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. Author manuscript; available in PMC: 2024 Jun 1.
Published in final edited form as: Cult Health Sex. 2022 Jun 23;25(6):681–697. doi: 10.1080/13691058.2022.2086709

Structural Vulnerability as a Conceptual Framework for Transgender Health Research: Findings from a Community Needs Assessment of Transgender Women of Colour in Detroit

Wesley M King a, Laura Jadwin-Cakmak a, Racquelle Trammell a,b, Kristi E Gamarel a
PMCID: PMC9780405  NIHMSID: NIHMS1837598  PMID: 35736653

Abstract

The concept of structural vulnerability explains how systems of oppression drive health inequities by reducing access to survival resources (e.g. food, housing) for marginalised populations. Indicators of structural vulnerability such as housing instability, violent victimisation, and poverty are often interconnected and result from intersectional oppression. We sought to demonstrate the utility of the structural vulnerability framework for transgender health research by examining patterns of structural vulnerability indicators among transgender women of colour in Detroit. We conducted latent class analysis and tested associations between classes and mental health and substance use outcomes. Membership to the Lowest Vulnerability class was negatively associated with post-traumatic stress disorder (PTSD) (aOR=0.10, 95% CI: 0.02–0.59). High Economic Vulnerability membership was associated with daily marijuana use (aOR=4.61, 95% CI: 1.31–16.16). Complex Multi-Vulnerability membership was associated with PTSD (aOR=9.75, 95% CI: 2.55–37.29), anxiety (aOR=4.12, 95% CI: 1.22–13.97), suicidality (aOR=6.20, 95% CI: 1.39–27.70), and club drug use (aOR=4.75, 95% CI: 1.31–17.29). Substantively different findings emerged when testing relationships between each indicator and each outcome, highlighting the value of theoretically grounded quantitative approaches to understanding health inequities. Community-driven interventions and policy changes that reduce structural vulnerability may improve mental health and substance use outcomes among structurally vulnerable trans women of colour.

Keywords: transgender, structural vulnerability, structural determinants of health, mental health, substance use

Introduction

Over the past two decades, scholarship on transgender (trans) people’s health in the USA has rapidly expanded (Sweileh 2018). Simultaneously, public health has directed more attention to the structural and social determinants of health (Bailey et al. 2017; Dalsania et al. 2021; Golden and Wendel 2020). For example, recent theoretical and empirical work has examined the relationship between structural racism and health (Bailey, Feldman and Bassett 2021; Phelan and Link 2015; Bailey et al. 2017), housing deprivation and health (Swope and Hernandez 2019; Lewis, Hernandez, and Geronimus 2019), and the spatial distribution of resources within cities and health (Jennings et al. 2017; Corburn 2017). Trans health research has likewise begun to document and work towards intervening upon structural and social determinants of health (Lacombe-Duncan et al. 2021; Hughto, Meyers et al. 2021; Hill et al. 2018; Glick et al. 2019).

A growing body of work has theorised the importance of using intersectionality as a framework to examine how systems of oppression interact to drive health inequities, including among trans populations (Wesp et al. 2019; Agénor 2020; Bowleg 2008, 2012, 2021). Intersectional thinking considers how the relationships between power systems based on social hierarchies shapes lived experiences and health outcomes (Poteat 2021; Agénor 2020). Intersectionality is therefore particularly useful for research with trans people of colour as members of this population are subjugated both by cisgenderism and racism. However, intersectionality theory does not delineate the mechanisms through which intersecting structural oppressions produce health inequities (Wesp et al. 2019). Understanding these mechanisms can identify points of effective intervention upstream of individual behaviour (Bauer 2014).

The structural vulnerability framework can be used to build on this literature as it provides conceptual guidance for analysing how intersectional structural oppression produces conditions in which stigmatised groups face threats to their health and survival (Bourgois et al. 2017; Quesada, Hart and Bourgois 2011). This framework describes how mutually reinforcing economic, political, and cultural insults become embodied among individuals who occupy subordinated positionalities, driving adverse health outcomes (Castaneda 2013; Quesada, Hart and Bourgois 2011). Observable structural vulnerabilities include poor living and housing conditions, social isolation, financial hardship and violent victimisation (Holmes 2011; Negi et al. 2020; Quesada, Hart and Bourgois 2011). While using the structural vulnerability framework does not necessitate intersectional thinking, intersectionality and structural vulnerability are complementary in that both interrogate societal power dynamics that inequitably distribute harms and benefits across populations.

