Abstract
Within the related epidemics of sex exchange, drug use, and poverty, access to health care is shaped by intersecting identities, policy, and infrastructure. This study uses a unique survey sample of young adults in Detroit, who are exchanging sex on the street, in strip clubs, and at after-hours parties and other social clubs. Factors predicting access to free or affordable health care services, such as venue, patterns of sexual exchange influence, drug use and access to transportation, were examined using multivariable logistic regression and qualitative comparative analysis. The most significant predictors of low access to health care services were unstable housing and lack of access to reliable transportation. In addition, working on the street was associated with decreased access to services. Coordinated policy and programming changes are needed to increase health care access to this group, including improved access to transportation, housing, and employment, and integration of health care services.
Keywords: Health care access, Sex work, Transportation, Housing instability, Health disparities
The often unmet health care needs of sex workers are well documented [1] [2, 3]. Individuals exchanging sex for money or other necessities identify needs across the spectrum of medical care, including drug detox and treatment, pregnancy/reproductive care, HIV/hepatitis/STI care, physical trauma, general medical, and dental and eye care [4]. After shelter and employment, among both homeless and housed sex workers, physical health care and mental health counseling were identified as the highest priority social service needs [4]. Despite this, sex workers often avoid health care due to negative past experiences in the health care system or anticipation of stigma. When they do present to care, they may be hesitant to disclose their involvement in sex work due to previous negative experiences, fear of disapproval and discrimination by health care providers, and embarrassment or shame [1, 4, 5]. In addition, sex workers may not access health care services due to a lack of awareness of service availability, difficulty managing time, street life distractions, mental or emotional health problems, fear of arrest, and frustration accessing services that are not adequately tailored to individuals at the intersection of multiple marginalized identities [4].
The barriers to access to health care for sex workers are amplified by the interactions between sexual violence, sexually transmitted infections, substance use, marginalization, stigma, and poverty. As described by Singer, these constitute a syndemic, a set of “enmeshed and mutually enhancing health problems that, working together in a context of deleterious social and physical conditions that increase vulnerability, significantly affect the [access to health care in] a population.” [6, p. 15]. Difficulties accessing care due to sex work are compounded by the inability of many street-based sex workers to meet basic needs for shelter, food, and safety [4, 7]. For sex workers who use drugs, the economic and physical toll of drug use, as well as social stigma also complicate access to care. Intersecting social conditions patterned on race, class, gender identity, and sexuality largely determine how and where sex work takes place and shape individuals’ experiences with drug use, violence, discrimination, and access to resources [8]. For example, in a sample of cisgender women working in strip clubs, Black/African American women and women using drugs were more likely to report exchanging sex in addition to stripping [9]. A qualitative time-geography study of Black/African American cisgender men who have sex with men identified paths and routines that were determined by employment status and addiction, with some of those exchanging sex to support their drug use reporting that only in spaces of street-based sex work did they feel free from judgment or stigma [10]. Transgender young adults face particularly harsh stigma in many aspects of their lives, including employment discrimination, and are more likely than other young adults to engage in sex exchange for basic needs, including health care, to combat the high levels of poverty they frequently experience [11].
The social patterning of access to health care in the Detroit metropolitan area is also shaped by a context of persistent race-based residential segregation. Residential segregation influences access to health-promoting resources such as healthy food and safe spaces for social and physical activity [12]. Concentrated neighborhood disadvantage resulting from extreme segregation affects residents’ ability to access services that support access to health care including reliable transportation. Concentrated neighborhood disadvantage also concentrates exposure to stigma in the health care setting, and few health care providers elect to work in poor, segregated neighborhoods that are perceived as undesirable places to practice medicine. Free clinics that attempt to fill this gap are often understaffed and overwhelmed by local need [12–14].
Funding restrictions, strictly defined target populations, and scarce resources limit the ability of health care systems to provide necessary care for marginalized individuals such as sex workers. Specifically, they challenge organizational capacity and reinforce the structures that directly affect access to health care, and thus underscore the challenges of providing care within the syndemics associated with sex work, drug use, and poverty [2, 7]. For example, programs run through departments of public health may target transgender women and gay and bisexual cisgender men for the prevention of HIV and other sexually transmitted infections, while separate community-based organizations (CBOs) address the need to document legal identity and citizenship status among Latino/a young adults. Still, other organizations attempt to improve transportation accessibility between the center city and suburban areas, despite needs for all of these services more broadly [4]. Without better coordination, these programs are limited in their ability facilitate access to health care within this marginalized population.
