State Policies and Healthcare Use Among Transgender People in the United States

Abstract

Introduction:

The introduction and passing of restrictive and protective transgender-specific state policies has increased over the past decade. These policies are critical for the health of transgender and other gender diverse (TGGD) people; however, little is known about the relationship between these policies and healthcare use, and the role that race/ethnicity plays in this relationship.

Methods:

Analysis was conducted in 2018-2019 using multilevel modeling and data from the 2015 U.S. Trans Survey (conducted by the National Center for Transgender Equality among nearly 28,000 TGGD people across the United States) to explore associations between transgender-specific state policies and healthcare avoidance due to fears of mistreatment. State policies included those related to experiences of discrimination, health insurance coverage, and changing legal documents. Restrictive and protective policies were measured individually and as a composite index. The relationship between race/ethnicity and healthcare use was also examined to determine if there were differences in the association between race/ethnicity and healthcare avoidance by state.

Results:

None of the individual policies were associated with healthcare use, but the composite policy index was significant, such that living in states with more protective policies was associated with reduced odds of avoiding healthcare due to fears of mistreatment. The relationship between race/ethnicity and healthcare also varied across states.

Conclusions:

Findings suggest the importance of advocating for more protective transgender-specific policies to improve healthcare access for TGGD people in the United States, particularly for TGGD people of color.

Introduction

Over the past decade, the number of transgender-specific policies in the United States (U.S.) has increased, including protective and discriminatory policies affecting transgender and other gender diverse (e.g., gender non-binary, gender queer; TGGD) people.¹ These policies influence a range of social institutions and include, for example, policies that determine health insurance coverage, limit access to public restrooms, and provide protections against discrimination within education, employment, housing, and public accommodations. However, little research has examined the relationships between transgender-specific policies and TGGD people’s healthcare avoidance, especially across U.S. states and while considering the role of race/ethnicity.

Building on White-Hughto et al.’s² socio-ecological framework of transgender-related stigma, stigmatizing policies are conceptualized at a structural level, and as contributing to and/or reflecting stigmatizing social norms by limiting power and marginalizing TGGD people. Transgender-specific stigmatizing policies can increase negative representations of TGGD people, increase discrimination and victimization, and limit access to resources.^2–5 For example, TGGD people have reported avoiding healthcare due to fears of mistreatment;⁶ this may be even greater in states where stigmatizing policies perpetuate and/or reflect more pervasive discrimination and victimization. Many state-level protective policies have also been introduced and/or passed over the last decade.⁷ These may reduce discrimination and victimization, potentially increasing access to education, employment, healthcare, etc.^8–10

Stigma has serious consequences for health, with TGGD people (and especially those of color) experiencing multiple health inequities with limited healthcare access.^6,11 Though extant research in this area is limited, previous studies demonstrate that living in states with more protective policies and less structural stigma is associated with improved mental and physical health.^12–14 One study using data from the Behavioral Risk Factor Surveillance System across 26 states, finds that living in states with more protective transgender-specific policies is associated with less time since the last routine checkup.¹²

Experiences with transgender-specific policies may be specific to the state’s social and geographical context, and may vary by other social identities, such as race/ethnicity. TGGD people of color experience more stigma;^6,15,16 especially within healthcare.^17–19 In addition to facing pervasive racism,^19–21 TGGD people of color can experience a greater prevalence, frequency, and severity of transgender-related stigma.^6,15 The consequences of experiencing transgender-related stigma may also be greater, with large racial/ethnic inequities in systematic vulnerability (i.e., social conditions like homelessness or poverty) and health outcomes (e.g., suicide and HIV).⁶ Experiences of stigma may vary by U.S. context. In addition to transgender-specific policies varying across states, historical experiences of race/racism and social attitudes towards racial minorities are also state-specific.²⁰ Therefore, when trying to understand how state-level policies are associated with experiences of healthcare for TGGD people of color, it is important to understand the specific state context in which these experiences occur.

To further understand associations between transgender-specific policies and healthcare among TGGD people in the U.S., this study examines the relationships between six state-level policies and healthcare avoidance. Stigmatizing policies were expected to be associated with more healthcare avoidance and protective policies were expected to be associated with less avoidance. It was also expected that healthcare avoidance would vary across states and that the association between race/ethnicity and healthcare avoidance would vary across states.

