PMCCPMCCPMCC

Search tips
Search criteria 

Advanced

 
Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
Drug Alcohol Depend. Author manuscript; available in PMC 2016 September 1.
Published in final edited form as:
PMCID: PMC4536098
NIHMSID: NIHMS709353

Differences in Alcohol Use and Alcohol-Related Problems between Transgender- and Nontransgender-identified Young Adults

Abstract

Background

Little is known about differences in alcohol use and alcohol-related problems between transgender- and nontransgender-identified populations. Using data from a large-scale health survey, we compare the drinking patterns and prevalence of alcohol-related problems of transgender-identified individuals to nontransgender-identified males and females. For transgender-identified people, we examine how various forms of victimization relate to heavy episodic drinking (HED).

Methods

Cross-sectional surveys were completed by 75,192 students aged 18–29 years attending 120 post-secondary educational institutions in the United States from 2011–2013. Self-reported measures included alcohol use, alcohol-related problems, victimization, and sociodemographics, including 3 gender-identity groups: transgender-identified individuals; nontransgender-identified males; and nontransgender-identified females.

Results

Compared to transgender-identified individuals, nontransgender-identified males were more likely to report HED in the past 2 weeks (relative risk=1.42; p=0.006); however, nontransgender-identified males and females reported HED on fewer days than transgender-identified people (incidence-rate ratios [IRRs] ranged from 0.28–0.43; p-values<0.001). Compared to transgender-identified people, nontransgender-identified males and females had lower odds of past-year alcohol-related sexual assault and suicidal ideation (odds ratios ranged from 0.24–0.45; p-values<0.05). Among transgender-identified people, individuals who were sexually assaulted (IRR=3.21, p=0.011) or verbally threatened (IRR=2.42, p=0.021) in the past year had greater HED days than those who did not experience those forms of victimization.

Conclusions

Compared to transgender-identified people, nontransgender-identified males and females: have fewer HED occasions (despite nontransgender-identified males having greater prevalence of HED); and are at lower risk for alcohol-related sexual assaults and suicidal ideation. Experiences of sexual assault and verbal threats are associated with greater HED occasions for transgender-identified people.

Keywords: Transgender, alcohol use, heavy episodic drinking, alcohol-related problems, violence

1. INTRODUCTION

After systematically documenting the dearth of research on transgender populations, the landmark Institute of Medicine report on the health of lesbian, gay, bisexual, and transgender populations recommended expanding research on the health of transgender populations (Institute of Medicine, 2011). The term transgender is commonly used as an umbrella term for people whose gender identity or expression does not conform with the sex they were assigned at birth (Institute of Medicine, 2011; The GenIUSS Group, 2014). Transgender populations often face multiple forms of discrimination and victimization, including physical, verbal, and sexual assault (Institute of Medicine, 2011; Stotzer, 2009), which can negatively impact their wellbeing and may result in maladaptive coping, such as heavy alcohol use (Keyes et al., 2011; Meyer, 2003).

Few epidemiologic surveillance systems collect information about transgender status (Institute of Medicine, 2011; The GenIUSS Group, 2014), limiting information that compares the drinking behaviors of transgender people to their cisgender counterparts (i.e., people whose gender identity and expression match their assigned sex at birth). One study of United States youth (aged 13–18 years) found that transgender youth had higher odds of lifetime alcohol use than cisgender youth, but were no different in their prevalence of regular alcohol use (i.e., alcohol use at least monthly in the past year) (Reisner et al., 2014a). Another study of Massachusetts adults (aged 18–64 years) found no differences in the prevalence of past-month heavy episodic use between transgender and cisgender populations (Conron et al., 2012). The remaining few studies about transgender individuals’ alcohol use utilized convenience-based samples or lacked direct cisgender comparison groups (Bradford et al., 2013; Gilbert et al., 2014; Hotton et al., 2013; Reisner et al., 2013; Rowe et al., 2015). Therefore, we have incomplete knowledge about drinking differences between transgender and cisgender populations, especially among young adults, the age group most at risk for heavy alcohol use in the United States (Centers for Disease Control and Prevention, 2010b; Fryar et al., 2006; Substance Abuse and Mental Health Services Administration, 2011; United States Department of Health and Human Services, 2009).

Alcohol use, especially heavy episodic drinking, can lead to acute problems—like blackouts, suicides, and sexual assaults (Darvishi et al., 2015; Perkins, 2002; White, 2003)—which contribute to multiple leading causes of death among young adults (Centers for Disease Control and Prevention, 2010a). In young adulthood, the prevalence of alcohol-related problems sometimes vary by gender (American College Health Association, 2012; Perkins, 2002). According to studies where transgender status was not assessed, males were more likely get into trouble with police, while females were more likely to experience sexual assault (Perkins, 2002). Meanwhile, blackouts and alcohol-related suicidality are equally common among males and females (American College Health Association, 2012; Perkins, 2002). Yet, little to no information exists about differences in alcohol-related problems for transgender people compared to their cisgender counterparts.