This study focuses on access to care for young people exchanging sex in Detroit as a microcosm of this syndemic. In order to identify points of potential program and policy coordination and intervention, we examine self-reported ability to obtain primary and mental health care, family planning, drug treatment, and HIV/STI testing services across a sample of young adults recruited from areas of the city known for street-based sex work, high- and low-end strip clubs, and social events and after-hours parties. To explore the interrelated predictors of access to care in this population, we used information collected in survey interviews about participant demographics, patterns of sexual exchange, drug use, access to transportation, and employment outside of sex work. Our goals were to understand how participants’ assessments of their access to a variety of services were shaped by their patterns of sexual exchange, drug use, and access to other resources; and to explore the policy and programming implications of these interconnected determinants of access to health care.
Methods
Study Design
The Detroit Youth Passages project is a collaboration between three community-based organizations (CBOs) in the Detroit, Alternatives for Girls, Detroit Hispanic Development Corporation, and Ruth Ellis Center, with the University of Michigan School of Public Health. This model was based on a long history of collaboration between these groups in Detroit [15]. The project seeks to understand how young people’s life circumstances contribute to marginalization and vulnerability in the setting of sexual exchange, and works with partner organizations to bring about change. In addition to the survey of young people living in Detroit engaging in sex work, on which this research is based, the Detroit Youth Passages project also includes ethnographic and Photovoice components addressing detailed participant information and urgent community needs [16–19].
The interviewer-administered survey was designed to broadly explore the themes identified in the ethnographic portion of the project, specifically the context in which transactional sex figured into the lives of the participants. The project aim was to understand the sexual vulnerabilities inherent in contexts navigated by young people of diverse gender, sexuality, and social position. Additionally, the project was specifically targeted toward youth who were uniquely situated with respect to gender and sexual identity and socioeconomic status, including cis and transgender women, and gay and bisexual men with histories of economic and residential instability. Our community partners were focused on better understanding the needs of youth who were not receiving CBO services.
Sampling
A venue-based sample was chosen principally because it operationalized the concept of sexual geographies that, in part, framed the project. Venue-based sampling helped identify these groups who were relatively isolated, small, but distinct target populations of interest [20, 21]. Due to the challenges of recruitment in venues heavily regulated by managers and with the goal of ensuring adequate representation across a wide range of characteristics, respondent-driven sampling was also employed to identify other individuals working in the venues, and participants were encouraged to refer contacts to the study. Prior descriptions of participant recruitment strategies support that venue-based and respondent-driven sampling frameworks may allow for increased sampling of lower SES and other groups that are missed by traditional study samples [20].
Recruitment
The study team met to identify venues that would allow survey sampling of young people, ages 18–30, engaging in transactional sex that included cis- and transgender participants, and participants who were representative of the racial and socio-economic diversity in the city. The community partners also helped to ensure that venues were chosen that would capture respondents not currently connected with their organizations. The four identified venues were the street, after-hours parties and social clubs, high-end strip clubs (described as having more security and a smaller client to performer ratio), and low-end strip clubs.
Based on the ethnographic phase of the project, online searches, and discussion with community partners, sites within each venue were identified for sampling. The specific recruitment site for each participant was not recorded in order to maintain confidentiality. For the street venue, geographic areas of the city known for street-based sex work were identified and respondents were recruited in those areas. For the strip club venues, 28 potential sites were identified. Ten of the sites were determined by our community partners to be remote from or less likely to employ our target sample and were excluded. Two catered exclusively to gay, bisexual, and transgender individuals, and because the structure of the clubs and clientele were considered substantively different from the other strip clubs they were excluded. For the after-hours/social club venue, sites were identified from the ethnographic phase of the study and discussion with the community partners.
Once the sites were identified, preliminary visits were made in which extensive field notes were taken to describe the time and space dynamics of the flow of customers and workers, the availability of space for interviews, and the demographics of the potential participants. During these initial visits, the owners or proprietors of the venue (excepting the street) were approached and the broad scope of the study was described. Several managers declined to allow recruitment at the site, and so, these clubs were also excluded. On selected dates and times that were representative of the venue, a convenience sample of interviews was conducted. Interviews were generally conducted a quiet or private space, where participants felt most comfortable. They were also offered the opportunity to have a private meeting outside of the venue at a later time if they preferred.
