Patterns of Healthcare Discrimination Among Transgender Help-Seekers

ARTICLE IN PRESS

RESEARCH ARTICLE

Patterns of Healthcare Discrimination Among Transgender Help-Seekers Meghan Romanelli, PhD, LCSW, Michael A. Lindsey, PhD, MSW, MPH

Introduction: Affirmative health care is imperative to address health and mental health disparities faced by transgender communities. Yet, transgender help-seekers experience discrimination that precludes their access to and participation in care. This study uses latent class analysis to examine patterns of healthcare discrimination among transgender help-seekers. Predictors of class membership are investigated to identify subpopulations at highest risk for healthcare discrimination.

Methods: Data were obtained from the 2015 U.S. Transgender Survey and analyzed in 2019. Ten healthcare experiences were included as latent class indicators. Latent class analysis and regression were performed in Mplus, version 8 to identify latent subgroups and examine the relationship between respondent characteristics and the latent classes.

Results: The final sample included 23,541 respondents. A 3-class model fit best: Class 1 experienced overt discrimination and interfaced with providers with limited trans-competence; Class 2 did not experience healthcare discrimination or report issues related to providers’ trans-competence; and Class 3 did not experience discrimination but had providers with low trans-competence. Transmen and respondents who were out as trans to their providers and reported psychological distress, suicidal thoughts, and disabilities were more likely to be members of Class 1 or 3 than Class 2. Conclusions: Experiences of healthcare discrimination are not homogeneous across transgender help-seekers. Predictors of the latent classes indicated that transgender help-seekers holding an additional marginalized identity may be at higher risk for healthcare discrimination or care from providers with limited trans-competence. Targeted engagement and education interventions might improve these transgender help-seekers’ access to and connections with care. Am J Prev Med 2019;000(000):1−9. © 2019 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.

INTRODUCTION

T

a

ransgender (trans) communities have elevated rates of health and mental health (MH) concerns relative to their cisgender counterparts.4,5 Accordingly, access to health and MH care is necessary to identify, monitor, and treat symptoms to prevent their escalation.6−8 Access to trans-affirmative care, moreover, is associated with better outcomes for trans

a Transgender is often used as an umbrella term used to describe individuals whose gender identity or expression does not align with their sex assigned at birth.1 Although trans and nonbinary individuals may have different healthcare needs and experiences,2 following the recommendations of the U.S. Transgender Survey researchers, a single term that encompasses a spectrum of identities is used for consistency and clarity.3

help-seekers (THSs), including decreased depression and suicidality.9 THSs, however, experience significant barriers to affirmative care, many of which stem from discriminatory providers and a dearth of knowledge for how to provide health care to trans communities.10 Among trans people, the rates of discrimination, including healthcare discrimination, vary by race/ethnicity, age, gender, gender expression, ability, MH history, From the McSilver Institute for Poverty Policy and Research, Silver School of Social Work, New York University, New York, New York Address correspondence to: Meghan Romanelli, PhD, LCSW, New York University, Silver School of Social Work, McSilver Institute for Poverty Policy and Research, 41 East 11th Street, Seventh Floor, New York NY 10003. E-mail: [email protected]. 0749-3797/$36.00 https://doi.org/10.1016/j.amepre.2019.11.002

© 2019 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.

Am J Prev Med 2019;000(000):1−9

1

ARTICLE IN PRESS Romanelli and Lindsey / Am J Prev Med 2019;000(000):1−9

2 11−17

and SES. Foundational research has explored the association between these characteristics and singular forms of healthcare discrimination, for example, the relationship between THSs’ age and service denial, verbal harassment, and physical victimization in healthcare settings—each as an independent outcome.12 Yet, THSs may experience different types of healthcare discrimination concurrently or throughout their help-seeking histories.18,19 This study uses latent class analysis (LCA) to simultaneously examine multiple indicators of healthcare discrimination to identify response patterns among THSs. Exploring patterns of healthcare experiences through a person-centered, rather than variable-centered, analysis allows for the identification of subpopulations within the larger heterogeneous population, providing more fine-grained details20,21 related to cumulative risk and the qualities of THSs’ help-seeking histories. This study also investigates predictors of class membership to identify which subgroups of THSs might be at greater risk for healthcare discrimination. LCA with covariates lends itself to identifying individuals with risks for healthcare discrimination. Results may inform targeted interventions, prioritizing efforts, and leveraging resources toward priority populations, that is, specific subgroups of THSs with the highest risk for healthcare discrimination.22

METHODS Study Sample Data were obtained from the U.S. Transgender Survey (USTS),3,23 the largest survey examining the experiences of trans people in the U.S., resulting in 27,715 respondents. Inclusion criteria required U. S. state, territory, or military base residence; age ≥18 years; and trans, nonbinary, or trans-spectrum identity. Data collection was approved by the University of California, Los Angeles’ IRB (IRB#15000961). This secondary data analysis used de-identified data and did not require approval by New York University’s IRB. A pilot study was conducted by the original investigators with the purpose of providing both a substantive and technical evaluation of the survey. A total of 324 questions were included in the final survey. Data were collected in the summer of 2015 via an anonymous online survey. An online-only format was used owing to the length and complexity of the skip logic. To reconcile the biases of using an Internet survey, outreach efforts focused on people who may have limited access to the Internet, including people of color, seniors, rural residents, and lowincome individuals. A multi-pronged recruitment strategy was used to reach these groups through various points of access, including trans-specific organizations, health centers, and online communities. USTS respondents were offered the opportunity to enter into a drawing for 1 of 3 cash prizes upon completion of the survey.3,23 This analysis focused on trans and nonbinary respondents who indicated yes to the following question: In the past year, have you

seen a doctor or other healthcare provider? In answering yes to this question, respondents were prompted to answer the next set of questions that served as the latent class indicators. The subsample used for this analysis included 84.9% (n=23,541) of the original sample.

