Abstract
Transgender identities are becoming increasingly common in the United States, and existing research provides ample evidence that risky sexual behaviors, substance use, and suicidality are prevalent experiences among transgender persons. Yet, prior research provides little insight into understanding the specific mechanisms that may promote deviant outcomes among transgender persons. Drawing from an aspect of general strain theory, the goal of this study is to examine the extent to which transwomen and transmen vary in risky sexual behaviors, substance use, and suicidality, and to explore the degree to which discrimination – as a source of strain – plays a role within this process. An analysis of data from transgender individuals from the Virginia Transgender Health Initiative Study (THIS) demonstrates that transwomen and transmen significantly diverge in self-reported risky sexual behaviors, substance use, and suicidality. Greater discrimination based on transgender identity relates to significantly increased odds of suicidality and elevated levels of substance use, but does not relate to risky sexual behavior. Overall, effects of discrimination on each outcome are similar for both transwomen and transmen.
Introduction
An individual with a non-normative gender identity is someone who holds a gender identity that is not traditionally accepted by society. Transgender is a general term that encompasses anyone whose gender identity does not correspond to the sex category they were assigned at birth (Serano 2013). From 2006 to 2016, the number of individuals who identified as transgender in the United States doubled from approximately 700,000 to 1.4 million (Flores et al. 2016). With some exceptions, existing studies on this population have tended to focus on health outcomes, generally showing that transgender individuals experience a litany of negative consequences relative to their non-transgender counterparts (e.g., Bauer et al. 2009; Kenagy 2005; Sanchez, Sanchez, and Danoff 2009) including higher rates of HIV (Herbst et al. 2008; Reback and Fletcher 2014) and risky sexual behavior (Eisenberg et al. 2017; Herbst et al. 2008; Kenagy 2005; Operario et al. 2011a). Moreover, a growing body of research has demonstrated that transgender individuals are at a much greater risk of substance use (Day et al. 2017) and suicidality than non-transgender individuals (Clements-Nolle et al. 2006; Herbst et al. 2008).
Overall, it is clear that some types of deviant behaviors are higher among transgender individuals than the general population. Yet, research examining the correlates of risky sex, substance use, and suicidality among transgender individuals is limited in three specific ways. First, although research has established higher rates of some types of deviance among transgender persons, few – if any – studies provide an application of theory to understand these patterns. Second, given the significant amount of discrimination often faced by transgender individuals (Bockting 2013; Su et al. 2016), is it unclear how and why discrimination plays a role in understanding risky sex, substance use, and suicidality among transgender persons. Third, to our knowledge, research has yet to examine whether patterns of substance use, risky sex, and suicidality significantly vary between adults who transitioned from a male identity to a female identity (transwomen) compared to those who transitioned from female to male (transmen). To address these three significant limitations, this study draws from a modified version of general strain theory (Agnew 2013; see Willits 2019).
At its core, general strain theory (Agnew 1992) asserts that individuals who experience strain – such as the failure to achieve goals, removal of positively valued stimuli, and presence of negative experiences – may engage in deviant behavior as a result. While the original general strain theory posits that this relationship is transmitted through negative affective states (Agnew 1992), there is evidence that emotional responses do not fully explain the relationship between strain and deviance (Willits 2019). Instead, strain may directly promote deviance. Given high rates of stigma and discrimination among transgender individuals (Bockting 2013), it is likely that experiencing discrimination based on ones’ transgender identity may promote deviant behaviors among transgendered persons. When viewed through general strain theory, discrimination based on non-normative gender identity (as a source of strain) may result in deviant behaviors such as risky sexual behavior, substance use, and suicidality, though these trends may vary based on specific identities (transwomen compared to transmen).
Overall, the preceding discussion highlights the pressing need to understand the link between perceived discrimination and deviant behaviors (risky sexual, substance use, and suicidality) among individuals who identity as transgender. Drawing from a modified version of general strain theory (Agnew 2013; Willits 2019), we use data from the 2005 Virginia Transgender Health Initiative Study (THIS) to examine the link between transgender identity and deviance with specific attention given to core differences between transgender persons who have transitioned their identity from male-to-female (transwomen) or female-to-male (transmen).
Deviance among transgender individuals
Risky sexual behavior
There is ample evidence highlighting that transgender persons engage in elevated levels of risky sexual behavior – and at an earlier age – than non-transgender individuals. For example, a study by Van Devanter et al. (2011) found that transwomen adolescents often reported resorting to sex work as a means of financial support. Moreover, stigma (discrimination) was often cited as a root cause of both risky sexual behavior and drug use. Wilson and colleagues (2010) found that transwomen youth were less likely to use a condom with their main sexual partner than other youth, and substance use often increased risky sexual behaviors (e.g., failure to use protection). It is also important to note that sexual education classes in schools often fail to address the identities and experiences of transgender youth, which could have negative consequences for their sexual safety and health into adulthood (Human Rights Campaign 2015).