Due to their positionality at the intersection of systemic racism and cisgenderism, trans women of colour in the USA have reported staggering rates of unemployment, poverty, low educational attainment, incarceration, food insecurity, housing deprivation, and violence (Sherman et al. 2020; Sevelius, Xavier et al. 2021; Chandler et al. 2021; Hirshfield et al. 2019; Bukowski et al. 2019). For example, in a recent study, 63.5% of trans women of colour living in Chicago were unstably housed and 72.4% lacked money for their basic necessities (Hotton et al. 2020). These indictors of structural vulnerability have been each independently associated with adverse health outcomes related to substance use and mental health (Bukowski et al. 2019; Chandler et al. 2021; Sherman et al. 2020; Nuttbrock et al. 2014).

However, structural vulnerabilities are typically co-occurring and mutually reinforcing (Bourgois et al. 2017; Quesada, Hart and Bourgois 2011; Hotton et al. 2020). For example, trans women have described losing employment after coming out, leading to financial hardship and housing loss (Jennings Mayo-Wilson et al. 2020). Similarly, others have described how family rejection can lead to youth homelessness, which drives participation in sex work and increases exposure to physical and sexual violence (Lacombe-Duncan et al. 2021; Greenfield et al. 2020; Alessi et al. 2020). Thus, to understand how structural vulnerability impacts health outcomes among trans women of colour, vulnerabilities should not be analysed independently.

This study seeks to demonstrate the utility of using the structural vulnerability framework to understand distributions of adverse health outcomes among trans populations. We quantitatively examined patterns of structural vulnerability and explore the relationship between structural vulnerability and mental health and substance use outcomes among a sample of trans women of colour in Detroit. These outcomes were selected based on the well-documented health inequities in substance use disorders, mood disorders, and suicidality impacting trans populations (Valentine and Shipherd 2018; Connolly and Gilchrist 2020; Hughto, Quinn et al. 2021). We hypothesised that structurally vulnerable participants would be more likely to exhibit symptoms of depression, anxiety and PTSD and more likely to use substances than less structurally vulnerable participants. Additionally, we expected to see that our operationalisation of structural vulnerability predicts mental health and substance use outcomes more consistently than single indicators (e.g. food insecurity, housing deprivation).

Rather than draw firm conclusions about causes of poor mental health or substance use, this study was intended to demonstrate how future trans health research examining social and structural determinants of health can apply the structural vulnerability framework.

Methods

Data for this study came from a needs assessment survey developed and implemented by the Love Her Collective, a community-academic partnership between researchers at the University of Michigan and the Trans Sistas of Color Project (TSoCP), a community advocacy organisation run by and for trans women of colour in Detroit. All study procedures were approved by the University of Michigan Institutional Review Board.

Researchers and community members developed the survey collaboratively. First, the research team conducted focus groups with community members to better understand their most pressing concerns (Lacombe-Duncan et al. 2021). The data collected in these focus groups, input from community leaders, and feedback from a community advisory board informed topics selected for this survey and item wording.

Between January and September 2020, 60 participants completed the online survey and received a $40 giftcard incentive. To participate, individuals had to be 18 years of age or older, live or work in Detroit, and self-identify as trans women of colour. Participants were recruited primarily through social media (e.g. the TSoCP and Love Her Collective Facebook pages) and word of mouth due to constraints imposed by the COVID-19 pandemic. Of the 60 participants, 24 completed the survey prior to March 10, 2020, the date of the first confirmed COVID-19 cases in Michigan, and 36 completed the survey after this date.

Measures

Demographics

Participants self-reported their race, ethnicity and age.

Indicators of Structural Vulnerability

We identified constructs that mapped onto the eight domains of the Structural Vulnerability Assessment Tool: financial security, residence, risk environment, food access, social network, legal status, education, and discrimination (Bourgois et al. 2017).

Financial security

Perceived financial situation was assessed with a multiple-choice item in which participants reported whether they had enough money to live comfortably, were barely getting by, or were not getting by over the past three months (Sevelius, Chakravarty et al. 2021). Monthly income was assessed with a multiple-choice item in which participants reported their income over the past 30 days across all sources (Gamarel, Sevelius et al. 2020; Nemoto and Operario 2016), which we dichotomised at $1,000 or above.

Residence

Participants reported their current living arrangement from 13 options including ‘I live in housing I own’ and ‘I live temporarily with friends or family.’ From this data, we created a dichotomous variable differentiating participants who reported owning or renting their home from those with other living arrangements. Participants also reported how often they worried about paying for housing over the past 3 months on a 1 (never) to 5 (always) scale (CDC 2015).