A burgeoning body of literature shows that, compared to cisgender people, transgender individuals have a higher risk of experiencing victimization (Grant et al., 2011; Institute of Medicine, 2011; Kosciw et al., 2012; Reisner et al., 2014a), which can induce psychological distress and lead to alcohol use as a way of coping (Keyes et al., 2011; Meyer, 2003). While this phenomenon has been shown in multiple empirical studies of alcohol use among transgender populations (Bradford et al., 2013; Gilbert et al., 2014; Hotton et al., 2013; Reisner et al., 2013; Rowe et al., 2015), there is a paucity of research about how certain forms of assault (i.e., physical, verbal, and sexual) are associated with heavy episodic drinking for transgender people. In studies where transgender status was not measured, different types of victimization have diverse effects on alcohol use (Begle et al., 2011; Tyler et al., 2012). For example, in a longitudinal study of adolescents, experiences of sexual and physical abuse predicted subsequent high-risk behavior (including alcohol use) for boys, and sexual abuse was the primary driver of subsequent high-risk behavior for girls (Begle et al., 2011). These results demonstrate how associations between victimization and alcohol use vary by gender, substantiating the need for a within-group analysis among transgender populations.

The purpose of this study was to examine alcohol use and alcohol-related problems for transgender young adult populations using data from a large epidemiologic surveillance system of college and university students in the United States. We examined differences in drinking behaviors and the prevalence of alcohol-related problems by gender identity, comparing transgender-identified people to their nontransgender-identified counterparts. We also examined how various forms of victimization were related to heavy episodic drinking among transgender-identified people.

2. MATERIAL AND METHODS

2.1. Study Design

The American College Health Association (ACHA) administered the National College Health Assessment Survey (NCHA) at postsecondary educational institutions. Data were compiled into a national dataset if schools: elected to administer the NCHA and pay a fee to ACHA for the self-administered questionnaire; and surveyed randomly selected students, surveyed students in randomly selected classrooms, or surveyed all students at their school (American College Health Association, 2012, 2013, 2014). The current investigation used cross-sectional survey data from schools participating in the Fall 2011 (44 schools), Fall 2012 (51 schools), or Fall 2013 (57 schools) surveys. In total, the sample included data from 120 unique higher education institutions. According to the Carnegie Classification of Institutions of Higher Education (Center for Postsecondary Research, 2011), 30.8% of the participating schools were Doctorate-granting Universities, 30.8% were Master’s Colleges and Universities, 26.7% were Baccalaureate Colleges, 10.0% were Associate’s Colleges, and 1.7% were Special Focus Institutions. Half were public schools, and half were private schools. Regarding location in the United States, 37.5% of participating schools were in the Northeast, 29.2% were in the South, 17.5% were in the Midwest, and 15.8% were in the West.

Students completed either paper or web surveys, depending on the format offered at their institution. Response rates were high for paper surveys (mean response proportions ranged from 71% to 100% from 2011–2013) but lower for web surveys (mean response proportions ranged from 16% to 21% from 2011–2013). Respondents anonymously completed surveys during a specific time period selected by each school’s administration. Institutions obtained approval of study procedures from their own Institutional Review Board (IRB).

2.2. Measures

2.2.1. Sociodemographics

Gender identity was measured with the following item: “What is your gender?” The 3 response options were: (1) “female;” (2) “male;” and (3) “transgender.” To emphasize each participants’ self-reported gender identity based on to this measure, we refer to each group as nontransgender-identified females, nontransgender-identified males, and transgender-identified people. Other sociodemographic measures were age (dichotomized as 21+ vs. under 21 to reflect the legal drinking age in the United States), race/ethnicity (White vs. non-White), sexual orientation (heterosexual, gay/lesbian, bisexual, unsure), education level (undergraduate vs. graduate student), employment (none vs. employed for at least 1 hour per week), current living situation (on-campus or off-campus), and membership in a fraternity or sorority (yes/no).

2.2.2. Victimization

Four forms of violence were measured dichotomously (yes/no) in the past 12 months. Physical fights, physical assaults, and verbal threats were measured individually with single items. Sexual assault was measured with 3 items after the initial stem “Within the last 12 months…”: (1) “were you sexually touched without your consent”; (2) “was sexual penetration attempted (vaginal, anal, oral) without your consent”; and (3) “were you sexually penetrated (vaginal, anal, oral) without your consent.” If participants responded yes to any of these questions, we coded them as having been sexually assaulted.

2.2.3. Alcohol Use

Participants were asked about their past-month drinking behaviors with the following item: “Within the last 30 days, on how many days did you use alcohol (beer, wine, liquor)?” Response options included: never used; have used, but not in the last 30 days; 1–2 days; 3–5 days; 6–9 days; 10–19 days; 20–29 days; used daily. From this item, we created three different variables: lifetime alcohol use (dichotomous yes/no, where “no” was represented by “never used”); past-month alcohol use (dichotomous yes/no, where “no” was represented by “never used” and “have used, but not the last 30 days”); and number of past-month drinking days (range: 0–30 days). For the latter variable, we created a continuous count variable by assigning midpoint values to categories with ranges (e.g., 1–2 days was coded as 1.5), as has been done previously (Corliss et al., 2008).