As transgender individuals age into adulthood, there is continued evidence that they partake in risky sexual behaviors. In a review of HIV prevalence and risky behaviors among transgender individuals, Herbst et al. (2008) found that large percentages of transwomen (ranging from 27% to 48%) reported engaging in behaviors such as unprotected anal intercourse, multiple casual sex partners, sex while drunk or high, and sex work (Herbst et al. 2008). Although there is a dearth of studies specifically examining differences between transwomen and transmen individuals, evidence suggests that some behaviors may be lower among transmen; a study examining transmen specifically found that 66% reported abstaining from sex, or only having one sex partner in the last 6 months (Clements-Nolle et al. 2001). However, 31% of transmen reported participating in sex work in this timeframe (Clements-Nolle et al. 2001). Among transwomen adults, exposure to transphobia was associated with greater odds of having unprotected receptive anal intercourse (Sugano, Nemoto, and Don Operario 2006). Additionally, transwomen having unprotected sex with a male primary partner was associated with lower self-efficacy to use condoms (Operario et al. 2011b).
Substance use
Patterns of substance use among transgender individuals also tend to begin at a young age. Transgender youth have a higher prevalence of substance use than non-transgender youth (Day et al. 2017). Heightened levels of substance use are found among transgender adults as well. One study using the National Transgender Discrimination Survey found that approximately 26% of all respondents reported misusing drugs or alcohol to cope with transgender-related discrimination (Klein and Golub 2016). One qualitative study among Black/African-American transwomen individuals by Crosby and Pitts (2007) found that every transwomen transgender participant reported using hormones through non-medical means.
Discrimination in healthcare is related to a litany of adverse health effects among transgender individuals (Bauer et al. 2009; Kenagy 2005; Sanchez, Sanchez, and Danoff 2009). Since transgender individuals are highly discriminated against in the healthcare arena, they may not have the means to obtain life-saving hormone therapy legally. This kind of hormone use was also accompanied by needle sharing (Crosby and Pitts 2007) which may promote adverse health effects like HIV. Reback and Fletcher (2014) reported that HIV-positive transwomen were significantly more likely to report recent methamphetamine use and lifetime injection drug use than HIV-negative transgender women. Additionally, self-reported injection drug use was associated with an increase in the odds of reporting HIV-positive status (Reback and Fletcher 2014).
Suicidality
As with the first two outcomes, suicidal ideation and attempted suicide – taken together as suicidality – are also prevalent among transgender youth. In one study of transgender youth by Grossman and D’Augelli (2007), almost half of the sample reported suicidal ideation, with one-quarter of the sample reporting suicide attempts. Suicidal ideation related to transgender identity specifically was significantly related to making a suicide attempt (Grossman and D’Augelli 2007). Another study found that prevalence of suicidal ideation in the last year was twice as high among transgender youth compared to non-transgender youth, and transgender youth had 2.99 higher odds of reporting suicidal ideation than non-transgender youth (Perez-Brumer et al. 2017). Similarly, depressive symptoms and victimization were significantly associated with higher odds in suicidal ideation among transgender youth (Perez-Brumer et al. 2017).
These patterns of suicidality seem to be universal for transgender adults as well. In a sample of transgender adults, younger age, gender-based discrimination, and gender-based victimization were all independently and significantly associated with attempted suicide (Clements-Nolle et al. 2006). Studies have found a wide range of transgender adults reporting pervasive suicidality. One study of nonwhite transgender individuals found that 38% of transgender adults reported suicidal ideation, with 63% of these individuals attributing this ideation to gender issues, and half of them making suicide attempts (Xavier et al. 2005). Another study of transgender adults found that gender-based victimization in high school was associated with a history of suicide attempts across the life span (Goldblum et al. 2012).
Overall, the patterns are clear: Transgender individuals are at a significantly elevated risk of engaging in risky sexual behaviors, substance use, and suicidality. Yet, understanding the specific theoretical processes by which this occurs remains unclear, and, consequently, researchers have little understanding on the etiology of these behaviors. Further, we know little about core differences in this process between individuals who transitioned from male-to-female relative to female-to-male. We now place the findings discussed above within the framework of general strain theory (Agnew 1992).
General strain theory and transgender persons
General strain theory
Agnew’s (1992) original general strain theory draws from Merton’s (1938) institutional anomie theory and reformulates it to focus on three individual-level sources of strain: failure to achieve positively valued goals, removal of positively valued stimuli, and presence of negative stimuli. Within the context of the present study, experiences of discrimination due to an individual’s transgender identify clearly fits into each of the three non-mutually exclusive forms of individual-level
The first form of strain is derived from Merton’s (1938) theory which conceptualizes strain as the “failure to achieve positively valued goals.” General strain adds to this conceptualization by acknowledging that people have personal goals beyond what is culturally subscribed, and if these personal goals are not achieved it can also be a source of strain. In the context of transgender individuals, experiencing discrimination due to ones’ transgender identity may result in the blockage of some cultural and personal goals. For example, research has established that transgender persons report experiencing discrimination in receiving adequate healthcare (e.g., Bauer et al. 2009; Kenagy 2005; Sanchez, Sanchez, and Danoff 2009) which could capture the failure to achieve the goal of positive mental and physical health.