Methods

Study sample and procedures.

Data were from the U.S. Trans Survey (USTS), a national survey of 27,715 TGGD people, implemented by the National Center for Transgender Equality (NCTE) from August-September 2015.²² Detailed USTS procedures are described in the study report.⁶ Eligibility criteria included identifying as TGGD, being at least age 18, and living in the U.S. For this analysis, individuals identifying as crossdressers (because they have fundamentally different experiences than those with other TGGD identities; n=758) and those living in U.S. territories (n=63) were also excluded. The survey was conducted online in English and Spanish and covered a broad range of topics (e.g., health, employment). Data were collected anonymously, and participants entered a cash-prize drawing as incentive. The NCTE attained approval from the University of California-Los Angeles North General IRB.

Measures included healthcare use, state-level policies, and individual-level and state-level covariates.

Healthcare use.

Healthcare use was measured as whether participants avoided seeing a doctor within the past year due to fears of being disrespected or mistreated as a trans person.

Policies.

State-level policy data were from the Movement Advancement Project (MAP)²³. Policies included non-discrimination protections for gender identity, religious exemption laws, private health insurance policies, Medicaid policies, regulations for a legal name change, and regulations for changing the gender marker on a state-issued ID. These policies vary across states (Figure 1) and can influence access to healthcare for TGGD people.^23–26 Policies were determined based on those that existed in each state at the start of USTS data collection (August 2015). However, for identity document policies, variables are based on more current MAP data (July 2018 for changing a gender marker and February 2017 for a name change) because earlier data were not available.

Figure 1:

Policies Across States

Policies were examined separately and as a cumulative index to capture the policy climate of each state. Analyzing individual policies determines how each policy is associated with healthcare, while the index examines the broader socio-political context^9,14 The index is especially useful because although research suggests that these six policies are important,^23–26 it is unknown if these are the exact policies that should be considered for healthcare access. To create the index, for each policy, the state received a −1 score if the policy was harmful (i.e., the policy restricts TGGD rights and/or resources), a +1 if the policy was protective (i.e., the policy protects TGGD rights and/or resources), and a 0 if the policy did not exist. The index was comprised of a sum of these points, where each positive point indicated an additional protective policy and each negative point indicated an additional harmful policy. The index (Figure 1) ranged from −3 (in Georgia) to 5 (in California and the District of Columbia [DC]).

Individual-level covariates.

Interpersonal and individual-level covariates were selected based on White-Hughto et al’s² framework and additional literature highlighting inequities experienced by TGGD populations.^{6,10,11,27,28} These included demographics, interpersonal experiences of stigma, identity and social support experiences, systematic vulnerability, health status, health insurance coverage, and identity document changes.

Demographics were comprised of age, gender identity, sexual orientation, race, U.S. citizenship, highest education level, and employment status. Gender identity included trans-feminine, trans-masculine, and other gender diverse participants (assigned male at birth [AMAB] and assigned female at birth [AFAB]). Sexual orientation was measured as heterosexual/straight; LGB+; asexual; and other. Race/ethnicity included non-Hispanic White; American Indian or Alaska Native; Asian, Native Hawaiian, or Pacific Islander (API); Black; Latinx/Hispanic; Multiracial; or other race/ethnicity. Education was measured as: high school graduate; some college; undergraduate degree; and graduate or professional degree. Being employed was defined as full-time, part-time, or self-employment.

Transgender-related stigma and racism variables included discrimination, verbal victimization, and physical violence, based on yes/no questions determining if these experiences occurred in the past year. Gender expression was based on whether participants reported living full-time in a gender different form their sex assigned at birth. Outness was measured using a 0-8 scale, where each point indicates an additional group (e.g., family, friends) to whom the respondent had disclosed their gender identity. Social support measured whether immediate family, co-workers, and/or classmates provide social support (i.e., some, most, or all of the people in that group know the participant’s gender identity and are, on average, very supportive or supportive).

Systematic vulnerability was examined through four separate variables measuring lifetime experiences of homelessness and sex work, current experiences of poverty, and incarceration in the past year. Health status included psychological distress in the past thirty days (measured through the Kessler Psychological Distress Scale [K6]),²⁹ lifetime experiences of suicidal ideation, HIV status, at least one incident of binge drinking and any illicit drug use in the past thirty days. Health insurance was measured by whether participants had health coverage. Legal document changes were measured by whether at least some of the participants’ identity documents list their correct name or gender.

