Disparities in HIV Testing,Condom Use,and HIV Education Between Transgender and Not Transgender High School–Aged Youth: Findings From the 2019 Youth Risk Behavior Survey

Abstract

Transgender individuals are disproportionately affected by HIV in the United States. Given increased risk of HIV among youth, there is a need to understand HIV risk and protective factors among transgender individuals who are 18 years and younger. Patterns of HIV testing, HIV education, and condom use have known associations with HIV outcomes among youth in general, but are understudied among transgender youth. This study assessed these outcomes by developing a series of sex-stratified multivariable logistic regression models using pooled Youth Risk Behavior Survey data. Results indicate female and male transgender youth as well as males who were not sure they were transgender were more likely have tested for HIV compared with their not transgender peers. Male transgender youth were significantly less likely to have received HIV education compared with not transgender males. Females not sure if they were transgender and male transgender youth were significantly less likely to have used condoms compared with, respectively, not transgender female and not transgender male counterparts. In sum, condom use and HIV education both remain lower among transgender individuals relative to their not-transgender peers. This highlights the need for the promotion of culturally appropriate HIV education and HIV prevention supports among transgender youth.

Keywords

transgender youth YRBS HIV testing HIV education condom use

Gender minority individuals (e.g., transgender individuals and gender diverse individuals who may or may not identify as transgender) are disproportionately affected by HIV in the United States. A recent Centers for Disease Control and Prevention (CDC) surveillance report found the prevalence of HIV among transgender women ages 18 years and older was 42%, with differing prevalence among Black (62%), Hispanic/Latinx (35%), and white (17%) transgender women (CDC, 2021). This is 53 to 193 times higher than the national prevalence of 0.32% among U.S. youth and adults aged 13 and older (CDC, 2021). Although less is known about HIV prevalence among transgender men and other gender minority individuals, available data indicate these populations experience disproportionate impacts of HIV (CDC, 2019a, 2022).

The heightened prevalence of HIV among transgender populations highlights a need to better understand HIV risk and protective factors among transgender people, especially youth, about whom less is known. HIV risks have become greater among all U.S. youth, who report increased sexual risk behaviors such as not using a condom (46.2%) compared with 53.8% in 2017 (Kann et al., 2018). An estimated 21% of all new HIV diagnoses in 2019 (CDC, 2021) and 45% of all undiagnosed HIV cases (CDC, 2020a) are among youth ages 13 to 24 years.

Despite the elevated HIV risk experienced by transgender people and youth overall, minimal empirical research has explored HIV risk among transgender youth (CDC, 2020a). Using the 2017 National Youth Risk Behavior Survey (YRBS), the CDC found transgender youth reported unique patterns of risk and protective behaviors compared with not-transgender peers (Eisenberg et al., 2017; Johns et al., 2018, 2019). For instance, transgender youth were more likely to have tested for HIV than not-transgender male and female peers (70% vs. 87%/87% never tested, respectively) (Kann et al., 2018). There are also important within-group differences: One study of at-home HIV testing among transgender youth found transgender women were significantly less likely to have tested for HIV than transgender men (6.1% vs. 20%; Jin et al., 2019).

In addition to HIV testing, condom use plays a key role in HIV prevention. In addition to HIV testing, condom use plays a key role in HIV risk reduction. Notably, the 2017 YRBS showed that while transgender youth were significantly more likely to have had sex in their lifetime (43%) compared with their not-transgender male (35%) and female (33%) peers, they were significantly more likely to not use a condom during last sexual intercourse (64%) when compared with their not-transgender male (38%) and female (49%) peers, respectively (Johns et al., 2019).

It is important to contextualize the behaviors of transgender youth within the structures in which they are embedded. One way to understand these structures and their effects is through minority stress theory (Tan et al., 2020). Minority stress is the excess burden of stress among marginalized populations as a result of distal (e.g., systemic stigmatization) and proximal (e.g., perception of stigma) factors (Meyer, 2003). Societal inequities that create an oppressive and stigmatizing environment for transgender people are well-documented (Grant et al., 2011; King et al., 2020). This suggests transgender and gender minority communities may be disproportionately engaging in HIV risk behavior because of their experiences with minority stress (Douglass & Conlin, 2022; Sevelius, 2013; Testa et al., 2015).

The gender affirmation framework, which considers how social affirmation (or nonaffirmation) of a person’s true gender may influence behavioral risk factors (Sevelius, 2013), can also be used to conceptualize HIV risk among transgender youth. For example, transgender youth who are not affirmed at their schools, and who may be forced to use incorrect restrooms at school might be more likely to leave school (Johns et al., 2021); similarly, youth who may fear being outed to their family by non-affirming educators or administrators (Fisher et al., 2018; Reisner et al., 2020) might be more likely to leave home and experience homelessness (McCann & Brown, 2021). Both of these factors can lead to loss of health insurance (Rew et al., 2021), engagement in sex work (Fletcher et al., 2014; Shelton, 2016), low self-esteem (Seibel et al., 2018), and weak social support networks (Eastwood et al., 2021)—all of which are socio-structural factors associated with HIV risk and acquisition (Brennan et al., 2012). Extant research into the topic of HIV risk among transgender communities supports the use of both Minority Stress Theory and Gender Affirmation as theoretical frameworks (Rich et al., 2020; Rood et al., 2018; Sha et al., 2021).