The second major type of strain outlined in Agnew’s (1992) theory is the “removal of positively valued stimuli.” This type of strain encompasses experiences in which individuals lose relationships or experiences they once valued. A person with a non-normative gender identity could experience a loss of valued relationships (e.g., Klein and Golub 2016) or employment as others may not accept their transgender identity and may end their affiliation with them. The third source of strain described in Agnew’s (1992) theory is the “presence of negative stimuli” which represents undesirable events or circumstances such as an unpleasant work environment, rules/regulations that one does not wish to follow, or victimization (e.g., Ford and Schroeder 2008; Mowen, Tolles, and Schroeder 2019). Research has established that individuals with a transgender identity are likely to experience violence and economic discrimination due to their identity (Lombardi et al. 2002) thus representing the presence of negative stimuli.
Overall, when viewed through the lens of general strain theory, it is clear that discrimination experiences may operate as a source of strain. Empirically, prior research suggests that discrimination may be particularly important in understanding the occurrence of risky sex, substance use, and suicidality among transgender individuals thus adding further evidence to the theoretical application of this theory to the current study. For example, research suggests risky sexual behavior and drug use are used as coping mechanisms for feelings of stigma – which may result from discrimination experiences – among transwomen adolescents (Van Devanter et al. 2011), and discrimination relates to decreased odds of condom use among transwomen (Operario et al. 2011b). Elevated levels of suicidality among transgender persons (Perez-Brumer et al. 2017), may also be a likely outcome of discrimination experiences. Likewise, high prevalence of needle sharing among transgender individuals (Crosby and Pitts 2007) may be the result of discrimination in the medical arena, thereby suggesting that the discrimination experiences may be a particularly powerful form of strain leading to these deviant outcomes.
Finally, it is worth noting that whereas general strain theory (Agnew 1992) posits that the effect of strains on deviant coping are transmitted via negative affect (e.g., anger, depression, anxiety), research on the role of negative affect is significantly mixed with a great deal of work finding a lack of support for this aspect of the theory (see a discussion of this by Willits 2019). In a modified version of general strain theory, as posited by Agnew (2013) and outlined by Willits (2019), it is argued that general strain theory can be broken into two specific theoretical processes. One aspect of the theory can be used to test individual variation in outcomes among those who have experienced strain, while the second aspect of the theory can be used to explain situational behavior as a matter of negative affect. As we discuss below in the methods section, the data used in this study limit our ability to fully test general strain theory as originally formulated (e.g., Agnew 1992) and, instead, we draw from the discussion of Willits (2019) to test the former aspect of general strain or the idea that “that certain individuals experiencing certain types of strain in certain circumstances are likely to engage in criminal coping” (661). In this case, we expect that transgender persons experiencing discrimination are likely to engage in the deviant coping mechanisms of risky sexual behavior, substance use, and suicidality.
In addition to testing the direct effects of strain on deviant coping, it is also possible that the effects of strain will diverge between transmen and transwomen. For example, individuals who transition their identity from having “lower” social status to “higher” social status (transmen) or vice versa (transwomen) may be exposed to new and/or different types of strain due to their new gender roles and the abandonment of their previous roles. For example, using slang or terms that were socially acceptable to use as a woman might not be appropriate for a man to say in public spaces, and could lead an individual to be more vigilant about their language (Schilt 2010: 53–54). Additionally, having one gendered history but having another gendered social identity may lead to culture shock and cause an individual to struggle to integrate their two identities, both past and present (Schilt 2010: 52). Given some evidence that trends in deviance diverge between transwomen and transmen, it is possibly that the theoretical process will unfold differently between these two groups. Consequently, research is sorely needed in this area to provide much-needed theoretical clarity and an empirical understanding on how patterns of risky sexual behavior, substance use, and suicidality differ between transwomen and transmen, thus raising attention to the goals of the current study.
Current study
The broad goal of this study is to examine the link between discrimination among individuals with non-normative gender identities and risky sexual behavior, substance use, and suicidality. More specifically, when viewed through the lens of general strain theory (Agnew 2013) it is likely that discrimination may lead to antisocial outcomes. To examine this topic, we use data from the Virginia Transgender Health Initiative Study, and ask two research questions with corresponding hypotheses. First, do transwomen and transmen differ in their levels of risky sexual behavior, substance use, and suicidality? Based on prior research, we expect (hypothesis one) that transwomen will report higher levels of each deviant behavior compared to transmen. Second, do feelings of discrimination vary in their effects on deviant outcomes for transwomen compared to transmen? We expect (hypothesis two) that discrimination will be significantly more impactful on deviant outcomes for transwomen compared to transmen.
Methods
Data
The data for this project come from the Virginia Transgender Health Initiative Study (THIS). THIS was commissioned by the Virginia Department of Health HIV Community Planning Committee with the overall goal to learn more about the health needs of transgender individuals in the state of Virginia, and to use these data to improve health outcomes within this population. THIS data were collected in 2005 and 2006. As a cross-sectional, community-based sample, THIS encompasses individuals aged 18 and older who identified themselves as transgender and lived or went to school in the state of Virginia in 2005 and/or 2006.