The interviews were conducted with 278 respondents by trained interviewers using paper surveys from May 1, 2012, to August 30, 2012. The survey responses were then entered into a secure database by trained research assistants for data cleaning and analysis. The data entry was double checked for coding errors, and then the entire dataset was reviewed by a third individual for inconsistencies.
Measures
In this project, we used a patient-centered approach intended to identify barriers and facilitators of five well-established components of access to health care: approachability, acceptability, availability and accommodation, and affordability [22]. Participants were asked “Do you have free or affordable access to the following?” with indicators of “yes” or “no” to a list of the following services: (1) family planning services; (2) STI or HIV testing; (3) drug treatment services; (4) health care, such as a clinic, hospital, or community health center; and (5) a counselor or a professional to talk to. The affordability domain was additionally assessed using the question “In the past year, have you ever had a situation in which you had to skip medical care because you could not afford it?”
Independent variables included patterns of sexual exchange, demographics, drug use, access to transportation, and having a current source of income. Patterns of sexual exchange were measured in several ways. Participants were asked about a list of possible items for which they had exchanged sex, including cash or money, gang membership, drugs or alcohol, food, clothes, or a cell phone, somewhere to stay, items for your friends, children, or family, transportation, home or care repair, anything else that you could not afford by yourself, or help to stay in the country. There were very few people who indicated exchanging for gang membership, for home/car repair, for immigration help, or in jail, so these were not included in the analysis. The frequency of exchange was measured as the number of exchanges in the past month. There was too much missing data regarding income per night from sex exchange to use this variable. Whether respondents worked for a pimp, madam, or other managerial figure was assessed with the question “Does anyone oversee your [sex exchange] work?”
Race was assessed with a single question asking respondents to select all applicable racial identities and was coded sequentially resulting in the following categories: white, non-Latino/a, Latino/a, Black/African American, and mixed race/other. Sex and gender identity were measured with two questions, the first asking for sex assigned at birth, and the second asking about a “transgender identity.” Based on our work with community partners and community members on our research team, this was considered the most reliable way to identify all transgender individuals, including male-to-female, female-to-male, and gender nonconforming respondents.
Age was measured in years and coded continuously. The highest level of education attained was recorded and then categorized as “Some high school,” “High school diploma or GED,” “Some college,” and “Bachelor degree or more.” Holding a high school diploma or GED was used as the reference category for the analyses. Housing insecurity was measured with a single question, “Are you ever worried about not having a place to stay?” Respondents were asked whether they had access to reliable transportation, with response options of “never,” “rarely,” “most of the time,” and “all of the time.”
Having a current source or sources of income was assessed with two different questions: (1) the respondent was asked if they were “currently working” and (2) they were asked whether they had “other work” beyond whatever they had specified in the first question. After each of these questions the respondents were asked to specify the type of work they did and their freeform responses were recorded. These were combined into a single dichotomous variable indicating whether the respondent had answered yes to either of these two measures.
Information on specific diagnoses, including human immunodeficiency virus (HIV), other sexually transmitted infections, and chronic health conditions was not collected. Our community partners emphasized the importance of limiting the length of the interview in order to minimize survey fatigue, and limiting questions that could be seen as compromising an individual’s ability to continue exchanging sex. Insurance status was also not assessed, nor was use of health care services in the past. This was, in part, due to the primary aim of the survey to describe the context of sexual exchange, where the measurement of access to health care was a secondary goal. Connection with community agencies was also not assessed, as our community partners helped to shape our sampling frame to specifically include respondents who were not already connected to services.
Respondents indicated whether they currently or had ever used alcohol, marijuana, crack (free base form of cocaine that can be smoked), cocaine (other than crack), heroin, meth, speed, bennies, uppers, or other stimulants, ecstasy, LSD, PCP, mushrooms or other hallucinogens, paint thinners, glues, gas, or other inhalants, nonprescribed use of prescription drugs, e.g., Oxycontin, Vicodin, Norco, Dilaudid, Xanax, or Seroquel, and unprescribed hormones or silicone. For items the participant used currently, they indicated whether they used them “rarely,” “weekly,” or “daily.” A composite measure of daily drug use was created, indicating current daily use of any drug other than alcohol or marijuana.