Although primarily used to conceptualize causality in health behavior, it is essential to highlight that both Minority Stress Theory and Gender Affirmation Framework consider the role of socio-structural context in producing HIV risk for transgender youth. For example, Wood et al. used a minority stress framework to demonstrate how trans youth experience barriers to accessing HIV prevention services, such as pre-exposure prophylaxis, highlighting the clear impact of transphobic stigma (Wood et al., 2019). In their initial articulation of gender affirmation as a framework for conceptualizing and assessing HIV risk among transgender women of color, Sevelius specifically emphasizes how intersecting social forces of racism, sexism, and transphobia define the contexts in which health behaviors occur (Sevelius, 2013). Building on this idea, Reisner et al. expanded the definition of gender affirmation to explicitly incorporate legal and medical factors (Reisner et al., 2016).

We note these latter factors are particularly worth highlighting in the context of widespread anti-transgender legislation seeking to restrict the medical and social autonomy of transgender youth; any analysis of HIV risk among transgender youth must, therefore, contextualize itself according to the sociopolitical dynamics which affect transgender youth and may influence health risk. Although data for this study were collected prior to the more recent and more widely publicized surges in legislation targeting transgender youth, bills and policies targeting transgender youth were considered and passed at this time; such legislation forms part of a wider tapestry of anti-transgender social stigma which serves as a barrier to health care access for transgender people, including transgender youth. In light of the growing role of legislative violence, future studies of this dynamic should explicitly consider jurisdictional differences associated with state-level policy restricting transgender youth health care and other forms of trans youth autonomy.

Relatedly, we must also consider how state- and school-level health education policy and programming affects transgender youth in regard to HIV prevention. Prior research has established that significantly fewer transgender and unsure youth than not-transgender youth reported being taught about HIV in school (Fontenot et al., 2020). Data from YRBS from 2009 to 2017 showed that HIV education can be associated with a variety of HIV preventive behaviors such as condom use and testing, but youth who may be more vulnerable to HIV were less likely to receive said education (Phillips et al., 2020). It is critical to analyze population-level patterns of access to HIV education as a beginning to improving delivery of gender-affirming HIV educational curricula and thus reducing the burden of HIV among transgender populations.

Accordingly, this study examines the disparities between transgender and not-transgender youth regarding HIV testing, HIV education, and condom use. We utilized a large and geographically diverse sample of high school-aged youth in the United States to analyze the prevalence and correlation of these factors. We hypothesized that, based on minority stress theory and the gender affirmation framework, transgender youth would be less likely to test for HIV, use condoms, and receive HIV education than not-transgender peers. However, the fact that many HIV prevention and treatment messages target transgender individuals may result in the attenuation of these associations. Furthermore, we hypothesized that there we would find critical within-group differences by age, race/ethnicity, and sexual identity. We discuss our findings in relation to the current sociopolitical context which affects transgender youth in the United States.

Method

Data

This study relies on secondary data from the 2019 YRBS, which is a biennial, national survey conducted by the CDC (Brener et al., 2013). There are both national and local versions; the local YRBS can be administered with alterations at a state, territorial, tribal, and urban school district level. Pooled data from local YRBS administrations were used for this study. Jurisdictions used a two-stage cluster sample design to identify a representative sample of students. Per CDC, participation in the YRBS is contingent on parental approval (CDC, 2020b). Consequently, the lack of such parental approval leads to the exclusion of students without such permissions. Participation is voluntary and completely anonymous; students completed the survey during one class period.

Analytic Sample

Data from 36 jurisdictions that included the gender modality (see Ashley, 2021) question in 2019 were pooled, resulting in an initial sample of 138,783 high school-aged youth. Several cases were excluded from the final analytic sample due to missing information on the dependent, independent, or control variables (see Allison, 2002). As three sets of models were assessed, each had a different analytic sample size due to the focus on a specific outcome. The three series of models had the following analytic sample sizes: HIV testing (n = 84,122); HIV education (n = 25,650); condom use (n = 34,950).

Measures

In this study, we assessed patterns of HIV testing, HIV education, and condom use among youth by gender modality.

Demographics

Age

Participants were asked, “How old are you?” Participants were given options for 12 years or younger, 13, 14, 15, 16, 17, or 18 years or older.

Race/Ethnicity

Participants were asked to identify themselves as the following: American Indian/Alaskan Native; Asian; Black/African American; Native Hawaiian/other Pacific Islander; white; Hispanic/Latino; Multiple-Hispanic; and Multiple-Non-Hispanic.