Participants were also asked about heavy episodic drinking, which was measured with the following item: “Over the last two weeks, how many times have you had five or more drinks of alcohol at a sitting?” Response options included “don’t drink,” “none,” “1 time,” “2 times,” through “10 or more times.” From this item, we created two different variables: any heavy episodic drinking; and number of heavy episodic drinking days in past 2 weeks, because responses generally followed a Poisson distribution (range: 0–10 days).

2.2.4. Alcohol-related Problems

We examined 7 alcohol-related problems. The question stem was: “Within the last 12 months, have you experienced any of the following when drinking alcohol?” Specific items included the following: did something you later regretted; forgot where you were or what you did; had unprotected sex; physically injured yourself; got in trouble with the police; someone had sex with me without my consent (i.e., sexual assault); and seriously considered suicide (i.e., suicidal ideation). We coded responses dichotomously yes/no: “no” if participants selected the options “N/A, don’t drink” or “no;” and “yes” if participants selected “yes.”

2.3. Data Analyses

Since young adults (age 18–29) have a higher prevalence of heavy episodic drinking than any other age group in the United States (Centers for Disease Control and Prevention, 2010b; Fryar et al., 2006; Naimi et al., 2003; Substance Abuse and Mental Health Services Administration, 2011), we restricted our current study to include only respondents in this age group. This removed 9,419 participants (including 33 transgender-identified students). After excluding the few participants who indicated they were neither undergraduate nor graduate students (113 participants indicated they were “not seeking a degree” and 389 indicated being some “other” kind of student), the sample included 79,054 participants.

In any particular analysis, we excluded participants if they were missing data on independent or dependent variables. Overall, missing data were minimal, with the largest proportion of missingness being 1.5% for membership in a fraternity or sorority. We removed 50 participants who were missing data for every alcohol use and alcohol-related problem variable. Of the remaining participants, we removed 4.8% of participants who were missing data on any independent variables, creating an analytic sample of 75,192 participants. To maintain the largest sample size possible, we allowed our sample sizes to vary using listwise deletion for each alcohol use and alcohol-related problem variable.

We conducted data analyses in Stata version 13 (College Station, TX). Data were collected from students nested within schools; to account for non-independence of observations, we used Rao-Scott corrected chi-square tests for bivariate analyses with categorical variables, and cluster-robust standard errors for multivariable regression models (StataCorp, 2013). Also, we controlled for sociodemographics in all multivariable models.

First, we examined the differences in drinking patterns by gender identity in the whole sample. To examine gender-identity differences in dichotomous alcohol use variables (i.e., lifetime alcohol use, past-month alcohol use, and heavy episodic drinking in the past 2 weeks), we used the modified Poisson regression approach to estimate risk ratios for transgender-identified people compared to nontransgender-identified female and male students, separately (Zou, 2004). To examine gender-identity differences in continuous alcohol use variables (i.e., number of past-month drinking days, number of heavy episodic drinking days), we used zero-inflated negative binomial models due to statistically significant likelihood-ratio tests of alpha=0 (p-values<0.05 for all models; indicating overdispersed data) and Vuong’s tests (p-values<0.05 for all models) (Vuong, 1989). Second, we examined the differences in alcohol-related problems by gender. For this, we restricted to participants who reported drinking in their lifetime. We fit models using logistic regression for each alcohol-related problem.

Within transgender-identified populations, we examined how victimization correlated with the number of heavy episodic drinking days. We used negative binomial regression because the data were overdispersed according to the likelihood-ratio test of alpha=0 (p-values<0.001), but Vuong’s test was not significant (p-value=0.248).

3. RESULTS

In total, there were 175 transgender-identified individuals, 50,465 nontransgender-identified females, and 24,552 nontransgender-identified males. Table 1 shows the sociodemographic characteristics by gender. Compared to transgender-identified people, nontransgender-identified females were more likely to be White, heterosexual, and fraternity/sorority members. Nontransgender-identified males were more likely than transgender-identified people to be heterosexual, graduate students, employed, and fraternity/sorority members.

Table 1
Sociodemographics and victimization by gender identity: National College Health Assessment Surveys, Fall 2011–2013, United States

Table 1 also shows the prevalence of victimization by self-identified gender identity. Overall, transgender-identified people were significantly more likely to have been in physical fights, physically assaulted, verbally threatened, and sexually assaulted than both their nontransgender-identified counterparts. For example, 21.1% of transgender-identified people reported being victims of sexual assault in the past year, compared to 8.5% of nontransgender-identified females and 3.5% of nontransgender-identified males.

3.1. Drinking Patterns by Gender Identity

As shown in Table 2, lifetime alcohol use was reported by 79.9% of transgender-identified people, 75.8% of nontransgender-identified females, and 74.8% of nontransgender-identified males. Past-month drinking was reported by 60.3% of transgender-identified, 62.1% of nontransgender-identified females, and 63.1% of nontransgender-identified males. Adjusting for sociodemographics, transgender-identified individuals and nontransgender-identified males and females had similar risk of lifetime and past-month drinking (RRs range from 0.96–1.04; p-values>0.05). Yet nontransgender-identified females drank on significantly fewer days than transgender-identified people in the past month (incidence-rate ratio [IRR]=0.69; p=0.006).