An initial qualitative survey, consisting of focus group data from transgender individuals, was implemented on a small scale to inform later phases of the survey questionnaire. Transgender support groups, informal peer networks, and service providers were used to recruit participants. The administrators recruited participants from all of Virginia’s health districts (Testa et al. 2012). As an incentive, participants were given 15 USD for completion of the questionnaire. Questionnaires were administered by paper or online at a secure website. Participants were eligible for the survey based on the following operationalization: persons who have lived or want to live full-time in a gender opposite their birth or physical sex; persons who have or want to physically modify their body to match who they feel they truly are; or persons who have or want to wear the clothing of the opposite sex in order to express their inner identity (Testa et al. 2012). The final sample consisted of 350 respondents from 60 cities and counties in Virginia.
Transgender identity
Because one of the keys goals of this study is to examine how specific transgender identity (transwomen or transmen) relates to each outcome, we include a variable that captures whether the individual identifies as transwomen or transmen. This variable was created by using the questions “What is your present gender identity? Check one only” and “What was your physical, assigned sex at birth? Check one only” and resulted in a binary variable of male to female (transwomen) and female to male (transmen) categories. Overall, 65.4% of the sample were transwomen (coded “1”) and 34.6% were transmen (coded “0”). Given the specific focus on differences between transwomen and transmen individuals, we present all measures used in the forthcoming analysis for each group with corresponding t-tests, shown in Table 1.
Table 1.
Descriptive statistics of the virginia transgender health initiative study (THIS).
| Variable | Transwomen (n = 229) |
Transmen (n = 121) |
Range | t statistica | ||
|---|---|---|---|---|---|---|
| Mean | SD | Mean | SD | |||
| Dependent Measures | ||||||
| Risky Sexual Behavior | 0.429 | 0.496 | 0.336 | 0.475 | 0, 1 | −1.640 |
| Substance Use | 3.096 | 2.649 | 3.919 | 3.006 | 0–12 | 2.296* |
| Suicidality | 0.573 | 0.496 | 0.781 | 0.416 | 0, 1 | 4.054*** |
| Source of Strain | ||||||
| Discrimination Experiences | 0.690 | 1.037 | 0.694 | 1.023 | 0–4 | 0.037 |
| Control Measures | ||||||
| Income | 7.281 | 3.047 | 6.424 | 2.802 | 1–11 | −2.593* |
| Race | −1.244 | |||||
| Nonwhite | 0.406 | 0.492 | 0.339 | 0.475 | 0, 1 | |
| White (ref.) | 0.594 | 0.492 | 0.661 | 0.475 | 0, 1 | |
| Age | 3.048 | 1.312 | 2.066 | 1.101 | 1–5 | −7.415*** |
| Education | 1.181 | |||||
| Less than High School | 0.100 | 0.301 | 0.008 | 0.091 | 0, 1 | |
| High School | 0.166 | 0.373 | 0.107 | 0.311 | 0, 1 | |
| Some College (ref.) | 0.336 | 0.473 | 0.554 | 0.499 | 0, 1 | |
| College | 0.389 | 0.489 | 0.331 | 0.472 | 0, 1 | |
| Living Arrangement | −1.244 | |||||
| Living Alone | 0.31 | 0.464 | 0.248 | 0.434 | 0, 1 | |
| Living with Others (ref.) | 0.69 | 0.464 | 0.752 | 0.434 | 0, 1 | |
| Employment | −0.568 | |||||
| Employed | 0.689 | 0.464 | 0.658 | 0.476 | 0, 1 | |
| Unemployed (ref.) | 0.311 | 0.464 | 0.342 | 0.476 | 0, 1 | |
SD = Standard Deviation, (ref.) = reference category, n= sample size
t statistic compares transwomen to transmen
p ≤.05
p ≤.01
p ≤.001
Dependent measures
Risky sexual behavior
The first dependent variable used in this study is risky sexual behavior. Although risky sex can include a wide range of behaviors, we considered any individual who reported being sexually active outside of a stable relationship as engaging in risky sex (for a review on defining risky sexual behavior, see Chawla and Sarkar 2019). To accomplish this coding schema, we drew from multiple questions indicating that the respondent was: a) sexually active; and, b) did not have a primary partner; and/ or, c) had a partner, but was not monogamous. Overall, transwomen report higher levels of risky sexual behavior than transmen (42.9% compared to 33.6%), although this difference is not statistically significant.
Substance use
The second dependent measure encompasses substance use. To create this measure, we drew data from 12 questions that asked each respondent about substance use including: marijuana, heroin, powder cocaine, alcohol, crack cocaine, hallucinogens, club drugs, methamphetamine, PCP, poppers, downers, or painkillers.1 We coded these variables as “1” if they had ever used each substance and “0” if they had never used them. The Kuder–Richardson test statistic for these items was 0.854, suggesting strong inter-item reliability, and items were summed to create a scale. The mean level of substance use is 3.096 for transwomen (SD = 2.659), which is significantly lower (p<.05) than the mean level of substance use for transmen at 3.919 (SD = 3.006).
Suicidality
The final dependent variable in this study is suicidality. This variable was created by combining responses to the questions “Have you ever thought about killing yourself?” and “Have you ever tried to kill yourself?” into a binary variable where a yes response to either question was coded as 1. We describe these combined phenomena as suicidality, and overall, transwomen report significantly lower levels (p< .001) of suicidality (57.3%) relative to transmen (78%).