Measures Ten items were included as indicators. The items expanded upon the healthcare discrimination questions asked in the National Transgender Discrimination Survey24 by providing more detail related to the qualities of THSs’ healthcare encounters. For example, the National Transgender Discrimination Survey item asks if respondents have been verbally harassed at the doctor’s office, whereas the USTS items provide more information about the source of the harassment. Items began with the phrase: In the past year, did you have any of these things happen to you, as a trans person, when you went to see a doctor or healthcare provider? The 10 response items and their frequencies are displayed in Table 1. Sociodemographic latent class predictors included race/ethnicity (white, biracial/multiracial, Latinx/Hispanic, black/African American, Asian/Native Hawaiian/Pacific Islander, Alaska Native/American Indian, and Middle Eastern/North African), gender identity (transwoman, transman, assigned female at birth nonbinary, and assigned male at birth nonbinary), age, insurance status (insured and uninsured), and SES (living at or near poverty: yes or no). Health and MH predictors included having a disability (yes or no), past-year suicidal thoughts (yes or no), and psychological distress. The Kessler Psychological Distress Scale25 was used, asking: During the past 30 days, how often did you feel: (1) so sad that nothing could cheer you up, (2) nervous, (3) restless or fidgety, (4) hopeless, (5) that everything was an effort, and (6) worthless? Respondents indicated: none of the time, a little of the time, some of the time, most of the time, or all the time. Other predictors included visual conformity to the gender binary (people can never or rarely tell that I am trans if I don’t tell them or people can always or most of the time tell that I am trans if I don’t tell them) and outness to healthcare providers. For the latter, respondents indicated if: all or most know that I am trans, some know that I am trans, no one knows that I am trans, or I have no current healthcare provider.

Statistical Analysis Data were analyzed in December 2018 and revised in August 2019. Preliminary analyses were conducted using SPSS, version 25. The LCA and latent class regression were conducted with Mplus, version 8. The survey’s standard weight was applied in Mplus to reflect the U.S. population based on the 2014 American Community Survey. As prior research has used LCA to understand the underlying dimensions of victimization,26 the method has similar utility to examine healthcare discrimination. LCA was chosen over other types of clustering methods for its strengths as a model-based technique; for example, fit statistics are provided for model comparisons, and class probability information is retained allowing for covariates to be included in the model.27,28 An analyze−classify−analyze strategy was used by completing a 3-step approach to modeling with the AUXILIARY R3STEP www.ajpmonline.org

ARTICLE IN PRESS Romanelli and Lindsey / Am J Prev Med 2019;000(000):1−9

3

Table 1. Demographic, Psychosocial, and Latent Class Indicator Characteristics of Unweighted Respondents (n=23,541) Sample characteristics Race/ethnicity White Biracial Hispanic/Latinx Black/African American Asian/Pacific Islander Alaskan Native/Native American Middle Eastern/North African Gender identity Transwoman Transman Nonbinary, assigned female at birth Nonbinary, assigned male at birth Age, mean (SD) At or near poverty Yes Insured Yes Self-identified as person with a disability Yes K6 score, mean (SD) Past 12-month suicidal thoughts Yes Current providers know I’m transgender No current provider No providers know Some providers know All or most providers know Visual conformity to the gender binary People can never or rarely tell I am transgender if I don’t tell them Latent class indicators My doctor knew I was trans and treated me with respect Yes I had to teach my doctor or other healthcare provider about trans people so that I could get appropriate care Yes A doctor or other healthcare provider refused to give me trans-related care Yes A doctor or other healthcare provider refused to give me other care Yes My doctor asked me unnecessary/invasive questions about my trans status that were not related to the reason for my visit Yes A doctor or other healthcare provider used harsh or abusive language when treating me Yes A doctor or other healthcare provider was physically rough or abusive when treating me Yes I was verbally harassed in a healthcare setting Yes I was physically attacked by someone during my visit in a healthcare setting Yes I experienced unwanted sexual contact in a healthcare setting Yes

& 2019

Respondents, n (%) 19,321 (82.1) 1,293 (5.5) 1,208 (5.1) 662 (1.2) 662 (2.8) 282 (1.2) 113 (0.5) 8,237 (35.0) 7,142 (30.3) 6,652 (28.3) 1,510 (6.4) 31.2 (13.3) 7,197 (30.6) 21,281 (90.4) 6,851 (29.1) 10.5 (6.0) 11,448 (48.6) 1,264 (5.4) 6,169 (26.2) 3,695 (15.7) 12,348 (52.5) 12,889 (54.8)

14,246 (60.5)

5,662 (24.1) 1,897 (8.1) 704 (3.0)

3,582 (15.2) 1,159 (4.9) 409 (1.7) 1,368 (5.8) 126 (0.5) 298 (1.3)

ARTICLE IN PRESS 4

Romanelli and Lindsey / Am J Prev Med 2019;000(000):1−9

option in Mplus, whereby the measurement model remained fixed when introducing predictors.29,30 Indicators were treated as categorical variables. An exploratory approach was used, as the exact number of latent classes was unknown. This exploratory approach began with a 1-class model and increased the number of classes until the model no longer improved and the log-likelihood was no longer replicated. An examination of item loadings and model fit indices for estimated latent classes was completed.29,30 The final number of classes was determined by the conceptual meaning; model entropy closest to 1; and the lowest values of log-likelihood, Bayesian Information Criterion (BIC), and Akaike Information Criterion (AIC).30−32 Latent classes with <5% of the sample were not considered because the class would not be generalizable to a broader population.33,34 A key assumption of LCA asserts that indicators are independent of one another, with their relationship to each other only explained by the common influence of the latent variable.21,35 This assumption, however, has often been found to be unrealistic.36 When indicators are similarly worded or logically tied to each other,37 conditional independence can be relaxed to allow for partial conditional independence.38 To assess violations of conditional independence, bivariate residuals of the indicator variables were examined in the TECH10 output of the initial estimated LCA model.39,40 Pearson test statistics ≥30 points indicate violations of model fit39 and can be addressed by correlating the residuals among affected indicator variables.37,40,41 Adding the few residual associations to the model, instead of increasing the number of classes to account for the residuals violating conditional independence, prevents minor residual correlations from interfering with the main modeling and enhances the chance of finding a solution with a best minimum BIC.39 Using multinomial logistic regression (invoked via R3STEP), demographic and psychosocial characteristics were examined as simultaneous predictors of the latent categorical variable.