State-level covariates included each state’s racial makeup, population density, and rural/urban makeup. These characteristics reflect the state context and can influence experiences of identity and healthcare. Racial makeup data were from the U.S. Census’s 2017 American Community Survey³⁰ and measured the percentage of the population that is non-Hispanic White. Population density data were from the 2010 decennial Census. Rural/urban makeup was measured using the U.S. Department of Agriculture’s 2013 Rural-Urban Continuum Codes,³¹ and was measured as the proportion of counties ranked as most rural or most urban.

Analysis.

Data were analyzed using STATA 14 (College Station, Texas). Multilevel logistic regression examined associations between state policies and healthcare avoidance. Missing data on healthcare avoidance were missing at random and no data were missing on demographics. Since no other covariates were missing more than 5% of responses, all missing data were excluded,³² resulting in a sample size of 23,323. Multicollinearity was assessed and the model was re-specified to ensure no multicollinearity.³³

Descriptive statistics were computed, and bivariate analyses examined relationships between independent variables and healthcare. Two separate multilevel multivariable logistic regression models were fit, one including the policy index and the other including separate policy variables. Both models were adjusted for all covariates. To account for the clustering of data by state, U.S. state (including all fifty states and DC) was the random intercept. To examine if associations between race/ethnicity and healthcare avoidance vary across states, race/ethnicity was the random slope. The random slope was measured as a binary variable, based on identifying as non-Hispanic White or as a person of color. Although the overall sample is large, when stratified by state, there were too few TGGD people of color in each state to examine differences across race/ethnicity. Therefore, even though experiences across racial/ethnic minorities are unique, the random slope only examined general experience of being a racial/ethnic minority.

Results

The participants’ average age was 30.59 (SD=12.19), with approximately 34% (n=7,933) identifying as trans-feminine, 30% (n=6,981) as trans-masculine, and 36% (n=8,409) as other gender diverse. Most of the sample was LGB+ (71.59%, n=16,698), non-Hispanic White (80.8%, n=18,845), and a U.S. citizen (98.36%, n=22,941). Participants disproportionately lived in states with more protective policies (e.g., California and New York). Approximately one-quarter of participants (n=5,430) avoided healthcare in the past year, with about 22% (n=4,238) of non-Hispanic Whites avoiding care, compared to a range from 23% (among API, n=150) to 36% (among American Indian/Alaska Native, n=96) among participants of color.

The policy index was significantly associated with decreased odds of avoiding healthcare due to fears of mistreatment (Adjusted Odds Ratio [aOR]=0.97, p=0.025) (Table 2). However, no associations between individual policies and avoiding healthcare were statistically significant (Data not shown). No state-level control variables were significantly associated with healthcare, but many individual-level covariates were.

Table 2:

Multilevel logistic regression results examining associations between policies and avoiding healthcare (n=23,323)