Discrimination
In line with general strain theory, we include a composite measure of strain (Agnew 1992) that encompasses experiences of discrimination. We created this index using five questions: 1) “Have you ever been denied enrollment in a health insurance plan because of your transgender status?” 2) “Have you ever experienced discrimination by a doctor or other healthcare provider due to your transgender status or gender expression?” 3) “Have you ever been denied a job you applied for due to your transgender status and/or gender expression?” 4) “Have you ever been fired from a job due to your employer’s reaction to your transgender status and/or gender expression?” and 5) “Have you ever lost housing or a housing opportunity due to your transgender status and/or gender expression?” We coded each of these questions as “1” for yes and “0” for no. We operationalized this variable as a composite index, with higher scores representing more discrimination and lower scores indicating less discrimination.2 Mean levels of discrimination were not significantly different between transwomen and transmen individuals with a mean of .690 for transwomen (SD = 1.037) and .694 for transmen (SD = 1.023).
Independent control variables
To protect against omitted variables bias, we include additional control measures in our models. To assess socioeconomic status, we include an income variable that captures household income from the previous year. This measure ranges from 1) “I have no source of income” to 11) “$100,000 or more.” The mean for transwomen (7.281; SD = 3.047) is significantly higher than the mean for transmen (6.424; SD = 2.802). For race, we include a dichotomous variable where 1 is nonwhite and 0 is white. Overall, 40.6% of transwomen were coded as nonwhite and 33.9% of transmen were coded as nonwhite. These differences were not statistically significant.
We also account for education level. Education ranges from 1) “less than high school” to 5) “master’s and above.” Mean levels of education were not significantly different between transwomen (3.233; SD = 1.246) and transmen (3.372; SD = .914). This means that, on average, individuals in the sample have some college education. For the analyses, we include education as a series of dichotomous variables (less than high school, high school, and college), using some college education as the reference group. We also account for respondent age (1 = 18–24, 2 = 25–34, 3 = 35–44, 4 = 45–54, 5 = 55 and older). The mean for transwomen (3.049) is significantly higher than the mean for transmen (2.066), with standard deviations of 1.312 and 1.101, respectively. Thus, on average, transwomen are between 35 and 44 years old while transmen are between 25 and 34 years old.
To account for living arrangement, we use a dichotomous variable coded 1 if the respondent was living alone and 0 if they were not living alone. We include this variable to account for the fact the LGBTQ+ individuals are at heightened risk of social isolation due to their lower likelihood to have children and/or higher likelihood of being estranged from their families (Klinenberg 2016). Overall, 31.0% of transwomen and 24.8% of transmen were coded as living alone. These means are not significantly different from each other. Finally, we include a variable for employment status coded 1 if respondents were working full or part time and 0 if they were unemployed. The means for employment are not significantly different between transwomen and transmen respondents with 68.8% of transwomen and 65.8% of transmen reporting being employed.
Analytic strategy
Due to missing data on one or more of the variables used in our analysis, we rely on data from 313 respondents representing about 89% of the original sample in our models with risky sexual behavior, 278 individuals (about 79% of the original sample) in our models with substance use, and 323 respondents in our models for suicidality (about 92% of the original sample). To address our research questions, we use a multivariate logistic regression for the two binary outcomes (risky sexual behavior and suicidality) and a negative binomial count regression for the count measure (substance use).3 For ease of interpretation, we present odds ratios (logistic regression) and incident risk ratios (negative binomial regression), and proceed in two ways. First, we present a model that examines differences in each outcome between transwomen and transmen transgender individuals, net the effect of the control variables. Second, we introduce an interaction term between transgender identity (transwomen) and discrimination to assess whether discrimination is more impactful on antisocial outcomes for transwomen individuals than transmen. To create the interaction term, we grand mean center each variable and multiplied them together.
Results
Risky sexual behavior
The logistic regression in Model 1 of Table 2 regresses risky sexual behavior onto the independent measures. The model is significant (Wald χ2 38.50, p< .001). Results of the first model reveal that transwomen individuals report significantly higher odds of engaging in risky sexual behavior than transmen individuals. Specifically, compared to transmen, transwomen report 96.2% higher odds of engaging in risky sexual behavior, which aligns with our expectations. Additionally, income is moderately associated with risky sex such that individuals with higher incomes report moderately lower odds of risky sexual behavior. Results also reveal that age is significantly associated with increased odds of risky sexual behavior by 22.1% per category; older individuals report greater odds of risky sexual behavior. Finally, those who live alone are about three times as likely (OR = 3.057) to engage in risky sexual behavior relative to those who do not live alone.
Table 2.