Sex

Participants were asked “What is your sex?” Choices were (1) Female or (2) Male.

Sexual Identity

Participants were asked, “Which of the following best describes you?” Participants were given options for (1) Heterosexual (straight), (2) Gay or lesbian, (3) Bisexual, and (4) Not sure.

Gender Modality

Participants were asked, “Some people describe themselves as transgender when their sex at birth does not match the way they think or feel about their gender. Are you transgender?” Options given were (1) No, I am not transgender, (2) Yes, I am transgender, (3) I am not sure if I am transgender, and (4) I do not know what this question is asking. Empirical and theoretical publications have demonstrated that individuals may identify as a gender that differs from their sex assigned at birth, but not as transgender; while these people explicitly do not consider themselves transgender, it may also not be accurate to describe them as cisgender (Ashley, 2021; Felt et al., 2021). The YRBS question does not allow for identification of this group, thus we intentionally use the language “not-transgender” as opposed to the more common “cisgender” in this manuscript, for accuracy.

Outcomes of Interest

HIV Testing

Participants were asked “Have you ever been tested for HIV, the virus that causes AIDS? (Do not count tests done if you donated blood.)” Options included (1) Yes, (2) No, and (3) Not sure. Individuals who indicated “Not sure” were dropped from analysis.

Condom Use

Participants were asked “The last time you had sexual intercourse, did you or your partner use a condom?” Options included (1) I have never had sexual intercourse, (2) Yes, and (3) No. Individuals who reported “I have never had sexual intercourse” were dropped from analysis.

HIV Education

Participants were asked “Have you ever been taught about AIDS or HIV infection in school?” Options included (1) Yes, (2) No, and (3) Not sure. Individuals who said “Not sure” were dropped from analysis.

Statistical Analysis

Data cleaning and recoding were conducted in SAS 9.4 (SAS Institute, Cary, NC). Analyses were carried out using SAS-Callable SUDAAN Version 11.0.1 (RTI International, Research Triangle Park, NC) to appropriately weight estimates and to account for the complex sampling design of the YRBS. Sex-stratified multivariable logistic regression models were developed to estimate the sex-specific odds of each of the three outcome variables (HIV testing, HIV education, and condom use) associated with gender modality, age, race, and sexual identity.

Results

HIV Testing

Demographics

The sample predominantly identified as not-transgender (97.2%)—the remainder either identified as transgender (1.5%) or were unsure if they were transgender (1.3%; Table 1). Youth were predominantly White (48.1%) or Hispanic (23.9%). Most of the population identified as heterosexual (83.6%); followed by bisexual (9.2%), with 3.1% identifying as gay/lesbian, and the rest were unsure of their sexual identity (4.2%). Similar proportions of youth were ages 15 (23.8%), 16 (25.4%), and 17 (24.7%) years, with smaller numbers of youth ages 14 years and under (12.6%) and 18 years old (13.6%). Overall prevalence of HIV testing was low for both female (14.7%) and male students (15.8%).

Table 1.

Age, Gender Modality, Race/Ethnicity, Sexual Identity, HIV Testing, HIV Education, and Condom Use of Participants.