Risk environment

Within the structural vulnerability framework, risk environment refers to physical safety from violence and injury (Bourgois et al. 2017). Exposure to intimate partner violence (IPV) in the past year was assessed with one item capturing physical IPV and one item related to sexual IPV. Participants also completed an adapted version of the 9-item victimisation subscale from the Gender Minority Stress and Resilience Measure (GMSRM) (Testa et al. 2015). Participants rated how frequently they experienced specific types of violence including being chased or beaten in the past year because they are a trans woman. Items were averaged for a final score ranging from 1 (never) to 4 (often) (α=0.95).

Food access

Participants reported how often they had to miss meals in the past 3 months because there was not enough money to pay for food on a scale from 1 (never) to 5 (always) (Coleman-Jensen et al. 2020).

Social network

In the context of structural vulnerability, social network primarily refers to ability to access social support. The survey did not contain closely corresponding measures. However, participants completed the six item rejection subscale of the GMSRM (Testa et al. 2015), which assesses social rejection from a variety of sources including family members, partners, and religious communities. Participants rated how often they experienced each form of rejection over the past year on a scale from 1 (never) to 4 (often), and scores were averaged (α= 0.83). A sample item from this scale is ‘Over the past year, how often have you been rejected at school or work because you are a transgender woman?’

Legal status

For trans people, legal gender affirmation is an important marker of legal status. Legal gender affirmation is the extent to which official identification reflects correct name and gender marker (Reisner, Radix and Deutsch 2016). Participants reported how important this was to them and whether or not their driver’s licence or ID showed their correct information (King and Gamarel 2020). We dichotomised this variable to distinguish those who reported that their identification correctly reflected their gender or that this was not important to them from those who reported that their documents did not correctly identify their gender and that this was important to them.

Education

We operationalised education as participant’s highest reported degree of formal schooling and created a dichotomous variable indicating whether participants completed high school.

Discrimination

We measured discrimination enacted by institutions, organisations or businesses using an adapted version of the discrimination subscale of the GMSRM (Testa et al. 2015), which includes six items regarding discrimination in housing, employment, healthcare, and public accommodations in the past year. Participants rated how frequently they experienced each form of discrimination, and we calculated an average score ranging from 1 (never) to 4 (often) (α=0.91). A sample item from the adapted scale is ‘During the past year, how often have you been denied health services because you are a transgender woman?’

Health Outcomes

We assessed 7 different health outcomes: PTSD symptoms, anxiety symptoms, suicidal ideation, marijuana use, tobacco use, hazardous alcohol use, and club drug use.

Mental health

Participants completed the Post Traumatic Stress Checklist (PCL-2) to assess PTSD symptoms (Lang et al. 2012). Participants were asked to rate the severity of PTSD symptoms on a 1–5 scale over the past month. We averaged the two items, and participants who received a score of 4 or higher were considered to meet clinical criteria for PTSD (Lang et al. 2012). Participants also completed the 7-item Generalised Anxiety Disorder scale (GAD-7) (Spitzer et al. 2006). Participants rated how frequently over the past two weeks they experienced common symptoms of anxiety on a 0–3 scale, and we calculated a total score across all items (α=0.94). Participants who received a score of 10 or higher were considered to meet clinical criteria for anxiety (Plummer et al. 2016). Lastly, a single item assessed whether participants seriously considered attempting suicide over the past year.

Substance use

Participants reported their alcohol use over the past year using the AUDIT-C (Bush et al. 1998) and use of cigarettes, marijuana, and other substances over the past 3 months using the ASSIST (Humeniuk et al. 2008). The AUDIT-C asks about frequency of alcohol use, amount of alcohol used on a typical day, and number of times consuming 6 or more drinks in the past year; participants who received a total score of at least 3 were considered to be hazardous alcohol users (Aalto et al. 2009). Participants who reported smoking at least one cigarette per day over the past 3 months were considered daily tobacco users, and participants who reported using marijuana ‘daily or almost daily’ were considered daily marijuana users. Participants who reported using cocaine, amphetamines, LSD, mushrooms, PCP or Ketamine were considered to have used club drugs in the past 3 months.

Analyses

We calculated descriptive statistics characterising the sample by demographics, indicators of structural vulnerability, and health outcomes. We calculated means and standard deviations for continuous measures and sample proportions for categorical measures. Less than 10% of the sample was missing data across all variables. We then fitted age-adjusted logistic regression models predicting each health outcome by each indicator of structural vulnerability.