Logistic regression models assessing risky sexual behavior (n= 313).
| Variable | Model 1 |
Model 2 |
Model 3 |
|||
|---|---|---|---|---|---|---|
| OR | RSE | OR | RSE | OR | RSE | |
| Transgender Identity | ||||||
| Transwomen | 1.962 | 0.591* | 1.956 | 0.589* | 1.967 | 0.593* |
| Source of Strain | ||||||
| Discrimination Experiences | - | - | 0.937 | 0.118 | 0.937 | 0.121 |
| Interaction Term | ||||||
| Transwomen × Discrimination | - | - | - | - | 1.129 | 0.301 |
| Control Measures | ||||||
| Income | 0.914 | 0.050+ | 0.913 | 0.050+ | 0.912 | 0.050+ |
| Nonwhite | 1.402 | 0.386 | 1.409 | 0.389 | 1.415 | 0.390 |
| Age | 0.779 | 0.095* | 0.778 | 0.094* | 0.779 | 0.095* |
| Education | ||||||
| Less than High School | 1.260 | 0.767 | 1.324 | 0.826 | 1.262 | 0.803 |
| High School | 0.721 | 0.305 | 0.727 | 0.307 | 0.72 | 0.303 |
| College | 0.633 | 0.201 | 0.628 | 0.200 | 0.634 | 0.202 |
| Living Alone | 3.057 | 0.863*** | 3.036 | 0.859*** | 3.020 | 0.854*** |
| Employed | 1.132 | 0.316 | 1.115 | 0.321 | 1.111 | 0.321 |
| Intercept | 1.078 | 0.488 | 1.113 | 0.541 | 1.157 | 0.543 |
| Wald χ2 | 38.50*** | 38.52*** | 38.52*** | |||
OR = Odds Ratio, RSE = Robust Standard Error, n= sample size
p ≤.10,
p ≤.05,
p ≤.01,
p ≤.001
In line with general strain theory, which suggests that experienced discrimination, as a source of strain, exacerbates risky sexual behavior, Model 2 introduces this measure. Results of this model demonstrate that discrimination is not associated with risky sexual behavior and the substantive results of the other measures are consistent with those presented in the prior model. To investigate our third hypothesis, Model 3 includes an interaction between transwomen and our discrimination index. Contrary to our expectations, this model reveals that experienced discrimination is not more influential on risky sexual behavior for transwomen than transmen.
Substance use
Next, we turn to examining the link between substance use and the independent variables, the results of which are shown in Table 3. Results of Model 1 demonstrate that transwomen report a 26.5% lower count of substance use than transmen. Age is moderately related to substance use such that older respondents report slightly more substance use than younger respondents. Those who have a high school degree have a 37.5% lower count of substance use, and individuals with a college degree report a 30.8% lower count of substance use than those with some college education. In line with general strain theory, Model 2 introduces the measure of discrimination experiences. Discrimination experiences are marginally (p= .059) associated with substance use, suggesting that perceptions of discrimination do relate to substance use within this sample. Model 3 presents the interaction term between gender identity and discrimination. As with the risky sexual behavior model, the interaction term was not significant, meaning that discrimination does not have a significantly different impact for transwomen and transmen on substance use. When this interaction term is included, perceived discrimination moves from marginal significance to being significantly associated with substance use (p = .034).
Table 3.
Negative binomial regression models assessing substance use (n= 278).
| Variable | Model 1 |
Model 2 |
Model 3 |
|||
|---|---|---|---|---|---|---|
| OR | RSE | OR | RSE | OR | RSE | |
| Transgender Identity | ||||||
| Transwomen | 0.735 | 0.080** | 0.737 | 0.081** | 0.744 | 0.081** |
| Source of Strain | ||||||
| Discrimination Experiences | - | - | 1.088 | 0.049+ | 1.095 | 0.047* |
| Interaction Term | ||||||
| Transwomen × Discrimination | - | - | - | - | 1.132 | 0.103 |
| Control Measures | ||||||
| Income | 1.002 | 0.019 | 1.002 | 0.020 | 1.001 | 0.197 |
| Nonwhite | 1.009 | 0.111 | 1.021 | 0.112 | 1.014 | 0.111 |
| Age | 1.082 | 0.049+ | 1.080 | 0.048+ | 1.014 | 0.111 |
| Education | ||||||
| Less than High School | 1.050 | 0.286 | 1.010 | 0.268 | 0.981 | 0.252 |
| High School | 0.625 | 0.127* | 0.609 | 0.120* | 0.603 | 0.120* |
| College | 0.692 | 0.078*** | 0.704 | 0.079** | 0.710 | 0.080** |
| Living Alone | 0.984 | 0.107 | 1.007 | 0.109 | 1.003 | 1.090 |
| Employed | 1.074 | 0.111 | 1.092 | 0.112 | 1.101 | 0.113 |
| Intercept | 3.701 | 0.639*** | 3.429 | 0.605*** | 3.44 | 0.609*** |
| Wald χ2 | 26.16*** | 30.02*** | 32.69*** | |||
IRR = Incident Risk Ratio, RSE = Robust Standard Error, n= sample size
p ≤.10,
p ≤.05,
p ≤.01,
p ≤.001
Suicidality
Finally, we examine the relationship between the independent variables and suicidality. A logistic regression presented model in Table 4 regresses suicidality onto the independent variables, and this model is significant (Wald χ2 34.10, p < .001). Results reveal that transwomen individuals report significantly lower levels of suicidality than transmen individuals. Specifically, being transwomen is associated with 51.8% lower odds of suicidality than being transmen. Results further reveal that being nonwhite is associated with 59.2% lower odds of suicidality. These patterns extend to the second model, which steps in the measure of perceived discrimination. Findings reveal that scoring one unit higher on perceived discrimination is associated with 47.3% greater odds of suicidality. In the third model, the interaction term is not significant, indicating that discrimination does not moderate the link between specific transgender identity and suicidality.