Table 2
Unadjusted estimates and adjusted comparisons of drinking patterns by gender identity: National College Health Assessment Surveys, Fall 2011–2013, United States

Heavy episodic drinking in the past 2 weeks was reported by 27.4% of transgender-identified people, 28.1% of nontransgender-identified females, and 40.9% of nontransgender-identified males. In adjusted analyses, nontransgender-identified males were significantly more likely to report heavy episodic drinking than transgender-identified people, but there were no differences between transgender-identified people and nontransgender-identified females. However, nontransgender-identified males and females reported heavy episodic drinking on significantly fewer days than transgender-identified people (IRRs range from 0.28–0.43; p-values<0.001).

3.2. Alcohol-related Problems by Gender Identity

As shown in Table 3, nontransgender-identified drinkers had lower odds of reporting alcohol-related sexual assaults than transgender-identified drinkers (odds ratios [ORs] ranged from 0.24–0.45; p-values<0.05). Nontransgender-identified drinkers also had significantly lower odds of suicidal ideation while drinking compared to transgender-identified drinkers (ORs range from 0.37–0.42; p-values<0.01). On the other hand, nontransgender-identified males had higher odds than transgender-identified people of forgetting where they were or what they did while drinking (OR=1.68; p=0.014). Transgender-identified drinkers were no different from nontransgender-identified drinkers with regards to doing something they later regretted, getting in trouble with the police, having unprotected sex, or physically injuring themselves while drinking.

Table 3
Unadjusted estimates and adjusted comparisons of alcohol-related problems among lifetime drinkers by gender identity: National College Health Assessment Surveys, Fall 2011–2013, United States

3.3. Correlates of Heavy Episodic Drinking Days Among Transgender-identified People

Table 4 shows how past-year victimization is correlated with the number of heavy episodic drinking days among transgender-identified people, controlling for sociodemographics. Transgender-identified individuals who were verbally threatened (IRR=2.42, p=0.021) or sexually assaulted (IRR=3.21, p=0.011) had more heavy episodic drinking days than their transgender-identified peers who did not experience those stressors.

Table 4
Correlates of heavy episodic alcohol use for transgender-identified people: National College Health Assessment Surveys, Fall 2011–2013, United States

4. DISCUSSION

Our study addresses gaps identified in the Institute of Medicine report (Institute of Medicine, 2011), including differences in alcohol use and alcohol-related problems between transgender-identified individuals and their nontransgender-identified counterparts. Transgender-identified people have a similar prevalence of heavy episodic drinking to nontransgender-identified females, but are less likely to use than nontransgender-identified males; however, transgender-identified people who engaged in heavy episodic drinking do so more frequently than their nontransgender-identified counterparts. This result is particularly striking because young adult males were previously found to be among the heaviest drinkers in the United States (Centers for Disease Control and Prevention (CDC), 2012; Substance Abuse and Mental Health Services Administration, 2013).

Sexual assaults in the past year were more common among transgender-identified people than nontransgender-identified people, which is notable because nontransgender-identified females in the general population disproportionately experience sexual assault victimization (Basile et al., 2007; Krebs et al., 2007; Tjaden and Thoennes, 1998, 2000). Being sexually assaulted was strongly associated with a greater number of heavy episodic drinking days for transgender-identified people, which dovetails with previous research showing that sexual assault exacerbated drinking (Begle et al., 2011; Keyes et al., 2011; Tyler et al., 2012). Furthermore, alcohol-related sexual assaults were elevated among transgender-identified drinkers compared to their nontransgender-identified counterparts. Together these findings suggest a potential stress-response feedback loop, where the response (i.e., heavy episodic drinking) to a stressor (i.e., sexual assault) can increase exposure to subsequent stressors. Though we could not determine directionality of these findings, previous studies showed both that victimization can induce drinking (Begle et al., 2011; Keyes et al., 2011; Tyler et al., 2012) and that drinking environments can foster aggressive behaviors (Abbey, 2002; Cunradi et al., 2012; Mair et al., 2013; Studer et al., 2014), which may contribute to the sexual assault of transgender-identified people.

Suicidal ideation while drinking was also much more common among transgender-identified people than nontransgender-identified individuals. Previous studies suggest that transgender-identified people are at great risk for depression and suicidality (Bockting et al., 2005; Clements-Nolle et al., 2006; Reisner et al., 2013, 2014b), and many people who are suicidal use alcohol for its disinhibiting effects in order to harm themselves (Pompili et al., 2010). Moreover, alcohol is a depressant that can negatively change one’s mood (Valenzuela, 1997), making the precise relationship between alcohol use and suicidal ideation in our cross-sectional study unclear. Nevertheless, alcohol may exacerbate underlying mental health problems for transgender-identified people, making them more vulnerable to self-inflicted premature mortality than their nontransgender-identified counterparts.

The differences in heavy episodic drinking frequency between transgender-identified nontransgender-identified people are a serious public health problem, and our findings can inform primary prevention efforts. For transgender-identified individuals, we found the number of heavy episodic drinking days was associated with verbal threats and sexual assaults, and therefore eliminating—or at least reducing—the victimization of transgender-identified people may mitigate their heavy episodic drinking. Because transgender-identified people comprise a small proportion of the entire population—0.2% of our sample, which is comparable to national estimates (Gates, 2011)—a cost-effective way to reduce their drinking disparities may be to explicitly incorporate transgender and gender nonconformity issues into existing drinking and violence intervention prevention programs.