	aOR	95% CI	p-value
Policies
Policy composite score	0.97	0.94,1.00	0.025
State-Level Characteristics
State proportion of non-Hispanic White people, mean (SD)	0.99	0.99,1.00	0.068
State population density, mean (SD)	1.00	1.00,1.00	0.833
State proportion living in a rural area, mean (SD)	1.22	0.07,22.64	0.892
State proportion living in an urban area, mean (SD)	0.94	0.72,1.22	0.636
Individual-Level Sociodemographic Characteristics
Age	0.98	0.98,0.99	<0.001
Gender identity
Trans-feminine	Reference Group
Trans-masculine	1.63	1.49,1.78	<0.001
Other gender diverse (AFAB)	0.83	0.75,0.93	0.001
Other gender diverse (AMAB)	0.60	0.51,0.71	<0.001
Sexual identity
Heterosexual/Straight	Reference Group
LGB+	1.06	0.95,1.20	0.301
Asexual	1.05	0.90,1.23	0.524
Other	1.10	0.92,1.30	0.292
Race/Ethnicity
White	Reference Group
American Indian/Alaska Native	1.32	0.99,1.77	0.059
Asian, Native Hawaiian, Pacific Islander	0.93	0.75,1.16	0.511
Black	0.95	0.77,1.18	0.661
Latinx/Hispanic	0.97	0.82,1.14	0.686
Multiracial	1.01	0.86,1.20	0.842
Other	1.19	0.97,1.46	0.087
Has U.S. citizenship	0.96	0.74,1.25	0.749
Highest education level
Less than high school	Reference Group
High school graduate (including GED)	1.20	0.97,1.49	0.089
Some college (no degree)	1.32	1.08,1.61	0.007
Undergraduate degree	1.64	1.34,2.02	<0.001
Graduate or professional degree	1.76	1.40,2.21	<0.001
Employment status
Employed	Reference Group
Unemployed	0.81	0.73,0.89	<0.001
Out of the labor force	0.85	0.78,0.93	<0.001
Experiences of Trans-Related Stigma
Experienced discrimination	2.48	2.27,2.72	<0.001
Experienced verbal harassment	1.93	1.79,2.08	<0.001
Experienced physical violence	1.38	1.23,1.54	<0.001
Experiences of Racism
Experienced discrimination	0.91	0.71,1.17	0.464
Experienced verbal harassment	1.29	1.09,1.52	0.003
Experienced physical violence	0.84	0.59,1.18	0.305
Gender Expression, Outness, and Social Support
Full time in gender different from sex assigned at birth	1.86	1.70,2.04	<0.001
Outness scale	1.00	0.98,1.03	0.614
Has support from family, coworkers, or classmates	0.81	0.75,0.88	<0.001
Systematic Vulnerability
Living at/near poverty	1.03	0.96,1.11	0.412
Ever experienced homelessness	1.44	1.33,1.55	<0.001
Incarcerated in the past year	0.84	0.63,1.11	0.212
Ever engaged in sex work/industry	0.97	0.87,1.08	0.625
Health Status and Health Insurance
Psychological distress	1.75	1.63,1.89	<0.001
Suicidal ideation	1.71	1.53,1.92	<0.001
HIV status
Not living with HIV	Reference Group
Living with HIV	0.53	0.32,0.87	0.012
Never tested/does not know	0.87	0.81,0.95	0.001
Binge drinking in the past month	1.00	0.92,1.08	0.921
Used drugs in the past month	1.05	0.97,1.13	0.202
Has health insurance coverage	0.99	0.89,1.09	0.808
Identity documents
Has preferred name on IDs	0.83	0.76,0.91	<0.001
Has preferred gender on IDs	0.94	0.85,1.04	0.225
Random Intercept	0.005	0.0006, 0.04
Random Slope	0.02	0.007, 0.05

Boldface indicates statistical significance (p<0.05)

For both models, the random intercept was statistically significant, indicating that the odds of avoiding healthcare varied across U.S. states. For both models, the random slope was statistically significant, indicating that associations between race/ethnicity and avoiding healthcare varied across states. Figure 2 demonstrates racial/ethnic differences in healthcare avoidance across states and highlights that, in nearly all states, TGGD people of color have higher reports of avoiding healthcare due to fears of mistreatment, with some states showing greater differences (e.g., Alaska and Maine) and others having similar reports across groups (e.g., Maryland and Virginia). Binary analyses (chi square tests and Fisher’s exact tests) demonstrate significant differences in only five states. Seven states had more non-Hispanic White people reporting healthcare avoidance than TGGD people of color, but these differences were not statistically significant. Six states were not included in this additional analysis because fewer than 10 participants in each state identified as a person of color.

Figure 2:

Comparisons of healthcare avoidance by race/ethnicity across all U.S. states¹

*p<0.05, **p<0.01

¹Figure excludes all states with fewer than 10 transgender and other gender diverse participants of color, including Montana, North Dakota, Oklahoma, South Dakota, West Virginia, and Wyoming.

Discussion

Contradictory to the hypotheses, no individual policies were significantly associated with healthcare avoidance. However, the policy index demonstrated that living in states with more protective policies was associated with reduced odds of avoiding healthcare due to fears of mistreatment (confirming hypotheses). The overall political climate may be more important for understanding healthcare avoidance than specific policies. However, individual policies are reciprocally related to the social environments in which they exist. Restrictive policies may be more likely to pass within states that already have stigmatizing social environments and protective policies may be more likely to pass within states with accepting social environments. At the same time, these policies also reinforce the ideologies in those environments, whether they are reinforcing stigma or promoting acceptance.