Outcomes	HIV testing					HIV education					Condom use
	Total	Female		Male		Total	Female		Male		Total	Female		Male
	Total	Yes	No	Yes	No	Total	Yes	No	Yes	No	Total	Yes	No	Yes	No
	% (n)	% (n)	% (n)	% (n)	% (n)	% (n)	% (n)	% (n)	% (n)	% (n)	% (n)	% (n)	% (n)	% (n)	% (n)
Overall	100% (84,122)	14.7% (7,087)	85.3% (37,374)	15.8% (6,572)	84.2% (33,089)	100% (25,650)	84.6% (11,327)	15.4% (2,222)	84.3% (10,129)	15.7% (1,972)	100% (34,950)	54.0% (9,351)	46.0% (8,478)	65.5% (10,958)	34.5% (6,163)
Age, years
14 or younger	12.6% (14,437)	10.4% (843)	89.6% (7,126)	14.4% (934)	85.6% (5,534)	12.5% (4,250)	83.8% (1,810)	16.2% (455)	82.4% (1,589)	17.6% (396)	5.3% (2,675)	55.2% (698)	44.8% (544)	64.3% (949)	35.7% (484)
15	23.8% (22,021)	10.9% (1,473)	89.1% (10,339)	13.0% (1,411)	87.0% (8,798)	24.6% (6,736)	85.1% (3,011)	14.9% (643)	83.3% (2,588)	16.7% (494)	15.9% (6,558)	56.8% (1,869)	43.2% (1,457)	71.0% (2,214)	29.0% (1,018)
16	25.4% (22,088)	13.4% (1,837)	86.6% (9,914)	14.6% (1,627)	85.4% (8,710)	25.1% (6,759)	85.4% (3,110)	14.6% (507)	85.3% (2,669)	14.7% (473)	25.9% (10,007)	55.5% (2,825)	44.5% (2,414)	67.7% (3,112)	32.3% (1,656)
17	24.7% (19,124)	17.3% (2,097)	82.7% (7,741)	17.3% (1,829)	82.7% (7,457)	24.7% (5,770)	85.2% (2,564)	14.8% (432)	85.4% (2,363)	14.6% (411)	33.1% (11,294)	52.5% (2,931)	47.5% (2,994)	63.1% (3,302)	36.9% (2,067)
18 or older	13.6% (6,452)	24.0% (837)	76.0% (2,254)	21.4% (771)	78.6% (2,590)	13.2% (2,135)	82.1% (832)	17.9% (185)	83.9% (920)	16.1% (198)	19.9% (4,416)	52.0% (1,028)	48.0% (1,069)	62.2% (1,381)	37.8% (938)
Gender modality
Not transgender	97.2% (81,812)	14.6% (6,822)	85.5% (36,433)	15.5% (6,222)	84.6% (32,335)	97.6% (25,000)	84.8% (11,047)	15.2% (2,153)	84.6% (9,910)	15.4% (1,890)	97.5% (33,994)	54.5% (9,195)	45.5% (8,172)	65.9% (10.727)	34.1% (5,900)
Transgender	1.5% (1,220)	24.2% (151)	75.8% (456)	31.3% (194)	68.7% (419)	1.1% (321)	74.9% (125)	25.1% (39)	74.1% (112)	25.9% (45)	1.3% (548)	40.3% (96)	59.7% (157)	45.2% (133)	54.8% (162)
Not sure	1.3% (1,090)	18.3% (114)	81.7% (485)	27.0% (156)	73.0% (335)	1.2% (329)	81.1% (155)	18.9% (30)	74.2% (107)	25.8% (37)	1.2% (408)	26.1% (60)	73.9% (149)	51.6% (98)	48.4% (101)
Race/ethnicity
White	48.1% (42,371)	11.9% (2,834)	88.1% (19,184)	12.5% (2,636)	87.5% (17,717)	50.8% (7,724)	86.8% (3,386)	13.2% (469)	86.0% (3,429)	14.0% (440)	51.6% (18,274)	56.0% (5,226)	44.0% (4,330)	65.9% (5,639)	34.1% (3,079)
Black	17.8% (13,730)	19.6% (1,541)	80.4% (5,940)	21.9% (1,429)	78.1% (4,820)	16.1% (5,338)	83.1% (2,401)	17.0% (569)	82.8% (1,878)	17.2% (490)	16.0% (5,104)	52.1% (1,175)	47.9% (1,168)	63.6% (1,726)	36.5% (1,035)
Hispanic	23.9% (16,370)	17.9% (1,830)	82.1% (7,041)	18.7% (1,617)	81.3% (5,882)	21.9% (7,940)	81.4% (3,508)	18.6% (793)	80.0% (2,934)	20.0% (705)	24.7% (7,134)	51.0% (1,816)	49.0% (1,829)	66.8% (2,273)	33.2% (1,216)
Asian	5.7% (5,606)	9.6% (319)	90.4% (2,516)	13.5% (370)	86.5% (2,401)	5.8% (2,855)	82.6% (1,210)	17.4% (243)	86.4% (1,195)	13.6% (207)	2.5% (1,191)	62.2% (337)	37.8% (267)	63.8% (371)	36.3% (216)
Other	4.5% (6,045)	13.8% (563)	86.2% (2,693)	17.2% (520)	82.8% (2,269)	5.3% (1,793)	85.7% (822)	14.3% (148)	87.5% (693)	12.5% (130)	5.1% (3,247)	48.6% (797)	51.4% (884)	61.8% (949)	38.3% (617)
Sexual identity
Heterosexual	83.6% (69,985)	14.0% (5,136)	86.0% (28,953)	15.1% (5,775)	84.9% (30,121)	84.4% (21,101)	85.2% (8,469)	14.8% (1,678)	84.7% (9,213)	15.3% (1,741)	83.1% (28,726)	58.2% (7,393)	41.8% (5,806)	66.8% (10,130)	33.2% (5,397)
Gay/Lesbian	3.1% (2,439)	20.6% (257)	79.4% (1,123)	25.9% (263)	74.1% (796)	2.7% (748)	79.9% (352)	20.1% (74)	78.3% (244)	21.7% (78)	3.2% (1,127)	19.6% (125)	80.4% (521)	47.2% (233)	52.8% (248)
Bisexual	9.2% (8,150)	18.8% (1,369)	81.2% (5,255)	21.2% (286)	78.8% (1,240)	9.2% (2,617)	83.9% (1,806)	16.1% (336)	85.5% (418)	14.5% (57)	11.0% (4,137)	43.5% (1,529)	56.5% (1,846)	55.1% (421)	45.0% (341)
Not sure	4.2% (3,548)	11.9% (325)	88.1% (2,043)	21.5% (248)	78.5% (932)	3.7% (1,184)	81.0% (700)	19.0% (134)	73.5% (254)	26.5% (96)	2.8% (960)	46.7% (304)	53.3% (305)	59.0% (174)	41.0% (177)