RESULTS Table 1 details characteristics of the 23,541 respondents in the data analytic sample. The final models were run with 200 random starts and 40 final stage optimizations. The log-likelihood, AIC, BIC, and adjusted BIC decreased until a 5-class solution was found. The 6-class model was examined but considered not trustworthy, as its best log-likelihood value was not replicated. Although the log-likelihood, AIC, BIC, and adjusted BIC were better for 4- and 5-class solutions, each of them included a latent class that contained <5% of the sample. Between the 2- and 3-class models, the 3-class model had highest entropy and the lowest AIC, BIC, and adjusted BIC. A 3-class solution was chosen based on model fit and a clear substantive interpretation of the classes. Items violating conditional independence were relaxed by adding their residual covariances to the model to allow for partial conditional independence.37,39 The residual associations for 4 indicator pairs were added to the model (specific pairs are noted in Figure 1). After these additions, no indicator pairs displayed bivariate misfit. Table 2 displays the model fit indices for 1- to 5-class solutions of the modified models that allowed for partial conditional independence. A 3-class solution was still chosen. Figure 1 displays the probabilities of each indicator within the 3 classes and the overall population. Average posterior probabilities were 0.88 for Class 1, 0.91 for Class 2, and 0.92 for Class 3, suggesting that the indicators adequately predicted class assignment.42,43 To

Figure 1. Three-class model: estimated probabilities by latent class membership. Final number of respondents from the data analytic sample without any missingness on the latent class indicators: n=23,388. The residual associations between the following pairs of indicators were included: 9 and 10; 7 and 9; 3 and 4; and 4 and 9. www.ajpmonline.org

ARTICLE IN PRESS Romanelli and Lindsey / Am J Prev Med 2019;000(000):1−9

5

Table 2. Model Estimation Number of classes 1 2 3 4 5

Log-likelihood

AIC

BIC

Adj. BIC

Smallest class (%)

Entropy

‒61030.67 ‒54884.14 ‒53550.41 ‒53375.38 ‒53215.72

122,089.34 109,818.27 107,172.82 106,844.75 106,547.44

122,202.18 110,019.77 107,462.97 107,223.57 107,014.92

122,157.69 109,940.32 107,348.57 107,074.21 106,830.59

100.00 18.53 6.24 1.75 1.83

Na 0.80 0.76 0.75 0.74

Note: Bold font numbers indicate the 3-class model is chosen. Six-class solution: best log-likelihood value was not replicated. AIC, Akaike Information Criteria; Adj. BIC, sample-size−adjusted BIC; BIC, Bayesian Information Criteria.

examine the quality of each indicator, univariate entropy was calculated.44 Various cut offs for removing indicators from the model have been cited in the literature, ranging from 0.055 to 0.2.45,46 All indicators’ univariate entropy values were above these cut offs (Figure 1). The following 3 classes were identified: 1. Class 1 (6.23%) experienced overt discrimination and the highest proportion of interactions potentially rooted in providers’ low trans-competence (i.e., knowledge and skills related to the health and healthcare needs of THSs). Class 1 had the highest probability (over and above other classes and the population) of being refused care; having to educate providers; being asked invasive questions; and being physically, verbally, and sexually assaulted in healthcare settings. 2. Class 2 (63.08%) did not experience overt discrimination or report issues related to providers’ trans-competence. Class 2 had the lowest probabilities of all indicators. 3. Class 3 (30.69%) did not experience overt discrimination and instead interfaced with providers who were respectful but may have low trans-competence. Class 3 members were treated with respect at the highest rate but also taught their provider about trans people, were asked invasive questions, and were refused trans-related care—factors associated with a provider’s trans-competence47—at higher rates than the population average. Table 3 presents complete results of the regression analysis, including ORs, 95% CIs, and p-values. The following results describe predictors of Class 1 membership compared with Class 2 membership. Respondents who identified as biracial, Alaskan Native/ Native American, and transmen had increased odds for Class 1 membership compared with white and transwomen respondents, respectively. Aging, living at or near poverty, having a disability, increasing psychological distress, and having suicidal thoughts also predicted Class 1 membership. Relative to respondents who indicated that none of their providers knew they were trans, & 2019

odds for Class 1 membership were significantly higher for respondents who indicated that some or most of their providers knew they were trans or that they did not have a regular care provider. Respondents who reported that people could never or rarely tell that they were trans had lower risk of being in Class 1. Predictors of Class 3 membership, relative to Class 2, were also tested. Asian/Pacific Islander respondents had decreased odds of Class 3 membership. The likelihood of Class 3 membership was higher for transmen and respondents with disabilities, increasing psychological distress, and suicidal thoughts. Respondents without a regular provider and those who were out to some or most of their providers were at higher risk of being in Class 3, whereas those who reported that people could never or rarely tell that they were trans had lower risk. Finally, predictors of Class 1 membership, relative to Class 3, were compared. Members of Class 1 were more likely to be biracial or Alaskan Native/Native American. Respondents who were older, living at or near poverty, disabled, reported suicidal thoughts, or had increasing distress all had higher odds of Class 1 membership. Meanwhile, those who were out to some or most of their providers had lower odds of Class 1 membership.