We used latent class analysis (LCA) to identify patterns in structural vulnerability indicators within the sample. To identify the number of classes, we fitted models with 1–5 classes on all indicators of structural vulnerability using logistic, ordered logistic, and ordinary least squares functions for dichotomous, categorical, and continuous indicators, respectively. We compared the fit of these models using Akaike information criteria (AIC) and Bayesian information criteria (BIC).

We then calculated the probability of class membership across the sample for the best fitting model and the marginal predicted means and probabilities of all structural vulnerability indicators for each class. Participants’ posterior predicted probability of class membership was entered into age-adjusted logistic regression models predicting each health outcome. To explore COVID-19 impact, we compared participants’ values for all structural vulnerability indicators and health outcomes by survey completion date (before vs. after March 10, 2020) using t-tests and chi square tests as appropriate. We found no significant differences; because of this, survey completion date was not entered as a covariate. All analyses were conducted in STATA 16.1.

Results

Participants ranged in age from 18 to 54 (M=29.0, SD=6.5) years. The majority of participants identified as Black/African American (83.3%), and 16.7% identified their ethnicity as Latina (Table 1).

Table 1.

Sample characteristics

Mean (SD) or Percent (n)
Demographics
Age 29.0 (6.5)
Race
Black/African American 83.3 (50)
Multiracial 6.7 (4)
Other 10.0 (6)
Ethnicity
Latina 16.7 (10)
Not Latina 81.7 (49)
Structural Vulnerability Indicators
Financial Situation
Comfortable 18.3 (11)
Barely Getting By 51.7 (31)
Not Getting By 26.7 (16)
Monthly Income > $1000 30.0 (18)
Own or Rent Home 48.3 (29)
Financial Worry about Housing (1–5) 3.3 (1.5)
Physical IPV 35.0 (21)
Sexual IPV 21.7 (13)
Anti-Trans Victimisation (1–4) 2.1 (1.5)
Food Insecurity (1–5) 2.6 (1.1)
Anti-Trans Rejection (1–4) 2.5 (0.9)
Legal Gender Affirmation Needs Met 41.7 (25)
High School Graduate 73.3 (44)
Anti-Trans Discrimination (1–4) 1.9 (0.9)
Health Outcomes
PTSD Symptoms 26.7 (16)
Anxiety Symptoms 35.0 (21)
Suicidal Ideation 16.7 (10)
Daily Tobacco Use 45.0 (27)
Hazardous Alcohol Use 50.0 (30)
Daily Marijuana Use 45.0 (27)
Club Drug Use 36.7 (22)
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Over half of participants described their financial situation as ‘barely getting by’ (51.7%), and 26.7% indicated ‘not getting by.’ Under a third of participants earned more than $1,000 in the last month (30.0%). Less than half of participants reported owning or renting their home (48.3%), and the mean score on the measure of financial worry about housing was 3.3 (SD=1.5). Over the past year, 35.0% of participants experienced physical IPV, and 21.7% experienced sexual IPV. The mean score on the victimisation score was 2.1 (SD=1.5). Regarding food access, the mean score on the measure of cutting or skipping meals was 2.6 (SD=1.1). The mean rejection score was 2.5 (SD=0.9). Under half of participants had their legal gender affirmation needs met (41.7%). The majority of participants (73.3%) were high school graduates. Finally, the mean discrimination score was 1.9 (SD=0.9) (Table 1).

Regarding health outcomes, 26.7% of participants met clinical criteria for PTSD and 35.0% for anxiety. Furthermore, 16.7% of participants reported suicidal ideation within the past year. Half of participants reported hazardous alcohol use in the last year (50.0%), and 45.0% reported daily tobacco use and daily marijuana use over the past three months. Finally, 36.7% reported club drug use.

Structural Vulnerability Indicators and Health Outcomes

Mental Health

Eight structural vulnerability indicators were associated with PTSD in age-adjusted logistic regression models (Table 2). Participants who reported not getting by financially had higher odds of PTSD than those who reported being financially comfortable (aOR=10.07, 95% CI: 1.004–100.93). Having higher financial worry about housing (aOR=1.88, 95% CI: 1.13–3.15), experiencing physical IPV in the last year (aOR=6.63, 95% CI: 1.69–25.96), experiencing sexual IPV in the last year (aOR=6.31, 95% CI: 1.52–26.11), and anti-trans victimisation (aOR=3.92, 95% CI: 1.74–8.79), rejection (aOR=4.42, 95% CI: 1.76–11.05), and discrimination (aOR=3.11, 95% CI: 1.51–6.39) were also associated with increased odds of PTSD. Finally, being a high school graduate was associated with lower odds of PTSD (aOR: 0.21, 95% CI: 0.07–0.78).