Our findings can also inform secondary prevention efforts. Both alcohol programs (e.g., brief interventions and treatment) and assault survivor services should be competent about transgender issues. This includes training staff on transgender competency and being inclusive of transgender people in all practices. In general, there are scant transgender-tailored alcohol treatment programs (Cochran et al., 2007), therefore creating transgender-inclusive programs is necessary. For survivor services, providers ought to understand that experiences of violence may lead to hazardous drinking behaviors, and implementing Screening, Brief Intervention, and Referral to Treatment (SBIRT) programs within survivor services should be considered. Conversely, general SBIRT programs should also incorporate assessment of assault experiences.

Our study propels the field forward, but much about transgender alcohol use remains unexamined. For example, there is scant information about transgender alcohol use over time. Longitudinal studies that include transgender status are necessary to discern mediators of heavy episodic drinking disparities. Heavy episodic drinking and related problems may also be influenced by transgender-specific factors, such as interpersonal transgender discrimination, internalized transphobia, stage of transition, and hormone replacement therapy. Additionally, structural stigma—institutionalized policies and laws that restrict any minority population’s opportunities, resources, or well-being (Link and Phelan, 2001)—may contribute to higher levels of heavy episodic drinking, as has been demonstrated among lesbian, gay, and bisexual populations (Hatzenbuehler et al., 2009, 2010, 2012). Although we examined gender differences in acute alcohol-related problems, long-term heavy alcohol use can lead to chronic morbidities, such as liver cirrhosis (Rehm et al., 2003; Rehm and Shield, 2014; Rehm et al., 2010), which we were unable to examine. Finally, there are likely intersections of gender identity with race/ethnicity, age, and sexual orientation; but we chose not to examine these effects because of the small number of transgender-identified respondents and the large number of covariates present in our models.

Though our findings are from a large epidemiologic study, they are not without limitations. One limitation concerns the measurement of gender identity in this survey. Based on the gender identity question asked in the NCHA, we were unable to discern whether transgender-identified people transitioned from male to female or female to male, conflating them into a monolithic group. Because of this, misclassification may have occurred: some cisgender people may have mistakenly selected the transgender option; and some transgender people may have selected the female or male options. As an example of the latter, some individuals who transitioned from male to female or female to male may not identify as transgender, but instead identify as female or male, respectively (Institute of Medicine, 2011; The GenIUSS Group, 2014). We were unable to identify these individuals because the NCHA did not include items measuring participants’ sex assigned at birth. We recommend future surveillance efforts utilize questions from the recent recommendations for identifying transgender people from the Gender Identity in U.S. Surveillance group (The GenIUSS Group, 2014).

All measures in our study were self-reported, and therefore the data were not externally validated. Because of the way we recoded the number of drinking days and heavy episodic drinking occasions, there may be misclassification for the alcohol use variables. Furthermore, NCHA defined heavy episodic drinking as 5 or more drinks for all participants, regardless of gender. Some studies define heavy episodic drinking behaviors based on a respondent’s gender (e.g., some studies consider 4 or more drinks for women to be heavy episodic drinking; National Institute on Alcohol Abuse and Alcoholism, 2015). Nevertheless, there is no recommended definition of heavy episodic drinking for transgender-identified people, therefore, the standard measure for all participants irrespective of gender was a strength of our study. Also, victimization items only captured experiences from the past year, and previous studies suggest that experiences of victimization earlier in the life-course may impact alcohol use later in life (Bontempo and d’Augelli, 2002; Rosario et al., 2009; Talley et al., 2014). Finally, we did not have data about institutional level factors that directly relate to transgender status (e.g., school climate for transgender people) or alcohol use and related problems (e.g., alcohol outlets near campus), and our results may be confounded by these unmeasured variables.

The overall study design also limited our findings. Due to the cross-sectional nature of this study, we could not discern the directionality of the correlates of heavy episodic drinking for transgender-identified people. Data were also drawn from a sample of undergraduate and graduate students at non-randomly sampled universities; therefore, findings may not generalize to students at other institutions or individuals not attending post-secondary institutions. Furthermore, our study is not free from selection bias, particularly because our sample included a larger proportion of nontransgender-identified females than males; a previous study with students showed that females were more likely to respond to surveys than males (Sax et al., 2003), but we were unable to examine gender differences in response rates. Also, participation rates were less than optimal for web surveys, which is a common artifact of web survey administrations (Cook et al., 2000). Survey administration time periods were not standardized across schools, and our results may be affected by schools’ selected time periods (e.g., before or after holiday breaks). Because the specific dates of survey completion were not publicly available, we could not assess the extent of this effect. Finally, 24 schools participated in more than one survey administration, and because surveys were anonymous, we could not be certain that individuals only participated in one survey period. In spite of our study’s inherent limitations, the NCHA rigorously sampled individuals and collected multiple measures of alcohol use and related problems, allowing for close examinations of drinking patterns and consequences in a population at high-risk for hazardous alcohol use and alcohol-related problems.