Females

In an unadjusted model (Table 2, Model 1), female students who identified as transgender were significantly more likely than female students who were not transgender to report ever testing for HIV (odds ratio [OR] = 1.87; 95% confidence interval [CI] = [1.19, 2.94]). After controlling for age, race/ethnicity, and sexual identity (Table 2, Model 2), female students who identified as transgender were no longer significantly more likely to have tested for HIV. Unsurprisingly, odds of testing decreased with decreased age. Also of note, Black and Hispanic females were significantly more likely than white females to have tested for HIV, as were bisexual females when compared with heterosexual females.

Table 2.

Odds Ratios and Adjusted Odds Ratios for HIV Testing Based on Gender Modality, Stratified by Sex.

Outcomes	Female n = 44,461						Male n = 39,661
	Model 1:Gender modality			Model 2:Gender modality + demographics			Model 1:Gender modality			Model 2:Gender modality + demographics
	p-value	OR	95% CI	p-value	aOR	95% CI	p-value	OR	95% CI	p-value	aOR	95% CI
Gender modality
Not transgender		1.00			1.00			1.00			1.00
Transgender	.0065	1.87**	[1.19, 2.94]	.0845	1.55	[0.94, 2.55]	<.0001	2.49***	[1.67, 3.72]	.0009	2.07**	[1.35, 3.18]
Not sure	.3169	1.31	[0.77, 2.24]	.6361	1.15	[0.64, 2.07]	.0025	2.02*	[1.28, 3.19]	.0467	1.63*	[1.01, 2.65]
Age, years
14 or younger				<.0001	0.36***	[0.28, 0.46]				<.0001	0.58***	[0.45, 0.75]
15				<.0001	0.39***	[0.31, 0.48]				<.0001	0.55***	[0.44, 0.68]
16				<.0001	0.50***	[0.41, 0.60]				.0001	0.63***	[0.50, 0.79]
17				.0001	0.67***	[0.55, 0.82]				.0097	0.76**	[0.62, 0.94]
18 or older					1.00						1.00
Race/ethnicity
White					1.00						1.00
Black				<.0001	1.81***	[1.54, 2.14]				<.0001	1.98***	[1.66, 2.37]
Hispanic				<.0001	1.62***	[1.37, 1.91]				<.0001	1.63***	[1.40, 1.90]
Asian				.1652	0.83	[0.64, 1.08]				.5614	1.09	[0.82, 1.44]
Other				.1707	1.22	[0.92, 1.62]				.0004	1.48**	[1.19, 1.83]
Sexual identity
Heterosexual					1.00						1.00
Gay/Lesbian				.0526	1.43	[1.00, 2.07]				.0004	1.77**	[1.29, 2.42]
Bisexual				.0008	1.38**	[1.14, 1.67]				.0375	1.41*	[1.02, 1.95]
Not sure				.1176	0.81	[0.63, 1.05]				.0780	1.35	[0.97, 1.90]

Source. Pooled 2019 YRBS Data.

Note. OR = odds ratio; CI = confidence interval; aOR = adjusted OR.

p < .05. **p < .01. ***p < .001.

Males

For males, both students who identified as transgender and those who were unsure if they were transgender were significantly more likely to have tested for HIV than those who were not transgender (OR = 2.49, 95% CI = [1.67, 3.72] and OR = 2.02, 95% CI = [1.28, 3.19], respectively). In the adjusted model (Table 2, Model 2), differences remained significant both for males who identified as transgender (aOR = 2.07, 95% CI = [1.35, 3.18]) and those who were unsure if they were transgender (aOR = 1.63, 95% CI = [1.01, 2.65]), albeit at lower magnitudes. Odds of testing also decreased with decreasing age. Furthermore, Black, Hispanic, and other race/ethnicity males were significantly more likely than white males to have tested for HIV, as were gay and bisexual males when compared with their heterosexual counterparts.

HIV Education

Demographics

Youth primarily identified as not transgender (97.6%; Table 1); 1.1% identified as transgender and 1.2% were unsure if they were transgender. The sample was predominantly white (50.8%) and Hispanic (21.9%). Most of the population identified as heterosexual (84.4%), followed by bisexual (9.2%), with 2.7% identifying as gay/lesbian (2.7%). Similar proportions of youth were ages 15 (24.6%), 16 (25.1%), and 17 years old (24.7%), with smaller numbers of youth ages 14 years old and under (12.5%) and 18 years old (13.2%). Overall prevalence of HIV education was high for both female (84.6%) and male (84.3%) students.