Statistical Analysis
Descriptive statistics for access to each of the health services and the proportion of individuals reporting having skipped health care services due to cost were calculated across venues and demographic variables. Given the significant variation by racial and gender identity across the venues, the decision was made to include venue in all analyses, even for what would conventionally be reported as bivariate relationships. Bivariate relationships, stratified by venue, were calculated using logistic regression on each of the measures of health care access with the measures of demographics, patterns of sexual exchange, drug use, access to transportation, and employment as the independent variables. Next, multivariable logistic regression models for each health care service were constructed including venue, racial identity, and gender identity a priori, as well as all variables that showed statistically significant relationships in the bivariate analyses.
After construction of traditional multivariable regression models, relationships in the data set were explored using qualitative comparative analysis (QCA) with crisp sets. QCA is an analytic strategy which uses set theoretic principles to examine causal complexity in the data by identifying “a 100% fit [model] for as many cases as possible, rather than a very good fit for all of the cases, on average” [23, p. 8]. This method compares sets of cases to identify subsets of cases that define necessary (meaning that the condition must be present for the outcome of interest to occur) or sufficient (meaning that the condition is all that must be present for the outcome of interest to occur) conditions in the data. It is particularly useful that it readily identifies multiple pathways to the outcome of interest and does not require assumptions of a random sample or normal distribution. QCA allows for identification of multiple sets of conditions. For example, it would be possible to identify all of the unique subgroups that have excellent access to health care, rather than generating a description of the average group with access to health care.
QCA emphasizes asymmetric causality, meaning that the conditions which lead to the presence of an outcome may not be the same as those which lead to the absence of an outcome, a useful feature in this analysis where it was hypothesized that the factors which lead to having access to health care would be different from those which lead to not having access to health care; daily drug use, for instance, might decrease access to health care, but not using drugs may not be sufficient to lead to an increase in access to health care.
In the analysis, the focus was on two sets of respondents: (1) those with complete health care access, defined as reporting access to all of the services, and (2) those with incomplete health care access, defined as reporting no access to at least one of the services listed. Analyses were conducted using strict binary groups—individuals with either complete access to health care, or individuals without complete access to health care. All respondents fell into one of these two groups. Two sets of respondents defined by their access to health care were compared with subsets of respondents defined by venue of sexual exchange, racial identity, gender identity, having completed high school, having work, daily drug use, having regular transportation, exchanging for shelter, and working under a “manager.” QCA was performed using the independent fs/QCA software package [24]. Solution subsets within each venue with at least three respondents were retained for the final solution groups. A consistency threshold of 75% was used to defining groups as subsets of the health care access outcomes. Consistency is a conventional QCA measure of the proportion of the respondents in the subset who meet the outcome of interest [25].
Results
Table 1 shows the demographic makeup of the sample. There was marked variation in the distribution of gender identity across venues, with the two strip club venues having nearly entirely cis-gender female participants. There was also variation in racial identity across venues, with all venues except the high end strip clubs having majority Black/African American respondents; the high end strip clubs had slightly more White, non-Latino/a respondents than Black/African American respondents, but each group made up approximately 40% of the total in that venue. The average age of the sample was 24, and this was similar across venues. Nearly two-thirds of the sample had completed high school or a GED, and 70% reported that they were working. The majority of those who reported “currently working” or having “other work” identified work outside of the sex industry, including working in retail, bars/restaurants, health care, and education. Approximately 30% of respondents who were “currently working” or reported having “other work” identified that job as either dancing, stripping, escorting, etc. (data not shown). Approximately 40% were unstably housed, and nearly half had inconsistent access to reliable transportation.
Table 1.