DISCUSSION This study found that experiences of healthcare discrimination are not homogeneous across THSs, as 3 distinct classes emerged from the data. The implications for preventive medicine are twofold. First, engagement interventions may be needed for THSs presenting with histories of overt discrimination. Second, education interventions targeting provider attitudes, biases, and trans-competence are recommended. Findings from the regression analysis also confirmed subgroups of THSs who might be at higher risk for receiving discriminatory care, further specifying those who might be in need of targeted interventions. Healthcare discrimination not only disrupts the receipt of needed care but also exposes THSs to extraneous stress, inhibits patient−provider communication

ARTICLE IN PRESS Romanelli and Lindsey / Am J Prev Med 2019;000(000):1−9

6

Table 3. Multinomial Logistic Regression of Demographic and Psychosocial Variables on Latent Class Membership Latent class predictor Race (ref: white) Biracial Hispanic/Latinx Black/African American Asian/Pacific Islander Alaska Native/Native American Middle Eastern/North African Gender identity (ref: transwoman) Transman Nonbinary, assigned female at birth Nonbinary, assigned male at birth Age At or near poverty Yes Insured Yes Self-identified disability Yes K6 score Past 12-month suicidal thoughts Yes Current providers know I’m transgender (ref: no providers know) Have no current regular providers Some providers know Most providers know Visual conformity to the gender binary (ref: people can always/most of the time tell I’m transgender if I don’t tell them) People can never or rarely tell I am transgender if I don’t tell them

Class 1 vs Class 2 (ref) OR (95% CI)

Class 3 vs Class 2 (ref) OR (95% CI)

Class 1 vs Class 3 (ref) OR (95% CI)

1.76*** (1.34, 2.31) 1.00 (0.71, 1.39) 1.05 (0.70, 1.57) 0.91 (0.58, 1.42) 2.71*** (1.73, 4.24) 1.61 (0.75, 3.47)

0.86 (0.70, 1.05) 0.89 (0.73, 1.09) 0.75 (0.55, 1.02) 0.65** (0.50, 0.86) 1.37 (0.92, 2.02) 1.35 (0.76, 2.41)

2.05*** (1.50, 2.79) 1.12 (0.77, 1.62) 1.40 (0.86, 2.27) 1.39 (0.83, 2.33) 1.98** (1.20, 3.27) 1.19 (0.48, 2.93)

1.42** (1.10, 1.83) 0.91 (0.66, 1.25) 0.66 (0.41, 1.25) 1.01* (1.00, 1.02)

1.53*** (1.34, 1.76) 1.17 (0.97, 1.42) 0.88 (0.67, 1.15) 1.00 (1.00, 1.00)

0.92 (0.70, 1.21) 0.78 (0.54, 1.11) 0.75 (0.44, 1.26) 1.01* (1.00, 1.02)

1.25* (1.02, 1.53)

0.97 (0.84, 1.11)

1.30* (1.04, 1.62)

0.80 (0.60, 1.07)

1.06 (0.86, 1.30)

0.75 (0.54, 1.05)

2.37*** (1.94, 2.89) 1.10*** (1.08, 1.13)

1.40*** (1.22, 1.61) 1.04*** (1.02, 1.05)

1.69*** (1.37.2.10) 1.06*** (1.04, 1.09)

2.02*** (1.58, 2.58)

1.26*** (1.11, 1.44)

1.60*** (1.22, 2.08)

2.33*** (1.46, 3.73) 3.67*** (2.64, 5.10) 5.56*** (4.06, 7.63)

3.92*** (2.65, 5.82) 12.81*** (9.16, 17.91) 22.13*** (16.40, 29.87)

0.60 (0.32, 1.10) 0.29*** (0.18, 0.46) 0.25*** (0.16, 0.39)

0.74** (0.60, 0.91)

0.87* (0.77, 0.98)

0.85 (0.68, 1.07)

Note: Boldface indicates statistical significance (*p≤0.05; **p≤0.01; ***p≤0.001). Class 1: Experiences of overt discrimination and interactions stemming from limitations in provider’s trans-competence. Class 2: No overt discrimination or interactions stemming from limitations in provider’s trans-competence. Class 3: Interactions stemming from provider’s limited trans-competence only.

and trust,16,48 invokes a sense of overall medical mistrust,49 creates negative treatment expectancies,50 and shapes future help-seeking behaviors.51−55 Holding an additional marginalized identity (e.g., biracial and Native THSs or poverty-impacted THSs) was associated with increased risk for experiencing patterns of overt healthcare discrimination. Detailed knowledge of these groups’ help-seeking histories may inform the use of engagement interventions to enhance their access and connections to treatments. Providers should be aware that THSs who present with these histories may experience barriers to accessing preventive services, be at risk for forgone care, and have worse health outcomes.19,49 Engagement interventions have been used with other populations marginalized by the healthcare system (e.g., help-seekers with MH concerns, disabilities, or chronic illnesses or people of color).56−59 Common engagement

strategies used to improve treatment acceptability include eliciting information about past healthcare experiences, rapport building, and cultural acknowledgment.60,61 Similar engagement strategies may be appropriate for THSs, for example, to address medical mistrust or negative expectancies created by healthcare discrimination. More research is needed to identify and test tailored strategies. As another way to promote engagement, office environments should signal trans-inclusivity (e.g., providers should post nondiscrimination policies and use inclusive intake forms).8,19,62 This strategy could be helpful for Class 2, as findings showed members may be less out to their providers; they may have avoided disclosing their gender identity if they feared receiving poor treatment.24,62,63 It is important, however, that THSs feel safe to present authentically in healthcare settings,12 as www.ajpmonline.org