Table 4.
Logistic regression models assessing suicidality (n= 323).
| Variable | Model 1 |
Model 2 |
Model 3 |
|||
|---|---|---|---|---|---|---|
| OR | RSE | OR | RSE | OR | RSE | |
| Transgender Identity | ||||||
| Transwomen | 0.482 | 0.141* | 0.476 | 0.142* | 0.441 | 0.141* |
| Source of Strain | ||||||
| Discrimination Experiences | - | - | 1.473 | 0.219** | 1.550 | 0.261** |
| Interaction Term | ||||||
| Transwomen × Discrimination | - | - | - | - | 0.724 | 0.289 |
| Control Measures | ||||||
| Income | 1.001 | 0.052 | 1.001 | 0.055 | 1.003 | 0.055 |
| Nonwhite | 0.408 | 0.111*** | 0.389 | 0.107*** | 0.390 | 0.108*** |
| Age | 0.923 | 0.105 | 0.929 | 0.106 | 0.924 | 0.106 |
| Education | ||||||
| Less than High School | 0.32 | 0.183* | 0.217 | 0.129* | 0.236 | 0.139* |
| High School | 0.604 | 0.233 | 0.544 | 0.224 | 0.548 | 0.224 |
| College | 0.931 | 0.305 | 0.985 | 0.327 | 0.970 | 0.322 |
| Living Alone | 0.829 | 0.227 | 0.834 | 0.234 | 0.853 | 0.236 |
| Employed | 0.838 | 0.244 | 0.897 | 0.266 | 0.907 | 0.267 |
| Intercept | 7.762 | 3.904*** | 5.928 | 3.142*** | 6.080 | 3.233*** |
| Wald χ2 | 34.10*** | 40.30*** | 38.82*** | |||
OR = Odds Ratio, RSE = Robust Standard Error, n= sample size
p ≤.10,
p ≤.05,
p ≤.01,
p ≤.001
Discussion
The goal of this project was to apply one aspect of general strain theory (Willits 2019) to examine the link between discrimination and risky sexual behavior, substance use, and suicidality among transgender individuals. Specifically, this project considered differences in these outcomes for transwomen and transmen individuals. To address this topic, we used data from the Transgender Health Initiative Survey (THIS) from 2005 to 2006 and a series of logistic regression and negative binomial count models to examine our research questions. We now turn back to our hypotheses and extant literature to explore the key findings of this study.
Our first hypothesis, that those who transitioned their identity from male to female (transwomen) would report higher levels of risky sexual behavior, substance use, and suicidality than those who transitioned from female to male (transmen), received mixed support. In support of this hypothesis, findings demonstrated that transwomen individuals reported much higher odds of engaging in risky sexual behavior than transmen. One potential explanation for this finding could be that there is a higher prevalence of sex work among transwomen relative to transmen (e.g., Herbst et al. 2008). And, in fact, supplemental descriptive analysis of the THIS data confirm this pattern; although only 5% of the sample indicated that their current or most recent source of income was sex work, 94% of these individuals were transwomen and only 6% were transmen.
On the other hand, results of our analyses examining substance use and suicidality did not support our hypothesis. In fact, we found the opposite as transwomen individuals reported significantly lower levels of substance use and suicidality than transmen. Although this is the first study to examine these outcomes between transwomen and transmen, there are some potential explanations for these findings. One potential explanation for the higher prevalence of these behaviors among transmen is that individuals who transitioned into male categories may engage in higher types of some deviant behaviors to fit into heteronormative masculine norms – such as substance use. Substance use is perceived as a traditionally masculine behavior, even in adolescence (Wilkinson et al. 2018). It has also been found that men who are highly committed to the “male role” show more severe alcohol and drug use than men who are not highly committed to these gender stereotypes (Lash, Copenhaver, and Eisler 1998). Therefore, in an effort to exhibit a more masculine persona, transmen individuals may be intensifying their substance use behaviors to align with masculine norms. Future research should examine this link more thoroughly.
Likewise, we also found that transwomen reported lower levels of suicidality than transmen, which was not what we had hypothesized. Adolescent transmen have been found to have higher rates of suicide attempts than transwomen, non-binary, questioning, female, and male adolescents (Toomey et al. 2018). This pattern among adolescent transmen could extend into their adult years, and therefore could be an explanation as to why transmen individuals in this study experienced higher levels of suicidality. Further research should examine the contextual factors that contribute to these trends.
Finally, our second hypothesis posited that experienced discrimination would be significantly more impactful on deviant outcomes for transwomen compared to transmen. We did not find support for this hypothesis for any of our three outcomes. Although our findings indicate divergent patterns of deviance between transwomen and transmen, it is possible that experiences of discrimination operate similarly irrespective of specific transitions to male or female identities. Future research should consider additional sources of strain beyond discrimination that are likely to influence this population, such as family relationships or victimization (Guadalupe-Diaz and Anthony 2016), and how these may operate distinctly based on specific transgender identities.