Demographic description of the study sample
| Street | Low-end strip clubs | High-end strip clubs | Social/after-hours clubs | Totala | ||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Mean | (Range) | Mean | (Range) | Mean | (Range) | Mean | (Range) | Mean | (Range) | |
| Age | 24.8 | (18–30) | 23.6 | (18–30) | 23.9 | (19–29) | 23.5 | (18–30) | 24 | (18–30) |
| No. | (Col %) | No. | (Col %) | No. | (Col %) | No. | (Col %) | No. | (Col %) | |
| Gender identity | ||||||||||
| Transgender woman | 35 | (34.0) | 2 | (4.8) | 0 | (−) | 36 | (34.6) | 73 | (26.3) |
| Cisgender man | 32 | (31.1) | 9 | (21.4) | 0 | (−) | 33 | (31.7) | 74 | (26.6) |
| Cisgender woman | 36 | (35.0) | 31 | (73.8) | 29 | (100.0) | 35 | (33.7) | 131 | (47.1) |
| Racial identity | ||||||||||
| Black | 67 | (65.0) | 33 | (78.6) | 11 | (37.9) | 55 | (52.9) | 166 | (59.7) |
| White | 19 | (18.4) | 1 | (2.4) | 12 | (41.4) | 22 | (21.2) | 54 | (19.4) |
| Hispanic | 6 | (5.8) | 1 | (2.4) | 1 | (3.4) | 11 | (10.6) | 19 | (6.8) |
| Mixed/Asian/other | 11 | (10.7) | 7 | (16.7) | 5 | (17.2) | 16 | (15.4) | 39 | (14.0) |
| Highest level of education achieved | ||||||||||
| Some high school | 30 | (29.4) | 7 | (16.7) | 6 | (20.7) | 16 | (15.4) | 59 | (21.3) |
| High school GED | 53 | (52.0) | 11 | (26.2) | 10 | (34.5) | 38 | (36.5) | 112 | (40.4) |
| Some college | 16 | (15.7) | 21 | (50.0) | 12 | (41.4) | 37 | (35.6) | 86 | (31.0) |
| Bachelors degree or more | 3 | (2.9) | 3 | (7.1) | 1 | (3.4) | 13 | (12.5) | 20 | (7.2) |
| Currently working, or has other work | ||||||||||
| No | 60 | (58.3) | 1 | (2.4) | 1 | (3.4) | 19 | (18.3) | 81 | (29.2) |
| Yes | 43 | (41.7) | 40 | (97.6) | 28 | (96.6) | 85 | (81.7) | 196 | (70.8) |
| Worried about not having a place to stay | ||||||||||
| No | 43 | (46.2) | 28 | (68.3) | 21 | (72.4) | 61 | (67.8) | 153 | (60.5) |
| Yes | 50 | (53.8) | 13 | (31.7) | 8 | (27.6) | 29 | (32.2) | 100 | (39.5) |
| Access to reliable transportation | ||||||||||
| Never | 10 | (9.8) | 2 | (4.8) | 1 | (3.4) | 9 | (8.7) | 22 | (7.9) |
| Some of the time | 60 | (58.8) | 8 | (19.0) | 7 | (24.1) | 31 | (29.8) | 106 | (38.3) |
| All of the time | 32 | (31.4) | 32 | (76.2) | 21 | (72.4) | 64 | (61.5) | 149 | (53.8) |
| Totala | 103 | 42 | 29 | 104 | 278 | |||||
aIndividual blocks may not sum to totals if there was missing data
Descriptive data on patterns of sexual exchange are shown in Table 2. The average number of exchanges in the past month was 15.8 (range 0–208) with the most exchanges taking place on the street venue. Most respondents had exchanged for cash, and nearly a quarter to a third had exchanged for either food, clothing, phone, drugs or alcohol, a place to stay, items for friends, children, or family, transportation, or something else that was not listed.
Table 2.