Altogether, for people who engage in heavy episodic drinking, transgender-identified people imbibe much more frequently than their nontransgender-identified counterparts. Combined with the finding that transgender-identified people are at elevated risk for several alcohol-related problems, transgender-identified people may have a high risk of morbidity and mortality in young adulthood and possibly across the lifespan. Public health researchers and practitioners must develop effective interventions that reduce victimization and marginalization of transgender-identified people to help improve their overall health and wellbeing.

Highlights

  • Heavy episodic drinking (HED) differs among transgender and nontransgender people.
  • Sexual assault after drinking is higher for transgender than nontransgender people.
  • Suicidality after drinking is higher for transgender than nontransgender people.
  • For transgender people, sexual assault is associated with greater HED days.
  • For transgender people, verbal threats are associated with greater HED days.

Acknowledgments

Role of Funding Source

This research article was supported in part by the National Institute on Drug Abuse (awards F31DA037647 to R.W.S.C. and R01DA037568 to R.D.S.) and Department of Veterans Affairs (VA) Office of Academic Affiliations and the Center for Health Equity Research and Promotion at the VA Pittsburgh Healthcare System (postdoctoral fellowship to J.R.B.). The American College Health Association (ACHA) administered the data collection process for this study, and were among the first organizations to measure transgender-identified people in their surveillance system. The opinions expressed in this work are those of the authors and do not necessarily represent those of our funders, the post-secondary educational institutions included in this study, the ACHA, the VA, or the United States Government.

Footnotes

Conflicts of Interest

None

Contributors

Robert W.S. Coulter led the conceptualization, data analysis, and writing of this manuscript. John R. Blosnich, Leigh A. Bukowski, A. L. Herrick, Daniel E. Siconolfi, and Ron D. Stall contributed substantially to the conceptualization, data interpretation, and writing of this manuscript. All authors have read and approved the submission of this manuscript to Drug and Alcohol Dependence.