Social/political climates can influence healthcare use among TGGD people by directly influencing access to resources (e.g., health insurance policies may determine the ability to pay for care). Furthermore, stigmatizing social climates may contribute to increased discrimination and/or violence. When stigma is experienced within social settings, anticipated stigma also increases^34,35 and when TGGD people anticipate stigma (especially within healthcare), they may be more likely to avoid care due to these concerns.

Current findings are consistent with previous studies suggesting that state-level policies matter for the health of TGGD people.^12,14 Though other research has demonstrated that policies are associated with healthcare use among TGGD people,^12–14 this study is unique because it examines the relationship between state-level policies (as both individual policies and an index) and healthcare avoidance across all 50 states (and DC), while accounting for the clustering of data by state. Even after controlling for state-level policies and all covariates, the relationship between U.S. state and healthcare avoidance was significant, indicating that the variability across states was still unexplained. Additional factors not measured in this study, such as healthcare accessibly (e.g., quality of care), and political, social, and historical characteristics of U.S. states, may account for this unobserved heterogeneity. Future research should examine the healthcare needs of TGGD people in states where avoidance is especially high.

The relationship between race/ethnicity and avoiding healthcare varied across states. In almost all states, TGGD people of color reported more healthcare avoidance than non-Hispanic White participants. Most of these binary associations were not statistically significant. However, Figure 2 demonstrates how the association between race/ethnicity and healthcare avoidance varies across states. Unfortunately, these analyses were unable to explore more nuanced experiences across race/ethnicity. Future research should consider probability sampling or community-based sampling strategies that disproportionately recruit TGGD people of color to allow for more intersectional analyses examining gender identity and race/ethnicity.

Still, these findings demonstrate that, even after controlling for state policies and all covariates, that in general, the relationship between being a racial/ethnic minority and avoiding healthcare is state-specific. Although this study controlled for the prevalence of transgender-related and racist stigma, the severity, frequency, and expectation of stigma are not measured. Given the different social and historical contexts of racism across U.S. regions,²⁰ these experiences likely vary across states, and could result in varied relationships between race/ethnicity and healthcare avoidance across states. In addition, binary associations between race/ethnicity and healthcare avoidance were only significant in five states, with TGGD people of color being more likely to report healthcare avoidance. It is unclear why these five states demonstrate significant differences. However, these states are all disproportionately comprised of non-Hispanic White people (>80% of people in each state³⁰). Though this is controlled for in multivariable analyses, this may play a role in the differences when examining binary results.

Although this study examines state-level policies, local and federal policies may also be important for the health of TGGD people. These policies should be considered within the context of race/ethnicity to account for differences in experiences. A combination of protective policies addressing discrimination, health insurance, and identity documents should allow for TGGD people to have more access to healthcare. However, since findings suggest that the policy climate is what matters, it is especially important to also consider advocating for protective policies (and against harmful ones) that are not included in this analysis (e.g., bathroom bills, safe school laws, and conversion therapy laws). As the enactment of transgender-specific policies continues to increase in the U.S.,⁷ it is important to ensure that these policies are promoting better for health for TGGD people, and especially TGGD people of color.

Limitations.

The USTS uses a convenience sample, which are common among hard-to-reach populations,³⁶ but are less generalizable. Although estimates suggest that U.S. TGGD populations are racially/ethnically diverse,³⁷ the sample is disproportionately non-Hispanic White, highlighting challenges of a convenience sample, especially with online data collection.^38–40

Data are also cross-sectional, so no causal inferences can be made. Though policy variables were based on the timing of USTS data collection, identity document policies were more current. It is also possible that policies that did not exist in 2015 were being discussed at the time, potentially changing the social/political climate. Longitudinal analyses may be more appropriate for capturing how changes in policies over time influence healthcare use. Finally, variables included in the analysis were limited to those available in the USTS and publicly available state-level data. State-level variables describing the availability and quality of healthcare services for TGGD people were not available, which represents an important area for future research. Individual-level variables describing state residence (e.g., length of residence, reason for living in a particular state) were also unavailable; state residence and migration patterns among TGGD people are not random and could influence experiences of healthcare.^41,42 In addition, the USTS, measured healthcare avoidance based on whether participants avoided seeing a doctor; while the term “doctor” may intend to broadly describe healthcare, this survey question may exclude healthcare offered by other types of providers.