Patterns of sexual exchange in the study sample
| Street | Low-end strip club | High-end strip clubs | Social/after-hours clubs | Totala | ||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Mean | (Range) | Mean | (Range) | Mean | (Range) | Mean | (Range) | Mean | (Range) | |
| Number of exchanges in the past month | 26.4 | (0–208) | 12.3 | (0–30) | 11.8 | (0–24) | 7.1 | (0–55) | 15.8 | (0–208) |
| No. | (Col %) | No. | (Col %) | No. | (Col %) | No. | (Col %) | No. | (Col %) | |
| Items exchanged | ||||||||||
| Cash | 97 | (94.2) | 41 | (97.6) | 28 | (96.6) | 85 | (81.7) | 251 | (90.3) |
| Food, clothing, phone | 41 | (39.8) | 11 | (26.2) | 10 | (34.5) | 27 | (26.0) | 89 | (32.0) |
| Drugs or alcohol | 51 | (49.5) | 12 | (28.6) | 9 | (31.0) | 27 | (26.0) | 99 | (35.6) |
| Place to stay | 41 | (39.8) | 10 | (23.8) | 2 | (6.9) | 21 | (20.2) | 74 | (26.6) |
| Items for friends, children, or family | 31 | (30.1) | 8 | (19.0) | 5 | (17.2) | 9 | (8.7) | 53 | (19.1) |
| Transportation | 30 | (29.1) | 7 | (16.7) | 4 | (13.8) | 16 | (15.4) | 57 | (20.5) |
| Something else | 53 | (51.5) | 13 | (31.0) | 10 | (34.5) | 40 | (38.5) | 116 | (41.7) |
| Anyone oversees your work? | ||||||||||
| No | 77 | (93.9) | 28 | (82.4) | 12 | (50.0) | 19 | (100.0) | 136 | (85.5) |
| Yes | 5 | (6.1) | 6 | (17.6) | 12 | (50.0) | 0 | (−) | 23 | (14.5) |
| Totala | 103 | (100.0) | 42 | (100.0) | 29 | (100.0) | 104 | (100.0) | 278 | (100.0) |
aIndividual blocks may not sum to totals if there was missing data
Drug use among respondents is shown in Table 3. The most commonly used substances were alcohol and marijuana, and only 9% of respondents reported daily use of drugs other than those two.
Table 3.
Drug use in the study sample
| Street | Low-end strip club | High-end strip clubs | Social/after-hours clubs | Totala | ||||||
|---|---|---|---|---|---|---|---|---|---|---|
| No. | (Col %) | No. | (Col %) | No. | (Col %) | No. | (Col %) | No. | (Col %) | |
| Current drug use | ||||||||||
| Alcohol | 49 | (48.0) | 30 | (71.4) | 22 | (75.9) | 78 | (75.7) | 179 | (64.9) |
| Marijuana | 54 | (52.9) | 27 | (64.3) | 13 | (44.8) | 52 | (50.0) | 146 | (52.7) |
| Crack | 13 | (12.7) | 0 | (−) | 1 | (3.6) | 2 | (1.9) | 16 | (5.8) |
| Cocaine | 11 | (11.1) | 0 | (−) | 3 | (10.3) | 8 | (7.7) | 22 | (8.1) |
| Heroin | 8 | (7.9) | 1 | (2.4) | 1 | (3.6) | 1 | (1.0) | 11 | (4.0) |
| Meth, speed, other uppers | 2 | (2.0) | 1 | (2.4) | 1 | (3.6) | 4 | (3.8) | 8 | (2.9) |
| Ecstasy, LSD, PCP, mushroom, other hallucinogen | 6 | (5.9) | 2 | (4.8) | 3 | (10.3) | 11 | (10.6) | 22 | (7.9) |
| Inhalents | 0 | (−) | 0 | (−) | 2 | (7.1) | 0 | (−) | 2 | (0.7) |
| Nonmedical prescription drugs | 5 | (4.9) | 5 | (11.9) | 3 | (10.3) | 12 | (11.7) | 25 | (9.1) |
| Daily use of drugs other than alcohol or marijuana | ||||||||||
| No | 89 | (86.4) | 40 | (95.2) | 28 | (96.6) | 96 | (92.3) | 253 | (91.0) |
| Yes | 14 | (13.6) | 2 | (4.8) | 1 | (3.4) | 8 | (7.7) | 25 | (9.0) |
| Totala | 103 | 42 | 29 | 104 | 278 | |||||
aIndividual blocks may not sum to totals if there was missing data
The proportion of respondents who report access to services by venue is shown in Fig. 1. In every venue, greater than 60% of respondents indicated access to all of the health care services. The most available service was STI/HIV testing, with over 90% of all respondents reporting access to this service, while roughly three quarters of respondents reported access to mental health services. Approximately one third of participants indicated that they had skipped a health care service due to cost across all venues.
Fig. 1.