This study moves the literature forward in a few key ways. Although general strain theory has been applied to a wide range of innovative contexts and behaviors including military service (Mowen, Tolles, and Schroeder 2019), whistle blowing (Kolbel and Herold 2019), teen pregnancy (Walker and Holtfreter 2019), and weapon possession (Brady, Baker, and Pelfrey 2019), this study is the first to apply this perspective to transgender identities and deviance. In testing the linkage between strain (experiences of discrimination) and deviant outcomes, we found support for one aspect of general strain theory as experiences of discrimination was linked to both suicidality and substance use. Although we conceptualize discrimination experiences as potentially falling into each of the three non-mutually exclusive categories of strain – failure to achieve positively valued goals (e.g., failure to achieve adequate health care), removal of positively valued stimuli (e.g., losing housing or employment), and presence of negative stimuli (which is likely captured by the overall composite measure of discrimination used in this study) – future research should more clearly examine individual’s subject reactions to each of these types of experiences. As Willits notes (2019: 667) individuals must “interpret such strains as high in magnitude and unjust … “ to turn to deviant coping. Future research should examine – qualitatively – how transgender persons interpret specific forms of discrimination to better elucidate the effect each has on behavior. For example, it is possible that the loss of a job may exert stronger feelings of unjustness than receipt of inadequate health care. At present, the current study is unable to offer insight into these specific possibilities.
In addition to expanding the theoretical understanding of the relationship between discrimination experiences and deviance among transgender persons, this study also provides evidence that transmen and transwomen have differing patterns of deviant behaviors, and studies could benefit by examining outcomes among transmen and transwomen separately. While it is common to combine many different non-normative gender identities into one category, this project shows that there is utility in separating out groups in order to understand trends between specific gender identity transitions.
Despite the contributions of this study, there are some notable limitations. First, the data used in this study come from the state of Virginia and may not be generalizable to the overall transgender population in the United States, particularly in light of research that has established regional variations in discrimination based on sexuality and identity (e.g., Costello, Rukus, and Hawdon 2019). Although THIS data were drawn from 60 counties in Virginia, it is possible that more disadvantaged individuals or those living in rural areas are underrepresented. Future research should explore the link between discrimination experiences and deviant outcomes among transgender persons in broader and more representative contexts. Second, although a core contribution of this study is the focus on understanding how outcomes vary between transwomen to transmen individuals, scholars have highlighted the need to recognize sexuality and gender on a spectrum (Sumerau 2010); unfortunately, data limitations preclude our ability to move beyond binary pathways within this population. Third, the substance use scale used in this study captures those who had ever used substances. It is possible that respondents may not currently be using substances or could have used them only once. Thus, this variable could be subject to recall bias. Similarly, our measure of risky sexual behavior was constructed as those who have been sexually active within the last 6 months, do not have a primary partner, and are not in a monogamous relationship, but it could include individuals who use protection. However, given the heightened prevalence of HIV among transgender individuals that was a factor in conducting THIS, having multiple partners is likely a risky sexual behavior.
In addition to data and measurement limitations, this study also has important theoretical limitations. First, there are likely many mechanisms that matter when understanding risky sexual behavior, substance use, and suicidality among transgender individuals including levels of family support, peer influence, key life events, and more. Future research on these outcomes (and other deviant outcomes) among transgender individuals should use additional theoretical frameworks that examine these core mechanisms. Although there has been recent calls to further refine general strain theory (e.g., Willits 2019), data limitations precluded us from testing the influence of negative affect on the relationship between strain and deviant outcomes. Thus, while we find a direct effect of discrimination experiences on deviant outcomes, it is possible that negative affect still plays a key role within the theoretical process of general strain. Future research should examine additional theoretical dimensions of general strain theory. Similarly, future research should apply additional theories of deviance such as social control, social learning, and life-course theory to examine deviant outcomes within this population. Finally, and echoing our above sentiments, there are likely numerous sources of strain among transgender persons such as victimization (see Guadalupe-Dias and Anthony 2017) that should be considered within additional theoretical perspectives.
As highlighted by this study, transwomen and transmen individuals significantly differ in their deviant outcomes, and these disparities may not be what are expected based on the current literature. When viewed through the lens of one aspect of general strain theory, understanding these differences is important in order to accurately target policy, programs, and healthcare interventions where it is most needed among various transgender populations. As evidenced by this study, transgender individuals may be particularly affected by their social environment, and these effects vary based on their specific identity (transwomen or transmen). In light of the fact that a growing number of people are identifying themselves as transgender (Flores et al. 2016), these findings are an essential first step in understanding core factors and theoretical processes that help explain deviant outcomes among transgender persons.
Funding
This research was supported in part by the Center for Family and Demographic Research, Bowling Green State University, which has core funding from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (P2CHD050959).
Footnotes
Because alcohol is not illegal, we engaged in a sensitivity analysis to examine whether the results held without alcohol in the scale. The substantive findings were the same and including alcohol in the scale improved model fit. As a result, we retained alcohol use in this measure.
We conducted t-tests for each individual item in the discrimination index by transgender identity (transwomen compared to transmen). Only item 5 (“Have you ever lost housing or a housing opportunity due to your transgender status and/or gender expression?”) was statistically significant. Thus, while transwomen and transmen do not vary by the overall index, there is slight variation around individuals’ items.
A negative binomial model is used over a Poisson model due to over dispersion in the dependent variable as shown by a likelihood ratio test of the overdispersion parameter alpha.
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