Females

There were no significant differences between transgender and not-transgender female students, either in the unadjusted model (Table 3, Model 1) or in the adjusted model (Table 3, Model 2). Odds of receiving HIV education increased with age. Notably, Black and Hispanic females were significantly less likely than white females to have received HIV education.

Table 3.

Odds Ratios and Adjusted Odds Ratios for HIV Education Based on Gender Modality, Stratified by Sex.

Outcomes	Female n = 13,549						Male n = 12,101
	Model 1:Gender modality			Model 2:Gender modality + demographics			Model 1:Gender modality			Model 2:Gender modality + demographics
	p-value	OR	95% CI	p-value	aOR	95% CI	p-value	OR	95% CI	p-value	aOR	95% CI
Gender modality
Not transgender		1.00			1.00			1.00			1.00
Transgender	.1235	0.53	[0.24, 1.19]	.1782	0.58	[0.27, 1.28]	.0482	0.52*	[0.27, 0.99]	.0873	0.56	[0.29, 1.09]
Not sure	.4111	0.77	[0.41, 1.43]	.5976	0.85	[0.46, 1.57]	.0624	0.53	[0.27, 1.03]	.2209	0.64	[0.31, 1.31]
Age, years
14 or younger				.3175	1.18	[0.86, 1.62]				.7175	0.94	[0.67, 1.31]
15				.1140	1.26	[0.95, 1.67]				.8771	0.97	[0.69, 1.37]
16				.0673	1.28	[0.98, 1.67]				.3911	1.13	[0.86, 1.48]
17				.1132	1.24	[0.95, 1.62]				.3731	1.15	[0.84, 1.58]
18 or older					1.00						1.00
Race/ethnicity
White					1.00						1.00
Black				.0274	0.74*	[0.57, 0.97]				.0477	0.80*	[0.64, 1.00]
Hispanic				.0024	0.67**	[0.52, 0.87]				.0036	0.67**	[0.51, 0.88]
Asian				.0788	0.72	[0.50, 1.04]				.6617	1.08	[0.77, 1.51]
Other				.7187	0.93	[0.61, 1.40]				.3937	1.19	[0.80, 1.76]
Sexual identity
Heterosexual					1.00						1.00
Gay/Lesbian				.1808	0.75	[0.50, 1.14]				.1184	0.69	[0.44, 1.10]
Bisexual				.5453	0.94	[0.77, 1.15]				.6833	1.12	[0.66, 1.88]
Not Sure				.1731	0.76	[0.52, 1.13]				.0095	0.54**	[0.34, 0.86]

Source. Pooled 2019 YRBS Data.

Note. OR = odds ratio; CI = confidence interval; aOR = adjusted OR.

p < .05. **p < .01.

Males

Male students who identified as transgender were significantly less likely to have received HIV education than not-transgender males (OR = 0.52; 95% CI = [0.27, 0.99]) (Table 3, Model 1). After including age, race/ethnicity, and sexual identity (Table 3, Model 2) this association was no longer significant. In addition, males who were unsure about their sexual identity were significantly less likely to have received HIV education than heterosexual counterparts, as were Black and Hispanic male students compared with white peers.

Condom Use

Demographics

The sample predominantly identified as not-transgender (97.5%)—the remainder either identified as transgender (1.3%) or unsure if they are transgender (1.2%; Table 1). Youth were predominantly white (51.6%) or Hispanic (24.7%). A larger part of the population identified as heterosexual (83.1%), followed by bisexual (11%) and gay/lesbian (3.2%). Comparable proportions of youth were ages 16 (25.9%), and 17 (33.1%) years old. The sample had smaller numbers of youth ages 14 years and under (5.3%), 15 years old (15.9%), and 18 years old (19.9%). Condom use during last sexual intercourse was more common among male students (65.5%) than female students (54.0%).

Females

Female students who were unsure if they were transgender were significantly less likely to use condoms than not-transgender peers (OR = 0.29; 95% CI = [0.15, 0.58]; Table 4, Model 1). In adjusted models (Table 4, Model 2), a significant difference remained, albeit with a slightly weaker association (aOR = 0.44, 95% CI = [0.21, 0.94]). Odds of condom use were significantly higher in female students who were 15 years old compared with youth 18 and older, but no associations were seen for other ages. Lesbian, bisexual, and unsure female students were significantly less likely than heterosexual females to use condoms.

Table 4.

Odds Ratios and Adjusted Odds Ratios for Condom Use Based on Gender Modality, Stratified by Sex.