ARTICLE IN PRESS Romanelli and Lindsey / Am J Prev Med 2019;000(000):1−9

identity concealment can preclude full participation in care and access to appropriate care.8,64,65 More research is needed to tease apart the healthcare experiences of Class 2. Though it may be promising that Class 2 members largely avoided discrimination, this may be because there was not an opportunity for the provision of discriminatory care if respondents did not identify as trans or were not identified as trans during the healthcare encounter. Exclusion of trans people from the institutions that shape the healthcare system places the burden of navigating healthcare encounters on THSs.8,62,66−68 It is necessary to broaden the availability of affirmative providers through education interventions targeting trans-competence. These interventions may be appropriate for providers who served Class 3 members, as they seemed open to (indicated by the highest level of respect) but unprepared for THSs. As providers may discriminate against THSs as a way to reassert their authority when they feel uncertain interacting with them,66 education interventions may also be needed for providers who served Class 1 members. Although THSs may choose to seek care at lesbian, gay, bisexual, and transgender (LGBT)-specific health centers to avoid discrimination, this strategy may not be accessible for some groups who were at risk for Class 1 membership (e.g., THSs with disabilities may need specialized care not offered at LGBT-specific health centers47; povertyimpacted THSs may not be able to travel to LGBT health centers, which are found sparsely throughout the U.S. or in urban enclaves8,69−71; and THSs describe a dearth of affirmative providers in neighborhoods where predominantly people of color reside8). Incorporating content like basic community terminology and appropriate clinical interview questions has been connected to providers’ increased knowledge, ability, and willingness to provide care to THSs.72−75 Education alone, however, may not improve trans-competence. Instead, transphobia may predict provider knowledge,76 indicating that interventions addressing provider attitudes and biases are also needed.

Limitations Limitations must be considered when interpreting results. First, the data are cross-sectional and therefore causality cannot be inferred. Second, the USTS used nonprobability sampling strategies and was only distributed via the Internet; respondents are not representative of the U.S. trans population. Data from a large convenience sample of the hard-to-reach and geographically dispersed trans population, however, is progress from what previously has been feasible.77 Indeed, the USTS is the largest study of trans people across the U.S. Third, some indicators may need clarification in future research; for example, my doctor knew I was trans and treated me with respect is a compound answer that should be separated. Future research & 2019

7

may collect additional information related to the indicators, such as the frequency of each incident over the year. Finally, certain groups of THSs, such as those with disabilities, may have increased interactions with the healthcare system that expose them to more opportunities for discrimination. Future research should consider controlling for the frequency of contact with providers when assessing class membership.

CONCLUSIONS The study found distinct patterns of healthcare discrimination among THSs and identified subpopulations that may be at heightened risk for experiencing multiple forms of healthcare discrimination and interacting with providers with low trans-competence. Results also allowed for the identification of priority populations of THSs at greatest risk for healthcare discrimination. These THSs may benefit from targeted interventions to improve their experiences within the healthcare system and ultimately their well-being.

ACKNOWLEDGMENTS The authors thank the respondents of the U.S. Transgender Survey and the members of the Behavioral and Intervention Services Research in Context Lab at the McSilver Institute for their feedback. The preliminary findings of this manuscript were presented as an oral presentation at the National Lesbian, Gay, Bisexual, Transgender, and Queer/Questioning (LGBTQ) Health Conference in Atlanta, Georgia on May 31, 2019. Author contributions: MR conceptualized the study, analyzed the data, and wrote the original draft of the paper. MAL conceptualized the study, conducted the investigation, and reviewed and edited the paper. No financial disclosures were reported by the authors of this paper.

REFERENCES 1. Davidson M. Seeking refuge under the umbrella: inclusion, exclusion, and organizing within the category transgender. Sex Res Soc Policy. 2007;4(4):60–80. https://doi.org/10.1525/srsp.2007.4.4.60. 2. Downing JM, Przedworski JM. Health of transgender adults in the US, 2014−2016. Am J Prev Med. 2018;55(3):336–344. https://doi.org/ 10.1016/j.amepre.2018.04.045. 3. James S, Herman JL, Rankin S, Keisling M, Mottet L, Anafi M. The Report of the 2015 US Transgender Survey. Washington, DC: National Center for Transgender Equality, 2016. 4. Bockting WO, Miner MH, Swinburne Romine RE, Hamilton A, Coleman E. Stigma, mental health, and resilience in an online sample of the US transgender population. Am J Public Health. 2013;103(5):943– 951. https://doi.org/10.2105/AJPH.2013.301241. 5. Song Y, Sevelius J, Guzman R, Colfax G. Substance use and abuse. In: Makadon H, Mayer K, Potter J, Goldhammer H, eds. The Fenway Guide to Lesbian, Gay, Bisexual, and Transgender Health. Philadelphia, PA: American College of Physicians, 2008:209–248. 6. Burgess D, Lee R, Tran A, Van Ryn M. Effects of perceived discrimination on mental health and mental health services utilization among

ARTICLE IN PRESS 8

7.

8.

9.

10.

11.

12.

13.

14.

15.

16.

17.

18.

19.

20.

21.

22.

23.

24.