Despite limitations, this study also has many strengths. This study analyzed data from a very large sample of TGGD people across the U.S. and employed a unique approach to understanding how policies may influence TGGD healthcare avoidance. This study not only considers TGGD identity, but also race/ethnicity and its role on healthcare access for TGGD individuals.

Conclusions.

This study demonstrates that the social climate created by transgender-specific policies shapes TGGD people’s healthcare avoidance. Findings also show that TGGD healthcare avoidance across states varies by race/ethnicity. Within a U.S. political climate where transgender-specific policies are increasing, it is essential to consider policies that protect the health of TGGD people and especially TGGD people of color; through these policies, the U.S. can work towards achieving greater health equity.

Table 1:

Descriptive statistics and binary analyses examining the distribution of participants avoiding healthcare (n=23,323)

Variables	Sample Distribution	Avoided care due to fear of mistreatment
Policies
State non-discrimination protections, % (n)*
Includes gender identity/expression	47.83 (11,155)	22.62 (2,523)
Does not include gender identity/expression	52.17 (12,168)	23.89 (2,907)
State religious exemption laws, % (n)*
Broad law exists	57.13 (13,325)	23.73 (1,987)
Only specific law exists	6.96 (1,624)	25.49 (414)
No law exists	35.90 (8,374)	22.73 (3,029)
State private health insurance policies, % (n)
Has TGGD-specific protections	39.15 (9,131)	22.80 (2,082)
Does not have TGGD-specific protections	60.85 (14,192)	23.59 (3,348)
State Medicaid policies, % (n)*
No TGGD-specific Medicaid policies	71.10 (16,582)	23.79 (3,945)
Excludes TGGD-specific care	5.63 (1,312)	22.56 (296)
Includes TGGD-specific care	23.28 (5,429)	21.90 (1,189)
Gender marker change requirements on state ID, % (n)***
No policies exist	1.38 (321)	26.48 (85)
Requires proof of surgery, court order, or amended birth certificate	19.00 (4,432)	25.38 (1,125)
Accepts documentation from a limited list of providers	37.77 (8,810)	23.85 (2,101)
Accepts documentation from a broad range of providers	41.85 (9,760)	21.71 (2,119)
State legal name change requirements, % (n)*
Unclear rules or decided by an individual court	49.04 (11,438)	23.93 (2,737)
Requires a public announcement	8.30 (1,936)	21.85 (423)
Does not require a public announcement	42.66 (9,949)	22.82 (2,270)
Composite Score, mean (SD)**	1.61 (2.14)	1.54 (2.13)
State-Level Characteristics
State proportion of non-Hispanic White people, mean (SD)	77.78 (8.64)	77.59 (8.70)
State population density, mean (SD)	323.56 (891.68)	336.60 (982.30)
State proportion living in a rural area, mean (SD)	0.01 (0.02)	0.01 (0.02)
State proportion living in an urban area, mean (SD)	0.56 (0.22)	.56 (0.23)
Individual-Level Sociodemographic Characteristics
Age, mean (SD)***	30.59 (12.76)	28.49 (10.01)
Gender identity, % (n)***
Trans-feminine	34.01 (7,933)	20.96 (1,663)
Trans-masculine	29.93 (6,981)	31.30 (2,185)
Other gender diverse (AFAB)	29.00 (6,764)	20.17 (1,364)
Other gender diverse (AMAB)	7.05 (1,645)	13.25 (218)
Sexual identity, % (n)***
Heterosexual/Straight	11.18 (2,608)	21.36 (557)
LGB+	71.59 (16,698)	23.84 (3,980)
Asexual	10.66 (2,487)	20.43 (508)
Other	6.56 (1,530)	25.16 (385)