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

References

  • Abbey A. Alcohol-related sexual assault: a common problem among college students. J Stud Alcohol Drugs. 2002;14:118–128. [PMC free article] [PubMed]
  • American College Health Association. American College Health Association–National College Health Assessment (ACHA-NCHA) II: Reference group executive summary fall 2011. American College Health Association; Hanover, MD: 2012.
  • American College Health Association. American College Health Association–National College Health Assessment (ACHA-NCHA) II: Reference group executive summary Fall 2012. American College Health Association; Hanover, MD: 2013.
  • American College Health Association. American College Health Association–National College Health Assessment (ACHA-NCHA) II: Reference group executive summary Fall 2013. American College Health Association; Hanover, MD: 2014.
  • Basile KC, Chen J, Black MC, Saltzman LE. Prevalence and characteristics of sexual violence victimization among US adults, 2001–2003. Violence Vict. 2007;22:437–448. [PubMed]
  • Begle AM, Hanson RF, Danielson CK, McCart MR, Ruggiero KJ, Amstadter AB, Resnick HS, Saunders BE, Kilpatrick DG. Longitudinal pathways of victimization, substance use, and delinquency: findings from the National Survey of Adolescents. Addict Behav. 2011;36:682–689. [PMC free article] [PubMed]
  • Bockting W, Huang CY, Ding H, Robinson BB, Rosser BS. Are transgender persons at higher risk for HIV than other sexual minorities? A comparison of HIV prevalence and risks. Int J Transgend. 2005;8:123–131.
  • Bontempo DE, d’Augelli AR. Effects of at-school victimization and sexual orientation on lesbian, gay, or bisexual youths’ health risk behavior. J Adolesc Health. 2002;30:364–374. [PubMed]
  • Bradford J, Reisner SL, Honnold JA, Xavier J. Experiences of transgender-related discrimination and implications for health: Results from the Virginia Transgender Health Initiative Study. Am J Public Health. 2013;103:1820–1829. [PubMed]
  • Center for Postsecondary Research. [Accessed on June 9 2015];The Carnegie Classification of Institutions of Higher Education. 2011 http://carnegieclassifications.iu.edu/
  • Centers for Disease Control and Prevention. [Accessed on March 2 2015];10 Leading Causes Of Death By Age Group, United States – 2010. 2010a http://www.cdc.gov/injury/wisqars/pdf/10LCID_All_Deaths_By_Age_Group_2010-a.pdf.
  • Centers for Disease Control and Prevention. Behavioral Risk Factor Surveillance System Survey Data. U.S. Department of Health and Human Services, Centers for Disease Control and Prevention; Atlanta, Georgia: 2010b.
  • Centers for Disease Control and Prevention (CDC) [Accessed on Jun 6 2014];Behavioral Risk Factor Surveillance System Survey Data. 2012 http://apps.nccd.cdc.gov/brfss/
  • Clements-Nolle K, Marx R, Katz M. Attempted suicide among transgender persons: the influence of gender-based discrimination and victimization. J Homosex. 2006;51:53–69. [PubMed]
  • Cochran BN, Peavy KM, Robohm JS. Do specialized services exist for LGBT individuals seeking treatment for substance misuse? A study of available treatment programs. Subst Use Misuse. 2007;42:161–176. [PubMed]
  • Conron KJ, Scott G, Stowell GS, Landers SJ. Transgender health in Massachusetts: results from a household probability sample of adults. Am J Public Health. 2012;102:118–122. [PubMed]
  • Cook C, Heath F, Thompson RL. A meta-analysis of response rates in web-or internet-based surveys. Educ Psychol Meas. 2000;60:821–836.
  • Corliss HL, Rosario M, Wypij D, Fisher LB, Austin SB. Sexual orientation disparities in longitudinal alcohol use patterns among adolescents: findings from the Growing Up Today Study. Arch Pediatr Adolesc Med. 2008;162:1071. [PMC free article] [PubMed]
  • Cunradi CB, Mair C, Todd M, Remer L. Drinking context and intimate partner violence: evidence from the California community health study of couples. J Stud Alcohol Drugs. 2012;73:731. [PubMed]
  • Darvishi N, Farhadi M, Haghtalab T, Poorolajal J. Alcohol-related risk of suicidal ideation, suicide attempt, and completed suicide: a meta-analysis. PLoS One. 2015;10:1–14. [PMC free article] [PubMed]
  • Fryar CD, Hirsch R, Porter KS, Kottiri B, Brody DJ, Louis T. Smoking and alcohol behaviors reported by adults: United States, 1999–2002. US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Health Statistics; 2006.
  • Gates GJ. How Many People Are Lesbian, Gay, Bisexual And Transgender? The Williams Institute; Los Angeles, CA: 2011.
  • Gilbert PA, Perreira K, Eng E, Rhodes SD. Social stressors and alcohol use among immigrant sexual and gender minority Latinos in a nontraditional settlement state. Subst Use Misuse. 2014;49:1365–1375. [PMC free article] [PubMed]
  • Grant JM, Mottet L, Tanis JE, Harrison J, Herman J, Keisling M. Injustice At Every Turn: A Report Of The National Transgender Discrimination Survey. National Center for Transgender Equality; 2011.
  • Hatzenbuehler ML, Keyes KM, Hasin DS. State-level policies and psychiatric morbidity in lesbian, gay, and bisexual populations. Am J Public Health. 2009:99. [PubMed]
  • Hatzenbuehler ML, McLaughlin KA, Keyes KM, Hasin DS. The impact of institutional discrimination on psychiatric disorders in lesbian, gay, and bisexual populations: a prospective study. Am J Public Health. 2010:100. [PubMed]
  • Hatzenbuehler ML, Pachankis JE, Wolff J. Religious climate and health risk behaviors in sexual minority youths: A population-based study. Am J Public Health. 2012;102:657–663. [PubMed]
  • Hotton AL, Garofalo R, Kuhns LM, Johnson AK. Substance use as a mediator of the relationship between life stress and sexual risk among young transgender women. AIDS Educ Prev. 2013;25:62–71. [PubMed]
  • Institute of Medicine. The Health Of Lesbian, Gay, Bisexual, And Transgender People: Building A Foundation For Better Understanding. National Academies Press; Washington, D.C: 2011. [PubMed]
  • Keyes KM, Hatzenbuehler ML, Hasin DS. Stressful life experiences, alcohol consumption, and alcohol use disorders: the epidemiologic evidence for four main types of stressors. Psychopharmacology (Berl) 2011;218:1–17. [PMC free article] [PubMed]