Outcomes	Female n = 17,829						Male n = 17,121
	Model 1:Gender modality			Model 2:Gender modality + demographics			Model 1:Gender modality			Model 2:Gender modality + demographics
	p-value	OR	95% CI	p-value	aOR	95% CI	p-value	OR	95% CI	p-value	aOR	95% CI
Gender modality
Not transgender		1.00			1.00			1.00			1.00
Transgender	.0666	0.56	[0.31, 1.04]	.7915	1.09	[0.57, 2.10]	.0025	0.43**	[0.25, 0.74]	.0151	0.52*	[0.30, 0.88]
Not sure	.0004	0.29**	[0.15, 0.58]	.0335	0.44*	[0.21, 0.94]	.0610	0.55	[0.29, 1.03]	.2738	0.69	[0.36, 1.34]
Age, years
14 or younger				.0786	1.32	0.97, 1.79				.3210	1.17	[0.85, 1.61]
15				.0276	1.30*	1.03, 1.64				.0017	1.50**	[1.16, 1.93]
16				.1401	1.18	0.95, 1.47				.0290	1.29*	[1.03, 1.62]
17				.8303	1.02	0.83, 1.25				.7346	1.04	[0.83, 1.31]
18 or older					1.00						1.00
Race/ethnicity
White					1.00						1.00
Black				.5190	0.93	0.74, 1.17				.1991	0.88	[0.73, 1.07]
Hispanic				.1440	0.86	0.70, 1.05				.5941	1.05	[0.87, 1.27]
Asian				.1421	1.33	0.91, 1.93				.8226	0.95	[0.63, 1.45]
Other				.1052	0.81	0.63, 1.04				.1181	0.82	[0.64, 1.05]
Sexual identity
Heterosexual					1.00						1.00
Gay/Lesbian				<.0001	0.18***	0.11, 0.29				.0004	0.48**	[0.32, 0.73]
Bisexual				<.0001	0.56***	0.48, 0.67				.0092	0.65**	[0.48, 0.90]
Not SURE				.0285	0.67*	0.47, 0.96				.3732	0.79	[0.47, 1.33]

Source. Pooled 2019 YRBS Data.

Note. OR = odds ratio; CI = confidence interval; aOR = adjusted OR.

p < .05. **p < .01. ***p < .001.

Males

In unadjusted models (Table 4, Model 1), male students who identified as transgender were significantly less likely to use condoms (OR = 0.43; 95% CI = [0.25, 0.74]) compared with not-transgender male students. After controlling for age, race/ethnicity, and sexual identity (Table 4, Model 2), the significant association remained (aOR = 0.52; 95% CI = [0.30, 0.88]). 15- and 16-year-old male students were significantly more likely to use condoms than students 18 and older. Finally, gay and bisexual male students had significantly lower odds of using condoms than heterosexual male peers.

Discussion

Disparities in HIV incidence and prevalence between transgender individuals and their not-transgender peers cannot be ignored. However, research with transgender youth related to HIV risk and protective factors remains sparse. Thus, we sought to examine disparities between transgender and not-transgender high-school-aged youth across three dimensions of HIV prevention—HIV testing, HIV education, and condom use.

Contrary to our hypothesis yet consistent with prior findings, transgender youth were significantly more likely than their not-transgender peers to have ever tested for HIV. Regardless of reported sex or potential covariates, transgender youth were approximately twice as likely to have ever tested for HIV—a substantial difference. Male youth who were unsure if they were transgender were also approximately twice as likely to have tested for HIV. These patterns might be a result of HIV prevention and treatment messaging that targets transgender individuals, or due to providers specifically encouraging HIV testing for transgender patients due to known vulnerability (Ibitoye et al., 2021; Sharma et al., 2019). These efforts could only succeed in settings in which gender-affirming messaging and care is available, so future research should explore regional and sociopolitical differences. It is possible that this association might not be maintained over time. Emerging laws that criminalize or prevent the provision of hormone replacement therapy and other forms of social transition to transgender youth promote widespread transphobia which could negatively influence these outcomes. Such laws further a social culture that disincentivizes transgender people from seeking appropriate education and care due to widespread stigmatization and fear of harm at the hands of the state. Longitudinal multilevel modeling and rich qualitative investigation can help researchers understand the precise impact these laws will have on HIV testing behaviors in states such as Florida, Texas, and others that have enacted or plan to enact hostile anti-trans policies. However, such laws should be condemned and resisted on principle by public health researchers regardless of their impact due to their bigotry and the fact that they are antithetical to our field’s goal of health equity.

Consistent with past findings (Longmore et al., 2013), results also indicate a higher prevalence of HIV testing among non-white participants. Similar to associations seen among transgender youth, this elevated prevalence might be due to strategies to promote HIV prevention and treatment in non-white communities (Mathews et al., 2020). Due to sample size limitations, we were unable to explore differences at the intersection of gender modality and race/ethnicity; however, the presence of these parallel findings suggests the need for future intersectional analyses.