Table 2.

Age-adjusted odds of mental health outcomes by structural vulnerability indicators

PTSD Anxiety Suicidal Ideation
aOR (95% CI) aOR (95% CI) aOR (95% CI)
Financial Situation
Barely Getting By 2.58 (0.28–24.12) 1.79 (0.32–10.03)
Not Getting By 10.07 (1.004–100.93)* 7.28 (1.16–45.86)
Monthly Income >$1k 0.58 (0.17–1.94) 0.20 (0.02–1.71)
Own or Rent Home 0.49 (0.15–1.60) 0.96 (0.33–2.82) 0.56 (0.13–2.30)
Financial Worry about Housing 1.88 (1.13–3.15)* 2.12 (1.31–3.45)** 1.47 (0.84–2.57)
Physical IPV 6.63 (1.69–25.96)** 3.35 (1.04–10.86)* 4.73 (1.03–21.72)*
Sexual IPV 6.31 (1.52–26.11)* 3.83 (1.01–14.51)* 5.92 (1.17–29.91)*
Anti-Trans Victimisation 3.92 (1.74–8.79)** 1.79 (0.96–3.34) 1.76 (0.82–3.80)
Food Insecurity 1.71 (0.97–3.01) 1.59 (0.95–2.67) 2.18 (1.04–4.62)*
Anti-Trans Rejection 4.42 (1.76–11.05)** 1.40 (0.71–2.74) 0.98 (0.41–2.38)
Legal Gender Affirmation 1.05 (0.32–3.40) 1.05 (0.35–3.16) 0.92 (0.22–3.83)
High School Graduate 0.21 (0.06–0.78)* 1.17 (0.34–4.05) 1.14 (0.20–6.46)
Anti-Trans Discrimination 3.11 (1.51–6.39)** 1.91 (1.02–3.56)* 2.26 (1.07–4.77)*
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Note: Empty cells reflect models that could not be run due to perfect prediction. No participants with a monthly income >$1k reported clinically significant PTSD symptoms and no participants who reported being financially comfortable reported suicidal ideation

*

p < 0.05

**

p < 0.01

Financial worry about housing (aOR=2.12, 95% CI: 1.31–3.45), physical IPV (aOR=3.35, 95% CI: 1.04–10.86), sexual IPV (aOR=3.83, 95% CI: 1.01–14.51), and anti-trans discrimination (aOR=1.91, 95% CI: 1.02–3.56) were each associated with increased odds of anxiety. Additionally, physical IPV (aOR=4.73, 95% CI: 1.03–21.72), sexual IPV (aOR=5.92, 95% CI: 1.17–29.91), food insecurity (aOR=2.18, 95% CI: 1.04–4.62), and anti-trans discrimination (aOR=2.26, 95% CI: 1.07–4.77) were also each associated with increased odds of suicidal ideation.

Substance Use

In age-adjusted models, financial worry about housing was associated with 54% higher adjusted odds of daily marijuana use (95% CI: 1.05–2.27), and participants who reported physical IPV had 3.62 times the adjusted odds of hazardous alcohol use than those who did not (95% CI: 1.10–11.87). No indicators were associated with tobacco use or club drug use (Table 3).

Table 3.

Age-adjusted odds of substance use outcomes by structural vulnerability indicators