  • Kosciw JG, Greytak EA, Bartkiewicz MJ, Boesen MJ, Palmer NA. The 2011 National School Climate Survey: The Experiences Of Lesbian, Gay, Bisexual And Transgender Youth In Our Nation's Schools. GLSEN; New York, New York: 2012.
  • Krebs CP, Lindquist CH, Warner TD, Fisher BS, Martin SL. The Campus Sexual Assault (CSA) Study: Final Report. National Institute of Justice, US Department of Justice; Washington, DC: 2007.
  • Link BG, Phelan JC. Conceptualizing stigma. Ann Rev Sociol. 2001:363–385.
  • Mair C, Cunradi CB, Gruenewald PJ, Todd M, Remer L. Drinking context-specific associations between intimate partner violence and frequency and volume of alcohol consumption. Addiction. 2013;108:2102–2111. [PMC free article] [PubMed]
  • Meyer IH. Prejudice, social stress, and mental health in lesbian, gay, and bisexual populations: conceptual issues and research evidence. Psychol Bull. 2003;129:674. [PMC free article] [PubMed]
  • Naimi TS, Brewer RD, Mokdad A, Denny C, Serdula MK, Marks JS. Binge drinking among US adults. JAMA. 2003;289:70–75. [PubMed]
  • National Institute on Alcohol Abuse and Alcoholism. [Accessed on June 10 2015];Drinking levels defined. 2015 http://www.niaaa.nih.gov/alcohol-health/overview-alcohol-consumption/moderate-binge-drinking.
  • Perkins H. Surveying the damage: a review of research on consequences of alcohol misuse in college populations. J Stud Alcohol Drugs. 2002;14:91–100. [PubMed]
  • Pompili M, Serafini G, Innamorati M, Dominici G, Ferracuti S, Kotzalidis GD, Serra G, Girardi P, Janiri L, Tatarelli R. Suicidal behavior and alcohol abuse. Int J Environ Res Public Health. 2010;7:1392–1431. [PMC free article] [PubMed]
  • Rehm J, Gmel G, Sempos CT, Trevisan M. Alcohol-related morbidity and mortality. Alcohol Res Health. 2003;27:39–51. [PubMed]
  • Rehm J, Shield KD. Alcohol and mortality: global alcohol-attributable deaths from cancer, liver cirrhosis, and injury in 2010. Alcohol Res Curr Rev. 2014;35:174. [PMC free article] [PubMed]
  • Rehm J, Taylor B, Mohapatra S, Irving H, Baliunas D, Patra J, Roerecke M. Alcohol as a risk factor for liver cirrhosis: a systematic review and meta-analysis. Drug Alcohol Rev. 2010;29:437–445. [PubMed]
  • Reisner SL, Gamarel KE, Dunham E, Hopwood R, Hwahng S. Female-to-male transmasculine adult health a mixed-methods community-based needs assessment. J Am Psychiatr Nurses Assoc. 2013;19:293–303. [PubMed]
  • Reisner SL, Greytak EA, Parsons JT, Ybarra ML. Gender minority social stress in adolescence: disparities in adolescent bullying and substance use by gender identity. J Sex Res. 2014a;52:243–256. [PMC free article] [PubMed]
  • Reisner SL, White JM, Mayer KH, Mimiaga MJ. Sexual risk behaviors and psychosocial health concerns of female-to-male transgender men screening for STDs at an urban community health center. AIDS Care. 2014b;26:857–864. [PMC free article] [PubMed]
  • Rosario M, Schrimshaw EW, Hunter J. Disclosure of sexual orientation and subsequent substance use and abuse among lesbian, gay, and bisexual youths: critical role of disclosure reactions. Psychol Addict Behav. 2009;23:175. [PMC free article] [PubMed]
  • Rowe C, Santos GM, McFarland W, Wilson EC. Prevalence and correlates of substance use among trans* female youth ages 16–24 years in the San Francisco Bay Area. Drug Alcohol Depend. 2015;147:160–166. [PMC free article] [PubMed]
  • Sax LJ, Gilmartin SK, Bryant AN. Assessing response rates and nonresponse bias in web and paper surveys. Res High Educ. 2003;44:409–432.
  • StataCorp. Stata 13 Base Reference Manual. Stata Press; College Station, TX: 2013.
  • Stotzer RL. Violence against transgender people: a review of United States data. Aggress Violent Behav. 2009;14:170–179.
  • Studer J, Baggio S, Deline S, N’Goran AA, Henchoz Y, Mohler-Kuo M, Daeppen JB, Gmel G. Drinking locations and alcohol-related harm: cross-sectional and longitudinal associations in a sample of young Swiss men. Int J Drug Policy. 2014;26:653–661. [PubMed]
  • Substance Abuse and Mental Health Services Administration. [Accessed on April 25 2013];2010–2011 National Survey on Drug Use and Health. 2011 http://www.samhsa.gov/data/NSDUH/2k11State/NSDUHsae2011/NSDUHsaeUS2011.pdf.
  • Substance Abuse and Mental Health Services Administration. [Accessed on Oct 10 2014];Results from the 2013 National Survey on Drug Use and Health. 2013 http://www.samhsa.gov/data/NSDUH/2013SummNatFindDetTables/DetTabs/NSDUH-DetTabsTOC2013.htm.
  • Talley AE, Hughes TL, Aranda F, Birkett M, Marshal MP. Exploring alcohol-use behaviors among heterosexual and sexual minority adolescents: intersections with sex, age, and race/ethnicity. Am J Public Health. 2014;104:295–303. [PMC free article] [PubMed]
  • The GenIUSS Group. Best Practices For Asking Questions To Identify Transgender And Other Gender Minority Respondents On Population-Based Surveys. The Williams Institute; Los Angeles, CA: 2014.
  • Tjaden P, Thoennes N. Prevalence, Incidence, And Consequences Of Violence Against Women: Findings From The National Violence Against Women Survey. National Institute of Justice Centers for Disease Control and Prevention; Washington, DC: 1998.
  • Tjaden PG, Thoennes N. Extent, Nature, And Consequences Of Intimate Partner Violence. US Department of Justice, Office of Justice Programs, National Institute of Justice; Washington, DC: 2000.
  • Tyler KA, Gervais SJ, Davidson MM. The relationship between victimization and substance use among homeless and runaway female adolescents. J Interpers Violence. 2012;28:474–493. [PubMed]
  • United States Department of Health and Human Services, Substance Abuse and Mental Health Services Administration, Office of Applied Studies. [Accessed on July 9 2013];National Survey on Drug Use and Health, 2010. 2009 http://www.oas.samhsa.gov/nsduh/2k10MRB/2k10Q.pdf.
  • Valenzuela CF. Alcohol and neurotransmitter interactions. Alcohol Health Res World. 1997;21:144–148. [PubMed]
  • Vuong QH. Likelihood ratio tests for model selection and non-nested hypotheses. Econometrica. 1989;57:307–333.
  • White AM. What happened? Alcohol, memory blackouts, and the brain. Alcohol Res Health. 2003;27:186–196. [PubMed]
  • Zou G. A modified poisson regression approach to prospective studies with binary data. Am J Epidemiol. 2004;159:702–706. [PubMed]