Romanelli and Lindsey / Am J Prev Med 2019;000(000):1−9 gay, lesbian, bisexual and transgender persons. J LGBT Health Res. 2008;3(4):1–14. https://doi.org/10.1080/15574090802226626. Substance Abuse and Mental Health Services Administration (SAMHSA). Top Health Issues for LGBT Populations Information & Resource kit. Rockville, MD: SAMHSA, 2012. Romanelli M, Hudson KD. Individual and systemic barriers to health care: perspectives of lesbian, gay, bisexual, and transgender adults. Am J Orthopsychiatr. 2017;87(6):714–728. https://doi.org/10.1037/ort0000306. Kattari SK, Walls NE, Speer SR, Kattari L. Exploring the relationship between transgender-inclusive providers and mental health outcomes among transgender/gender variant people. Soc Work Health Care. 2016;55(8):635–650. https://doi.org/10.1080/00981389.2016.1193099. Stotzer RL, Silverschanz P, Wilson A. Gender identity and social services: barriers to care. J Soc Serv Res. 2013;39(1):63–77. https://doi.org/ 10.1080/01488376.2011.637858. Reisner SL, Pardo ST, Gamarel KE, White Hughto JMW, Pardee DJ, Keo-Meier CL. Substance use to cope with stigma in healthcare among US female-to-male trans masculine adults. LGBT Health. 2015;2 (4):324–332. https://doi.org/10.1089/lgbt.2015.0001. Kattari SK, Hasche L. Differences across age groups in transgender and gender non-conforming people’s experiences of health care discrimination, harassment, and victimization. J Aging Health. 2016;28 (2):285–306. https://doi.org/10.1177/0898264315590228. Kattari SK, Walls NE, Speer SR. Differences in experiences of discrimination in accessing social services among transgender/gender nonconforming individuals by (dis) ability. J Soc Work Disabil Rehabil. 2017;16(2):116–140. https://doi.org/10.1080/1536710X.2017.1299661. Mizock L, Fleming MZ. Transgender and gender variant populations with mental illness: implications for clinical care. Prof Psychol Res Pr. 2011;42(2):208–213. https://doi.org/10.1037/a0022522. Shires DA, Jaffee K. Factors associated with health care discrimination experiences among a national sample of female-to-male transgender individuals. Health Soc Work. 2015;40(2):134–141. https://doi.org/ 10.1093/hsw/hlv025. Bradford J, Reisner SL, Honnold JA, Xavier J. Experiences of transgender-related discrimination and implications for health: results from the Virginia Transgender Health Initiative Study. Am J Public Health. 2013;103(10):1820–1829. https://doi.org/10.2105/AJPH.2012.300796. Kattari SK, Walls NE, Whitfield DL, Langenderfer-Magruder L. Racial and ethnic differences in experiences of discrimination in accessing health services among transgender people in the United States. Int J Transgend. 2015;16(2):68–79. https://doi.org/10.1080/15532739.2015.1064336. Fredriksen-Goldsen KI, Simoni JM, Kim HJ, et al. The health equity promotion model: reconceptualization of lesbian, gay, bisexual, and transgender (LGBT) health disparities. Am J Orthopsychiatr. 2014;84 (6):653–663. https://doi.org/10.1037/ort0000030. Romanelli M, Lu W, Lindsey MA. Examining mechanisms and moderators of the relationship between discriminatory health care encounters and attempted suicide among US transgender help-seekers. Admin Policy Ment Health. 2018;45(6):831–849. https://doi.org/ 10.1007/s10488-018-0868-8. Howard MC, Hoffman ME. Variable-centered, person-centered, and person-specific approaches: where theory meets the method. Organ Res Methods. 2018;21(4):846–876. https://doi.org/10.1177/1094428117744021. Collins LM, Lanza ST. Latent Class and Latent Transition Analysis: With Applications in the Social, Behavioral, and Health Sciences. Hoboken, NJ: John Wiley & Sons, 2009. Lanza ST, Rhoades BL. Latent class analysis: an alternative perspective on subgroup analysis in prevention and treatment. Prev Sci. 2013;14 (2):157–168. https://doi.org/10.1007/s11121-011-0201-1. National Center for Transgender Equality. 2015 US Transgender Survey, Methodology Report: Questionnaire Design, Data Collection Methods, Data Cleaning Procedures, Weights, and Other Technical Information. Washington, DC: National Center for Transgender Equality, 2017. Grant JM, Mottet L, Tanis JE, Harrison J, Herman J, Keisling M. Injustice at Every Turn: A Report of the National Transgender

25.

26.

27.

28.

29.

30.

31.

32.

33. 34.

35.

36.

37.

38.

39.

40.

41.

42.

43.