Daily Marijuana Daily Tobacco Hazardous Alcohol Club Drugs
aOR (95% CI) aOR (95% CI) aOR (95% CI) aOR (95% CI)
Financial Situation
Barely Getting By 2.15 (0.47–9.93) 1.13 (0.25–5.10) 0.61 (0.14–2.64) 0.27 (0.06–1.30)
Not Getting By 2.28 (0.43–12.17) 0.69 (0.13–3.64) 0.65 (0.13–3.22) 0.58 (0.11–3.03)
Monthly Income >$1k 0.64 (0.20–2.00) 0.89 (0.26–3.01) 1.07 (0.34–3.37) 1.68 (0.50–5.60)
Own or Rent Home 0.50 (0.17–1.43) 0.94 (0.31–2.85) 0.49 (0.17–1.42) 0.84 (0.28–2.54)
Financial Worry about Housing 1.54 (1.05–2.27)* 0.99 (0.68–1.44) 1.05 (0.74–1.51) 0.77 (0.53–1.12)
Physical IPV 2.02 (0.66–6.17) 3.39 (0.97–11.88) 3.62 (1.10–11.87)* 2.63 (0.79–8.86)
Sexual IPV 1.11 (0.31–4.01) 3.15 (0.76–13.12) 2.13 (0.54–8.46) 2.35 (0.61–9.00)
Anti-Trans Victimisation 1.00 (0.56–1.79) 1.20 (0.65–2.23) 1.15 (0.63–2.08) 1.82 (0.95–3.47)
Food Insecurity 1.07 (0.67–1.71) 1.30 (0.79–2.12) 1.12 (0.70–1.79) 1.03 (0.63–1.67)
Anti-Trans Rejection 1.26 (0.66–2.40) 0.54 (0.26–1.10) 0.67 (0.34–1.30) 0.94 (0.48–1.84)
Legal Gender Affirmation 0.47 (0.16–1.38) 0.30 (0.09–1.02) 0.49 (0.17–1.45) 1.33 (0.44–4.05)
High School Graduate 0.57 (0.17–1.84) 0.64 (0.18–2.30) 1.91 (0.56–6.44) 0.76 (0.22–2.63)
Anti-Trans Discrimination 0.93 (0.52–1.68) 1.38 (0.73–2.60) 1.12 (0.62–2.02) 1.86 (0.97–3.60)
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*

p < 0.05

Latent Class Analysis

Based on the fit indices shown in Table 4, two and three class models were good representations of the data. We proceeded with a three class model as the BIC is more reliable when AIC and BIC produce conflicting results (Nylund, Asparouhov and Muthen 2007). The marginal predictions for Class 1 signalled lowest vulnerability for 10 of the 12 indictors (Table 5). The marginal predictions for Class 2 signalled greater vulnerability than Class 1, with the exception of physical and sexual IPV. For example, the predicted probability of owning or renting a home in this class was 0.25 (95% CI: 0.10–0.52) compared to 0.95 in Class 1 (95% CI: 0.48–0.99), and the predicted probability of having a monthly income greater than $1,000 was 0.17 (95% CI: 0.06–0.42) compared to 0.63 (95% CI: 0.40–0.82), respectively. Vulnerability appeared highest in Class 3. The predicted probability of physical IPV was 0.81 (95% CI: 0.55–0.94) compared to 0.16 in Class 2 (95% CI: 0.04–0.47) and 0.25 in Class 1 (95% CI: 0.11–0.47). Additionally, the predicted mean for anti-trans discrimination was 3.16 (95% CI: 2.96–3.36), over twice that of Class 2 (1.53, 95% CI: 1.33–1.73) and Class 1 (1.23, 95% CI: 1.06–1.39).

Table 4.

Fit indices of latent class models

Model Log-likelihood Degrees of Freedom AIC BIC
One Class −695.57 18 1427.14 1464.84
Two Class −612.27 32 1288.55 1355.57
Three Class −584.71 43 1255.43 1345.48
Four Class −566.53 58 1249.06 1370.53
Five Class −552.84 76 1257.68 1416.85
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Table 5.