Proportion of respondents with access to each service by venue
Bivariate Models
The significant bivariate relationships are shown in Table 4, adjusted for venue. The most consistent negative bivariate predictors were not having reliable transportation and being worried about a place to stay, which were associated with decreased access to all services, and working in the street venue, which was significantly associated with decreased access to all services except STI/HIV testing. Having work was associated with increased access to all services except STI/HIV testing, and Black/African American racial identity was associated with increased access to three of the five services, which were the most consistent positive bivariate predictors.
Table 4.
Bivariate relationships adjusted for venue.
| Family planning | (OR) | HIV/STD testing | (OR) | Drug treatment | (OR) | |
|---|---|---|---|---|---|---|
| Positive predictors | Currently working or has other work | (2.23) | Black/African American | (4.10) | Currently working or has other work | (2.17) |
| Negative predictors | Street venue | (0.50) | Ever used crack | (0.15) | Street venue | (0.45) |
| Cisgender male identity | (0.41) | No reliable transportation | (0.18) | No reliable transportation | (0.25) | |
| Not completed high school | (0.46) | Worried about a place to stay | (0.36) | Exchanged for shelter | (0.54) | |
| Current marijuana users | (0.47) | Worried about a place to stay | (0.37) | |||
| No reliable transportation | (0.23) | |||||
| Worried about a place to stay | (0.30) | |||||
| Exchanged for shelter | (0.42) | |||||
| Clinic/hospital | (OR) | Mental health | (OR) | Skipped health care | (OR) | |
| Positive predictors | Black/African American | (3.56) | Black/African American | (1.52) | Ever used nonprescribed drugs | (2.33) |
| Mixed/Asian/other | (4.60) | Currently working or has other work> | (2.28) | Ever used stimulants | (3.38) | |
| Currently working or has other work | (2.87) | Daily drug use | (3.59) | |||
| No reliable transportation | (3.71) | |||||
| Worried about a place to stay | (5.04) | |||||
| Exchanged for drugs | (3.15) | |||||
| Exchanged for shelter | (4.84) | |||||
| Exchanged for something else | (2.15) | |||||
| Anyone oversees your work | (4.74) | |||||
| Each additional exchange in the past month | (1.02) | |||||
| Negative predictors | Street venue | (0.28) | Street venue | (0.49) | Black/African American | (0.17) |
| Daily drug use | (0.34) | Daily drug use | (0.34) | Latino/Hispanic | (0.06) | |
| Ever used hallucinogens | (0.38) | Some reliable transportation | (0.51) | Mixed/Asian/other | (0.37) | |
| Some reliable transportation | (0.42) | No reliable transportation | (0.14) | Currently working or has other work | (0.36) | |
| No reliable transportation | (0.19) | Worried about a place to stay | (0.21) | |||
| Worried about a place to stay | (0.30) | Exchanged for shelter | (0.38) | |||
| Exchanged for shelter | (0.39) | Exchanged for something else | (0.54) | |||
| Exchanged for drugs | (0.45) | |||||
| Exchanged for something else | (0.42) | |||||
| Each additional exchange in the past month | (0.98) |
Multivariable Models
Once multivariable models were constructed, many of the bivariate relationships were no longer significant.
For family planning services, only transgender female, cisgender male identity, and worry about housing remained significant negative predictors, although working in a high-end strip club approached significance as a negative predictor also (Table 5 (1)). Decreased access to HIV/STI testing was significantly associated only with ever having used crack, although never having reliable transportation approached significance (Table 5 (2)). Worry about housing remained the only a significant predictor of decreased access to drug treatment services, although it is notable that not having reliable transportation was significant until the addition of the unstable housing variables, suggesting that the two may be interrelated (Table 5 (3)). Not having clinic, hospital, or community health center access was significantly associated with limited access to reliable transportation, and although the significance level of this was also attenuated with the addition of the unstable housing variables, the odds ratios remained stable (Table 5 (4)). The significant negative predictors of access to mental health services (Table 5 (5)), included not having access to reliable transportation and worry about housing.
Table 5.