Discrimination Survey. Washington, DC: National Center for Transgender Equality and National Gay and Lesbian Task Force, 2011. Kessler RC, Green JG, Gruber MJ, et al. Screening for serious mental illness in the general population with the K6 screening scale: results from the WHO World Mental Health (WMH) survey initiative. Int J Methods Psychiatr Res. 2010;19(suppl 1):4–22. https://doi.org/10.1002/mpr.310. Nylund K, Bellmore A, Nishina A, Graham S. Subtypes, severity, and structural stability of peer victimization: what does latent class analysis say? Child Dev. 2007;78(6):1706–1722. https://doi.org/10.1111/j.14678624.2007.01097.x. Vermunt JK, Magidson J. Latent class cluster analysis. In: Hagenaars JA, McCutcheon AL, eds. Applied Latent Class Analysis. Cambridge, United Kingdom: Cambridge University Press, 2002:89–106. Magidson J, Vermunt J. Latent class models for clustering: a comparison with K-means. Can J Mark Res. 2002;20(1):36–43. pdfs.semanticscholar. org/6add/265688cde63766bed6b920c4546e7c11ab99.pdf. Accessed September 6, 2019. Vermunt JK. Latent class modeling with covariates: two improved three-step approaches. Polit Anal. 2010;18(4):450–469. https://doi. org/10.1093/pan/mpq025. Asparouhov T, Muthen BO. Auxiliary variables in mixture modeling: 3-step approaches using Mplus. Mplus web notes 2013; https://www. statmodel.com/download/webnotes/webnote15.pdf. Published February 7, 2013. Accessed November 8, 2017. Lo Y, Mendell NR, Rubin DB. Testing the number of components in a normal mixture. Biometrika. 2001;88(3):767–778. https://doi.org/ 10.1093/biomet/88.3.767. Nylund KL, Asparouhov T, Muthen BO. Deciding on the number of classes in latent class analysis and growth mixture modeling: a Monte Carlo simulation study. Struct Equ Model. 2007;14(4):535–569. https://doi.org/10.1080/10705510701575396. Finch H, Bolin J. Multilevel Modeling Using Mplus. Boca Raton, FL: CRC Press, Taylor & Francis Group, 2017. Bauer DJ, Curran PJ. Distributional assumptions of growth mixture models: implications for overextraction of latent trajectory classes. Psychol Methods. 2003;8(3):338–363. https://doi.org/10.1037/1082989X.8.3.338. Lanza ST, Flaherty BP, Collins LM. Latent class and latent transition analysis. In: Weiner IB, editor. Handbook of Psychology. Hoboken, NJ: John Wiley & Sons, 2003:663–685. Hagenaars JA. Categorical causal modeling: latent class analysis and directed log-linear models with latent variables. Sociol Methods Res. 1998;26(4):436–486. https://doi.org/10.1177/0049124198026004002. Muthen B. MPlus discussion board: latent profile analysis. www.statmodel.com/discussion/messages/13/115. Published 2007. Accessed March 5, 2019. Uebersax JS. Probit latent class analysis with dichotomous or ordered category measures: conditional independence/dependence models. Appl Psychol Meas. 1999;23(4):283–297. https://doi.org/10.1177/ 01466219922031400. Asparouhov T, Muthen B. Residual associations in latent class and latent transition analysis. Struct Equ Model. 2015;22(2):169–177. https://doi.org/10.1080/10705511.2014.935844. Muthen B. MPlus discussion board: local independence assumption. www.statmodel.com/discussion/messages/13/3895.htmlTag edEn . Published 2009. Accessed March 5, 2019. Muthen B. MPlus discussion board: latent profile analysis. www.statmodel.com/discussion/messages/13/115. Published 2004. Accessed March 5, 2019. Masyn K. Latent class analysis and finite mixture modeling. In: Little TD, editor. The Oxford Handbook of Quantitative Methods in Psychology. New York, NY: Oxford University Press, 2013:551– 611. Wang J, Wang X. Structural Equation Modeling: Applications Using Mplus. Hoboken, NJ: John Wiley & Sons, 2012.

www.ajpmonline.org

ARTICLE IN PRESS Romanelli and Lindsey / Am J Prev Med 2019;000(000):1−9 44. Asparouhov T, Muthen B. Variable-specific entropy contribution. www.statmodel.com/download/UnivariateEntropy.pdfTag edEn . Published 2018. Accessed November 11, 2019. 45. Mawditt C, Sacker A, Britton A, Kelly Y, Cable N. The clustering of health-related behaviours in a British population sample: testing for cohort differences. Prev Med. 2016;88:95–107. https://doi.org/ 10.1016/j.ypmed.2016.03.003. 46. Perry LA, Dover TJ, Lancaster SL, Allen NE, Keel TG, Eliopulos LN. Development of a sexual assault event typology based on event attributes. Crim Justice Behav. 2018;45(11):1709–1722. https://doi.org/ 10.1177/0093854818784617. 47. Hudson KD. (Un) doing transmisogynist stigma in health care settings: experiences of ten transgender women of color. J Progress Hum Serv. 2019;30(1):69–87. https://doi.org/10.1080/10428232.2017.1412768. 48. Malebranche DJ, Peterson JL, Fullilove RE, Stackhouse RW. Race and sexual identity: perceptions about medical culture and healthcare among black men who have sex with men. J Natl Med Assoc. 2004;96(1):97–107. www.ncbi.nlm.nih.gov/pmc/articles/PMC2594754/pdf/jnma00301-0099. pdf. Accessed September 6, 2019. 49. D’Avanzo PA, Bass SB, Brajuha J, et al. Medical mistrust and PrEP perceptions among transgender women: a cluster analysis. Behav Med. 2019;45(2):143–152. https://doi.org/10.1080/08964289.2019.1585325. 50. Rood BA, Reisner SL, Surace FI, Puckett JA, Maroney MR, Pantalone DW. Expecting rejection: understanding the minority stress experiences of transgender and gender-nonconforming individuals. Transgend Health. 2016;1(1):151–164. https://doi.org/10.1089/trgh.2016.0012. 51. Cruz TM. Assessing access to care for transgender and gender nonconforming people: a consideration of diversity in combating discrimination. Soc Sci Med. 2014;110:65–73. https://doi.org/10.1016/j. socscimed.2014.03.032. 52. Jaffee KD, Shires DA, Stroumsa D. Discrimination and delayed health care among transgender women and men: implications for improving medical education and health care delivery. Med Care. 2016;54 (11):1010–1016. https://doi.org/10.1097/MLR.0000000000000583. 53. Kenagy GP. Transgender health: findings from two needs assessment studies in Philadelphia. Health Soc Work. 2005;30(1):19–26. https:// doi.org/10.1093/hsw/30.1.19. 54. Roberts TK, Fantz CR. Barriers to quality health care for the transgender population. Clin Biochem. 2014;47(10−11):983–987. https://doi. org/10.1016/j.clinbiochem.2014.02.009. 55. Shipherd JC, Green KE, Abramovitz S. Transgender clients: identifying and minimizing barriers to mental health treatment. J Gay Lesbian Ment Health. 2010;14(2):94–108. https://doi.org/10.1080/19359701003622875. 56. Smelson D, Kalman D, Losonczy MF, et al. A brief treatment engagement intervention for individuals with co-occurring mental illness and substance use disorders: results of a randomized clinical trial. Commun Ment Health J. 2012;48(2):127–132. https://doi.org/10.1007/ s10597-010-9346-9. 57. Jochems EC, Mulder CL, van Dam A, et al. Motivation and treatment engagement intervention trial (MotivaTe-IT): the effects of motivation feedback to clinicians on treatment engagement in patients with severe mental illness. BMC Psychiatry. 2012;12:209. https://doi.org/ 10.1186/1471-244X-12-209. 58. Ramos SR, Warren R, Shedlin M, Melkus G, Kershaw T, Vorderstrasse A. A framework for using eHealth interventions to overcome medical mistrust among sexual minority men of color living with chronic conditions. Behav Med. 2019;45(2):166–176. https://doi.org/10.1080/ 08964289.2019.1570074. 59. Lindsey MA, Bowery E, Smith K, Stiegler K. The Making Connections Intervention (MCI) Manual: Improving Treatment Acceptability Among Adolescents in Mental Health Services. Baltimore, MD: University of Maryland, 2009. 60. Lindsey MA, Brandt NE, Becker KD, et al. Identifying the common elements of treatment engagement interventions in children’s mental health services. Clin Child Fam Psychol Rev. 2014;17(3):283–298. https://doi.org/10.1007/s10567-013-0163-x.