Race/Ethnicity, % (n)***
Non-Hispanic White	80.80 (18,845)	22.49 (4,238)
American Indian/Alaska Native	1.14 (267)	35.96 (96)
Asian, Native Hawaiian, Pacific Islander	2.80 (654)	22.94 (150)
Black	2.73 (636)	25.31 (161)
Latinx/Hispanic	5.09 (1,186)	24.70 (293)
Multiracial	4.76 (1,110)	27.48 (305)
Other	2.68 (625)	29.92 (187)
Has U.S. citizenship, % (n)
Yes	98.36 (22,941)	23.26 (5,337)
No	1.64 (382)	24.35 (93)
Highest education level, % (n)*
Less than high school	3.02 (704)	23.58 (166)
High school graduate (including GED)	11.65 (2,718)	23.91 (650)
Some college (no degree)	38.21 (8,912)	23.75 (2,117)
Undergraduate degree	34.15 (7,965)	23.44 (1,867)
Graduate or professional degree	12.97 (3,024)	20.83 (630)
Employment status, % (n)***
Employed	66.40 (15,487)	23.81 (2,687)
Unemployed	12.97 (3,025)	24.17 (731)
Out of the labor force	20.63 (4,811)	21.04 (1,012)
Experiences of Trans-Related Stigma
Experienced discrimination, % (n)***
Yes	13.83 (3,225)	49.89 (1,609)
No	86.17 (20,098)	19.01 (3,821)
Experienced verbal harassment, % (n)***
Yes	47.58 (11,098)	33.57 (3,726)
No	52.42 (12,225)	13.94 (1,704)
Experienced physical violence, % (n)***
Yes	8.76 (2,043)	46.26 (945)
No	91.24 (21,280)	21.08 (4,485)
Experiences of Racism
Experienced discrimination, % (n)***
Yes	1.83 (427)	45.20 (193)
No	98.17 (22,896)	22.87 (5,237)
Experienced verbal harassment, % (n)***
Yes	4.83 (1,126)	37.39 (421)
No	95.17 (22,197)	22.57 (5,009)
Experienced physical violence, % (n)***
Yes	0.87 (203)	41.87 (85)
No	99.13 (23,120)	23.12 (5,345)
Gender Expression, Outness, and Social Support
Full time in gender different from sex assigned at birth, % (n)***
Yes	62.03 (14,467)	28.11 (4,066)
No	37.97 (8,856)	15.40 (1,364)
Outness scale, mean (SD)***	3.49 (2.33)	3.88 (2.18)
Has support from family, coworkers, or classmates, % (n)
Yes	62.31 (14,533)	23.74 (2,087)
No	37.69 (8,790)	23.74 (2,087)
Systematic Vulnerability
Living at/near poverty, % (n)***
Yes	33.51 (7,815)	27.22 (2,127)
No	66.49 (15,508)	21.30 (3,303)
Ever experienced homelessness, % (n)***
Yes	29.23 (6,818)	33.34 (2,273)
No	70.77 (16,505)	19.13 (3,157)
Incarcerated in the past year, % (n)**
Yes	1.29 (301)	30.90 (93)
No	98.71 (23,022)	23.18 (5,337)
Ever engaged in sex work/industry, % (n)***
Yes	10.50 (2,449)	32.05 (785)
No	89.50 (20,874)	22.25 (4,645)
Health Status and Health Insurance
Psychological distress, % (n)***
Yes	39.24 (9,152)	31.98 (2,927)
No	60.76 (14,171)	17.66 (2,503)
Suicidal ideation, % (n)***
Yes	82.90 (19,334)	25.85 (4,998)
No	17.10 (3,989)	10.83 (432)
HIV status, % (n)***
Not living with HIV	51.12 (11,923)	25.55 (3,046)
Living with HIV	0.63 (147)	14.97 (22)
Never tested/does not know	48.25 (11,253)	20.99 (2,362)
Binge drinking in the past month, % (n)***
Yes	25.12 (5,858)	26.80 (1,570)
No	74.88 (17,465)	22.10 (3,860)
Used drugs in the past month, % (n)***
Yes	27.92 (6,512)	27.99 (1,823)
No	72.08 (16,811)	21.46 (3,607)
Has health insurance coverage, % (n)***
Yes	87.68 (20,449)	22.65 (4,632)
No	12.32 (2,874)	27.77 (798)
Identity Documents
Has preferred name on IDs, % (n)***
Yes	50.65 (11,813)	21.57 (2,548)
No	49.35 (11,510)	25.04 (2,882)
Has preferred gender on IDs, % (n)*
Yes	32.38 (7,551)	22.25 (1,680)
No	67.62 (15,772)	23.78 (3,750)
Total	23,323	23.28 (5,430)

^*p<0.05

^**p<0.01

^***p<0.001