Despite the higher relative odds of testing among transgender youth compared with not-transgender youth, only one quarter of all transgender youth reported testing for HIV. In terms of absolute rates of testing in this population, there is a clear need for HIV testing to be bolstered among transgender youth (and youth in general), as the CDC recommends all individuals 13 years and older to be tested for HIV at least once, and populations at higher risk to be tested annually (CDC, 2020c). Minority Stress Theory and the Gender Affirmation Framework would suggest that transgender and unsure youth may experience fear and anxiety associated with seeking care due to anticipated discrimination. These experiences are likely to increase in response to widespread anti-trans social and structural stigmatization. Specifically, although transgender youth’s rates of testing may be elevated compared with not-transgender youth due to perceived risk, absolute rates of testing among transgender youth may still be depressed due to anticipated or actual anti-transgender discrimination at point of care (Kcomt et al., 2020; Meyer, 2003), and these may decrease further due to growing hostility toward transgender people. Creating trans-affirmative health care systems backed by trans-affirming policy remains a necessary step to increase transgender youth’s engagement in HIV prevention.

Fewer differences were found between transgender and not-transgender youth regarding HIV education. Regardless of gender modality, there were no significant differences for female youth. However, male youth who identified as transgender were half as likely to have received HIV education, even after controlling for race/ethnicity, age, and sexual identity. Furthermore, although male students who were unsure if they were transgender had similar differences, this association disappeared within the adjusted model. These findings are in line with our hypotheses and align with interpretations raised by other researchers—U.S. sex education efforts tend to center cisgender individuals, with an eye toward pregnancy prevention and cis-heterosexual sexual partnering, rather than inclusive and holistic sexual health (Bloom et al., 2022). The messaging within these education efforts may be less relevant or engaging to non-cisgender and non-heterosexual students, and thus students may never have received relevant HIV education. It is also plausible that the difference observed between male and female students is due to students being separated by sex for HIV and sexual education (Bigler & Signorella, 2011; Dunstan et al., 2017). Cisnormative, single-sex sexual education may both have separate content and be noninclusive to transgender students, generating the observed disparities in HIV education. For example, transgender male students in our sample may be inaccurately grouped as “girls” in cisnormative sex-segregated spaces, preventing them from accessing relevant HIV education. The fact that this finding was isolated to male students requires additional study.

Finally, condom use at last sex was broadly less likely among transgender and unsure youth than among not-transgender youth. This aligns with our hypothesis and is supported by robust literature (Haley et al., 2019). Research shows that exposure to violence, prejudice, lower self-esteem, and other stress-inducing structural factors may lead to inconsistent condom use among transgender youth (Budhwani et al., 2017; Vaitses Fontanari et al., 2019). Addressing the root causes of these factors should help increase the consistency of condom use among this population.

This study has limitations. Our data are based on a limited number of jurisdictions who asked questions assessing gender modality, HIV testing, HIV education, and condom use; thus, there may be a degree of sampling bias in the data available to us. The diversity of jurisdictions included also presents some uncertainty regarding factors that we did not assess—in particular, although all 50 states allow sexually transmitted disease diagnosis and treatment for minors, whether this is interpreted as including HIV services may vary by state; some states also explicitly allow these services beginning age 12, others at age 14 (CDC, 2019b). As a result, some of the participants in our study may have experienced barriers to accessing testing which we could not have accounted for. Such barriers are likely to increase for transgender people in light of the increasing prevalence of legislation aimed at barring transgender youth from accessing affirming medical care, thus future studies should consider these legal barriers more explicitly.

Furthermore, limitations arise as the study was based on secondary data analyses, and we had no control over questions asked or their wording. Some questions may have been confusing to students and resulted in misclassification. For example, the YRBS sex question fails to specify whether it is assessing birth sex or current/legal sex, and the survey does not include a question about current gender identity. Therefore, we are unable to understand how transgender students whose experiences of sex and gender may not be static understand questions assessing their sex and transgender status. Some youth may have chosen the sex that most closely resembles their current sex or gender while others may have chosen their sex assigned at birth. We are also unable to identify nonbinary students within this dataset, despite available data indicating that nonbinary youth are a significant and meaningful demographic. Moreover, the question assessing HIV education is broad and may not consider context, content, timing, or frequency. Finally, the question assessing condom use is not tied to specific sexual activities, and condom use may not be expected in cases such as sex between individuals with vaginas. The YRBS does not explicitly ask about other prophylactic barrier methods such as dental dams, thus we cannot assess the use of alternate methods of STI prevention.

Conclusion

Transgender individuals remain disproportionately affected by the HIV epidemic in the United States, despite decades of targeted messaging and outreach. Although we found that transgender youth were more likely to have tested for HIV than not-transgender peers, absolute rates are well below CDC recommendations. Condom use and HIV education both remain lower among transgender individuals relative to not-transgender peers. Although substantial work is required to promote HIV prevention for all youth, more focused work is necessary to ensure transgender individuals receive culturally appropriate HIV education and supports to access HIV prevention tools including condoms and testing services. Addressing transphobic stigma as a root cause of HIV inequities remains a high priority.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Institute on Alcohol Abuse and Alcoholism (NIAAA: R01AA024409 and R01AA029044).

ORCID iDs

Gregory Phillips II

Shahin Davoudpour

Caleb W. Curry

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