& 2019

9

61. Dixon LB, Holoshitz Y, Nossel I. Treatment engagement of individuals experiencing mental illness: review and update. World Psychiatry. 2016;15(1):13–20. https://doi.org/10.1002/wps.20306. 62. Bauer GR, Hammond R, Travers R, Kaay M, Hohenadel KM, Boyce M. “I don’t think this is theoretical; this is our lives”: how erasure impacts health care for transgender people. J Assoc Nurs AIDS Care. 2009;20(5):348–361. https://doi.org/10.1016/j.jana.2009.07.004. 63. Bauer GR, Scheim AI, Deutsch MB, Massarella C. Reported emergency department avoidance, use, and experiences of transgender persons in Ontario, Canada: results from a respondent-driven sampling survey. Ann Emerg Med. 2014;63(6):713–720.e1. https://doi.org/ 10.1016/j.annemergmed.2013.09.027. 64. Redfern JS, Sinclair B. Improving health care encounters and communication with transgender patients. J Commun Healthc. 2014;7(1):25– 40. https://doi.org/10.1179/1753807614Y.0000000045. 65. Hendricks ML, Testa RJ. A conceptual framework for clinical work with transgender and gender nonconforming clients: an adaptation of the Minority Stress Model. Prof Psychol Res Pr. 2012;43(5):460–467. https://doi.org/10.1037/a0029597. 66. Poteat T, German D, Kerrigan D. Managing uncertainty: a grounded theory of stigma in transgender health care encounters. Soc Sci Med. 2013;84:22–29. https://doi.org/10.1016/j.socscimed.2013.02.019. 67. Sperber J, Landers S, Lawrence S. Access to health care for transgendered persons: results of a needs assessment in Boston. Int J Transgend. 2005;8(2−3):75–91. https://doi.org/10.1300/J485v08n02_08. 68. Xavier R, Honnold JA, Bradford JB. The Health, Health Related Needs, and Lifecourse Experiences of Transgender Virginians. Richmond, VA: Virginia Department of Health, 2007. 69. Martos AJ, Wilson PA, Meyer IH. Lesbian, gay, bisexual, and transgender (LGBT) health services in the United States: origins, evolution, and contemporary landscape. PLoS One. 2017;12(7):e0180544. https:// doi.org/10.1371/journal.pone.0180544. 70. Sanchez NF, Sanchez JP, Danoff A. Health care utilization, barriers to care, and hormone usage among male-to-female transgender persons in New York City. Am J Public Health. 2009;99(4):713–719. https:// doi.org/10.2105/AJPH.2007.132035. 71. White Hughto JM, Rose AJ, Pachankis JE, Reisner SL. Barriers to gender transition-related healthcare: identifying underserved transgender adults in Massachusetts. Transgend Health. 2017;2(1):107–118. https://doi.org/10.1089/trgh.2017.0014. 72. Erich SA, Boutte-Queen N, Donnelly S, Tittsworth J. Social work education: implications for working with the transgender community. J Baccalaureate Soc Work. 2007;12(2):42–52. https://doi.org/10.18084/ 1084-7219.12.2.42. 73. Kelley L, Chou CL, Dibble SL, Robertson PA. A critical intervention in lesbian, gay, bisexual, and transgender health: knowledge and attitude outcomes among second-year medical students. Teach Learn Med. 2008;20(3):248–253. https://doi.org/10.1080/ 10401330802199567. 74. Rutherford K, McIntyre J, Daley A, Ross LE. Development of expertise in mental health service provision for lesbian, gay, bisexual and transgender communities. Med Educ. 2012;46(9):903–913. https://doi.org/ 10.1111/j.1365-2923.2012.04272.x. 75. Lelutiu-Weinberger C, Pollard-Thomas P, Pagano W, et al. Implementation and evaluation of a pilot training to improve transgender competency among medical staff in an urban clinic. Transgend Health. 2016;1(1):45–53. https://doi.org/10.1089/ trgh.2015.0009. 76. Stroumsa D, Shires DA, Richardson CR, Jaffee KD, Woodford MR. Transphobia rather than education predicts provider knowledge of transgender health care. Med Educ. 2019;53(4):398–407. https://doi. org/10.1111/medu.13796. 77. Miner MH, Bockting WO, Romine RS, Raman S. Conducting internet research with the transgender population: reaching broad samples and collecting valid data. Soc Sci Comput Rev. 2012;30(2):202–211. https://doi.org/10.1177/0894439311404795.