Latent class probabilities and marginal predictions

Class 1: Lowest Vulnerability Class 2: High Economic Vulnerability Class 3: Complex Multi-Vulnerability
Probability of Class Membership 0.39 (0.26–0.54) 0.32 (0.20–0.46) 0.29 (0.19–0.42)
Structural Vulnerability Indicators Predicted Probability 95% CI Predicted Probability 95% CI Predicted Probability 95% CI
Financial Situation
Comfortable 0.42 0.23–0.63 0 - 0.08 0.01–0. 40
Barely Getting By 0.58 0.37–0.77 0.64 0.39–0.83 0.35 0.17–0.60
Not Getting By 0 - 0.36 0.17–0.61 0.57 0.33–0.78
Past Month Income > $1k 0.63 0.40–0.82 0.17 0.06–0.42 0 -
Own or Rent Home 0.95 0.48–0.99 0.25 0.10–0.52 0.12 0.03 – 0.37
Past Year Physical IPV 0.25 0.11–0.47 0.16 0.04–0.47 0.81 0.55–0.94
Past Year Sexual IPV 0.16 0.06–0.39 0.09 0.01–0.44 0.57 0.32–0.79
Legal Gender Affirmation Needs Met 0.48 0.29–0.69 0.32 0.15–0.57 0.48 0.26–0.71
High School Graduate 1 - 0.47 0.25–0.71 0.65 0.41–0.84
Predicted Mean 95% CI Predicted Mean 95% CI Predicted Mean 95% CI
Financial Worry about Housing (1–5) 2.48 1.90–3.07 3.92 3.30–4.54 3.87 3.19–4.55
Past Year Anti-Trans Victimisation (1–4) 1.37 0.15–1.59 1.96 1.69–2.22 3.20 2.94–3.46
Food Insecurity (1–5) 2.15 1.72–2.57 2.64 2.14–3.14 3.21 2.72–3.71
Past Year Anti-Trans Rejection (1–4) 2.08 1.76–2.41 2.48 2.11–2.84 3.09 2.72–3.45
Past Year Anti-Trans Discrimination (1–4) 1.23 1.06–1.39 1.53 1.33–1.73 3.16 2.96–3.36
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Based on these patterns, Class 3 was referred to as the ‘Complex Multi-Vulnerability Class’, Class 2 the ‘High Economic Vulnerability Class’, and Class 1 the ‘Lowest Vulnerability Class’. The probability of belonging to the Complex Multi-Vulnerability Class was 0.29 (95% CI: 0.19–0.42), the High Economic Vulnerability Class 0.32 (0.20–0.46), and the Lowest Vulnerability Class 0.39 (0.26–0.54). Participants who completed the survey prior to March 10, 2020 (n=24), did not have a different probability of belonging to the Complex Multi-Vulnerability class (t=0.60, df=58, p=0.55), High Economic Vulnerability Class (t=−1.33, df=58, p=0.19), or Lowest Vulnerability Class (t=0.67, df=58, p=0.51) than those who completed the survey after this date.

Structural Vulnerability Classes and Health Outcomes

As shown in Table 6, probability of membership to the Complex Multi-Vulnerability Class was associated with increased odds of PTSD (aOR=9.75, 95% CI: 2.55–37.29), anxiety (aOR=4.12, 95% CI: 1.22–13.97), suicidal ideation (aOR=6.20, 95% CI: 1.39–27.70), and club drug use (aOR=4.75, 95% CI: 1.31–17.29) in age-adjusted logistic regression models. Likewise, probability of membership of the High Economic Vulnerability class was associated with increased odds of daily marijuana use (aOR=4.61, 95% CI: 1.31–16.16). Membership to the Lowest Vulnerability class was associated with decreased odds of PTSD (aOR=0.10, 95% CI: 0.02–0.59).

Table 6.

Age-adjusted odds of health outcomes based on posterior probability of class membership

Health Outcome Class 1: Lowest Vulnerability Class 2: High Economic Vulnerability Class 3: Complex Multi-Vulnerability
aOR (95% CI) aOR (95% CI) aOR (95% CI)
PTSD 0.10 (0.02–0.59)* 0.67 (0.18–2.58) 9.75 (2.55–37.29)**
Anxiety 0.59 (0.18–1.92) 0.39 (0.10–1.47) 4.12 (1.22–13.97)*
Suicidal Ideation 0.21 (0.03–1.33) 0.56 (0.10–3.08) 6.20 (1.39–27.70)*
Daily Marijuana Use 0.37 (0.12–1.17) 4.61 (1.31–16.16)* 0.67 (0.21–2.18)
Daily Tobacco Use 0.60 (0.18–1.98) 1.43 (0.40–5.07) 1.24 (0.37–4.20)
Hazardous Alcohol Use 0.85 (0.28–2.62) 0.83 (0.25–2.74) 1.42 (0.44–4.60)
Club Drug Use 0.69 (0.21–2.26) 0.27 (0.06–1.14) 4.75 (1.31–17.29)*
Open in a new tab
*

p<0.05

**

p<0.01

Discussion

Findings from this cross-sectional study of trans women of colour in Detroit indicate this community faces high degrees of structural vulnerability across multiple domains: financial security, residence, risk environment, food access, social network, legal status, education and discrimination (Bourgois et al. 2017). Membership to the Complex Multi-Vulnerability Class was positively associated with PTSD, anxiety, suicidal ideation, club drug use, and membership to the High Economic Vulnerability Class was positively associated with daily marijuana use. Membership to the Lowest Vulnerability class was negatively associated with PTSD.

Individually, most structural vulnerability indicators were associated with mental health outcomes in the expected direction. For example, consistent with prior research, completing high school was independently associated with PTSD, indicating a potential protective effect of socioeconomic position (Polimanti et al. 2019). However, the relationship between structural vulnerability and substance use outcomes was more apparent in LCA than when analysing indicators individually. Therefore, this study demonstrates the utility of the structural vulnerability framework and importance of choosing statistical approaches that account for the relationships between interconnected social and structural determinants of health when conducting research with trans women of colour (Hotton et al. 2020).

