority stress with tobacco use, binge drinking, and depression diagnosis. In this study, tobacco use was significantly associated with both binge drinking and depression. Binge drinking was more common among sexual minorities and associated with cigarette smoking and ever e-cigarette use, but not current e-cigarette use. Depression diagnosis was more prevalent among gender and sexual minorities and was associated with all four tobacco use indicators. Minority stress may be not just directly related to tobacco use, but also indirectly through these other factors. Ultimately, interventions to reduce the fundamental causes of unhealthy behavior, such as minority stress, could greatly improve sexual and gender minority health. Where this is not feasible, interventions that build resiliency, social support, and tobacco-free norms may help reduce LGBT tobacco use.

4.1. Limitations

There are a number of limitations in this study. The 2016 BRFSS instrument only assesses use of cigarettes, e-cigarettes, and smokeless tobacco. Smokeless tobacco product use is low among sexual and gender minorities (Johnson et al., 2016) and was therefore not included in this analysis, but limited research has shown disparities for other tobacco product use (Johnson et al., 2016) (e.g., hookah), indicating a need for more data on sexual and gender minority tobacco use across the full spectrum of product types. The findings of this study are not nationally representative. Only respondents from 26 states in the 2016 BRFSS study were asked questions on sexual and gender identity. Participating states may have been different demographically and in terms of tobacco use prevention and control measures compared to states that did not participate.

Sexual and gender identity measures likely underestimate the prevalence of the population who is part of the LGBT community. Individuals may not feel comfortable self-identifying in a survey, or they may identify in a way that the current measures do not capture. In addition, gender minority individuals may self-identify with a label other than ones included on the list of choices provided in the survey (e.g., gender fluid, gender queer, agender, bi-gender, demi-gender, non-binary, or two spirit). Likewise, sexual minority individuals may use labels other than the L, G, or B to self-identify (e.g., polysexual, queer, or pansexual). Sexual and gender identity measures that provide more comprehensive identity label options, reflective of the broad range of labels used by those in LGBT community, may help address this limitation in future studies. BRFSS is a phone-based survey, which can be biased towards higher SES which may mean that socioeconomically marginalized groups such as those who are transgender are underrepresented (Crissman et al., 2017). The transgender sample size may be too small to power comparative analyses.

While the BRFSS has updated its sex measure from phone interviewers assessing sex using vocal timbre (as early as 1984) to asking respondents to self-identify (as of 2016), the question wording does not specify sex at birth or current gender identity (Riley et al., 2017). Therefore, it cannot be known how this question was interpreted by transgender respondents and whether, for instance, their answers reflected their current gender identity or sex-assigned-at-birth-certificate. Based on variation in responses by transgender identity, some transgender participants seemed to have reported their current gender identity while others their sex-assigned-at-birth-certificate. Among male-to-female transgender respondents, 67.2% responded to the “sex” measure as male and 32.8% female. Among female-to-male transgender respondents, 29.9% reported as male and 70.1% female. Analysis of data with larger samples, use of sex-assigned-at-birth-certificate and current gender identity measures, could help fill important gaps on the intersection between sex, gender identity, and tobacco use.

5. Conclusions

Sexual minority identity was significantly associated with cigarette and e-cigarette use but patterns of results were mixed. Compared to their straight peers, bisexual males, bisexual females, gay males, and gay and lesbian females had different comparative tobacco product use profiles. Better understanding of differences in how minority stress and other unhealthy behavior pathways differ by sexual identity and by gender identity, as well as the influence of other factors, may help better explain these findings to better inform public health responses.

As demonstrated by the BRFSS data, when measures to identify gender and sexual minority individuals are incorporated into large, national studies, a more fine-grained analysis becomes possible. The addition of such measures to other large-scale studies would be useful to continuing efforts to gauge and eventually reduce health disparities in the LGBT population and to facilitate better public health and policy responses to address those gaps. This dataset provided a larger LGBT-identified sample that not only included those who are transgender, those who are sexual minorities, but also supported analyses stratified by sex. Future analyses should build on these findings and test for additive or multiplicative relationships between tobacco use and minority group memberships such as sex, sexual identity, gender identity, race, ethnicity, geography, and socio-economic status.

Supplementary Material

Supplementary Tables

Acknowledgments

The authors are grateful to those who made this analysis possible: colleagues at the Office of Health Communication and Education and the Office of Science at the Center for Tobacco Products, the states who elected to implement the BRFSS Sexual and Gender identity optional module, and the thousands of BRFSS participants who responded to the module measures.

Funding

This work was supported by the Food and Drug Administration, Center for Tobacco Products (FDA/CTP). This publication represents the views of the author(s) and does not represent FDA/CTP position or policy. All authors report no conflicts of interests or competing financial interests.

Abbreviations:

AOR

Adjusted odds ratio

BRFSS

Behavioral risk factor surveillance system

CDC

Centers for Disease Control and Prevention

CI

95% confidence interval

LGBT

Lesbian, gay, bisexual, and transgender

UOR

Unadjusted odds ratio

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.ypmed.2018.05.014.

Footnotes

☆

Disclaimer: This publication represents the views of the author(s) and does not represent FDA/CTP position or policy.

The authors declare there is no conflict of interest.

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