The structural vulnerability framework can be used to understand the manifestations of the interlocking systems of social, economic and political power (e.g. intersectional oppression) that shape health inequities and identify sources of structural inequality that can be ameliorated through policy change and reallocation of resources (Quesada, Hart and Bourgois 2011). Structural vulnerability indicators related to the domains of financial status, residence, risk environment, education and discrimination were particularly useful in distinguishing latent classes. This finding suggests that structural vulnerability among trans women of colour may be reduced through interventions that support housing stability, safety from violence, and equitable access to resources critical to wellbeing such as healthcare and employment.

However, trans women of colour are routinely denied access to services with the potential to reduce structural vulnerability such as housing, education and food assistance programmes (Aspani 2018; Kopansky 2014–2015; Rosentel et al. 2020; Russomanno, Patterson and Jabson 2019; Hudson 2018; Jennings Mayo-Wilson et al. 2020; Lacombe-Duncan et al. 2021). Trans activists have called for changes to policies that govern the administration of sex and gender in state surveillance and healthcare, social service, and public benefit programmes (Spade 2015; Quinan 2017). Policies that promote gender classification and segregation create barriers to services that the most structurally vulnerable trans people need and create conditions in which they are subject to increased scrutiny and violence through state surveillance systems (e.g. policing, incarceration) (Spade 2015; Quinan 2017; Yarbrough 2021). As such, eliminating or significantly altering the administration of gender in these settings may both prevent and alleviate structural vulnerability for trans women of colour (Spade 2015). These changes might include, for example, making procedures for name and gender marker changes more accessible, removing requirements that forms of identification (e.g., birth certificates, driver’s licences) must have consistent name and gender markers to prove identity, and creating gender-neutral, trans-inclusive services.

Additionally, communities have organised networks that distribute resources to alleviate structural vulnerabilities for those in need. The Black Trans Fund, a national community-led philanthropic organisation, supports numerous Black trans social justice organisations across the country who operate emergency assistance, bail assistance, shelter, and healthcare access programmes (Groundswell Fund 2021). In Detroit, these funds helped TSoCP organise care packages and provide emergency financial assistance to community members impacted by COVID-19 (Knoppow 2020). Evaluating the public health impact of these community-led initiatives may provide evidence needed to direct funding, research, and programming efforts towards scaling effective programmes that reduce structural vulnerability for trans women of colour (Gamarel et al. 2022).

Limitations

Because this study was conducted with a small sample of trans women of colour in Detroit during the start of the COVID-19 pandemic, our findings have limited generalisability. Furthermore, the sample size may have impeded detection of additional latent classes (Tekle, Gudicha and Vermunt 2016). Additionally, because our recruitment efforts relied on the social networks of the research team, trans women of colour who are not connected to TSoCP had a lower chance of being sampled, further limiting generalisability. Finally, the survey was developed for needs assessment and lacked measures of important structural vulnerability constructs relevant to this population such as criminal justice system involvement. Although community members adapted the violence and discrimination measures to reflect their experiences of transgender women, our application of intersectionality may be limited by not assessing additional axes of oppression (e.g. racism, ableism). Future research with trans women of colour should consider using discrimination measures developed for populations with multiple marginalised identities such as the Intersectional Discrimination Index to better capture how systems of power render this population structurally vulnerable (Scheim and Bauer 2019).

Conclusion

Findings from this study demonstrate the value of using the structural vulnerability framework to identify mechanisms of intersectional oppression that produce health inequities impacting trans women of colour. Incorporating structural vulnerability shifts the focus of health research from behavioural and psychological processes towards structural, political, or community factors that influence health (Gamarel, King et al. 2020). Consequently, the onus for change is on systems that perpetuate racism and cisgenderism and drive structural vulnerability. Further research using rigorous qualitative and quantitative methods such as LCA should extend this work to better understand and intervene upon indicators and patterns of structural vulnerability among trans women of colour.

Acknowledgements

This work was funded by a grant from the University of Michigan Institute for Research on Women and Gender and supported in part by a National Institute of Aging training grant to the Population Studies Center at the University of Michigan (T32AG000221). The authors are grateful to Love Her Collective team members, community advisory board members, and the individuals who participated in this study.

Footnotes

Declaration of Interests

The authors have no conflicts of interest to declare.

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