Multivariable models of access to (1) family planning; (2) HIV/STI testing; (3) drug treatment; (4) clinic, hospital, or community health center; and (5) mental health services
| [1] | [2] | [3] | [4] | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| OR | (p) | OR | (p) | OR | (p) | OR | (p) | |||||
| (1) Family planning services | ||||||||||||
| Venue (reference = after-hours/social) | ||||||||||||
| Street | 0.52 | (0.09) | 0.53 | (0.11) | 0.63 | (0.28) | 0.69 | (0.42) | ||||
| Low-end strip club | 1.02 | (0.97) | 1.17 | (0.80) | 0.88 | (0.85) | 0.82 | (0.77) | ||||
| High-end strip club | 0.58 | (0.41) | 0.61 | (0.45) | 0.38 | (0.17) | 0.26 | (0.07) | ||||
| Gender identity (reference = cisgender women) | ||||||||||||
| Transgender women | 0.58 | (0.27) | 0.62 | (0.33) | 0.55 | (0.24) | 0.33 | * | (0.05) | |||
| Cisgender men | 0.34 | * | (0.02) | 0.35 | * | (0.02) | 0.27 | ** | (0.01) | 0.22 | ** | (0.00) |
| Racial identity (reference = white) | ||||||||||||
| Black/African American | 1.62 | (0.30) | 1.76 | (0.24) | 1.59 | (0.35) | 1.20 | (0.74) | ||||
| Latino/a | 0.99 | (0.99) | 0.92 | (0.90) | 0.72 | (0.65) | 0.33 | (0.16) | ||||
| Mixed/Asian/other | 2.01 | (0.25) | 1.94 | (0.28) | 1.87 | (0.32) | 1.33 | (0.68) | ||||
| Education (reference = high school/GED) | ||||||||||||
| Some high school | 0.44 | * | (0.03) | 0.47 | (0.05) | 0.57 | (0.18) | 0.49 | (0.10) | |||
| Some college | 1.01 | (0.99) | 1.07 | (0.88) | 0.92 | (0.84) | 0.77 | (0.59) | ||||
| Bachelors degree or more | 1.04 | (0.95) | 1.06 | (0.93) | 0.95 | (0.94) | 0.72 | (0.67) | ||||
| Current marijuana use | 0.49 | * | (0.03) | 0.47 | * | (0.03) | 0.53 | (0.09) | ||||
| Reliable transportation (reference = always) | ||||||||||||
| Never have reliable transportation | 0.27 | * | (0.02) | 0.52 | (0.30) | |||||||
| Sometimes have reliable transportation | 1.02 | (0.96) | 1.20 | (0.67) | ||||||||
| Currently working or has other work | 2.23 | (0.06) | 1.83 | (0.21) | ||||||||
| Worried about not having a place to stay | 0.41 | * | (0.03) | |||||||||
| Exchange for a place to stay | 0.65 | (0.30) | ||||||||||
| N | 269 | 269 | 268 | 249 | ||||||||
| (2) HIV/STD testing | ||||||||||||
| Venue (reference = after-hours/social) | ||||||||||||
| Street | 0.86 | (0.77) | 1.34 | (0.60) | 1.51 | (0.49) | 1.85 | (0.35) | ||||
| Low-end strip club | 1.45 | (0.67) | 1.75 | (0.53) | 1.60 | (0.61) | 1.64 | (0.60) | ||||
| High-end strip club | 3.92 | (0.22) | 4.53 | (0.19) | 5.11 | (0.20) | 4.92 | (0.22) | ||||
| Gender identity (reference = cisgender women) | ||||||||||||
| Transgender women | 1.97 | (0.36) | 2.33 | (0.27) | 2.41 | (0.27) | 2.15 | (0.36) | ||||
| Cisgender men | 0.99 | (0.99) | 1.09 | (0.89) | 0.89 | (0.86) | 0.97 | (0.97) | ||||
| Racial identity (reference = white) | ||||||||||||
| Black/African American | 3.53 | * | (0.04) | 2.68 | (0.13) | 2.73 | (0.13) | 1.86 | (0.38) | |||
| Latino/a | 1.16 | (0.84) | 1.29 | (0.75) | 1.16 | (0.86) | 0.43 | (0.36) | ||||
| Mixed/Asian/other | 3.16 | (0.18) | 2.75 | (0.25) | 2.95 | (0.23) | 2.92 | (0.36) | ||||
| Ever used crack | 0.18 | ** | (0.00) | 0.20 | ** | (0.00) | 0.19 | ** | (0.01) | |||
| Reliable transportation (reference = always) | ||||||||||||
| Never have reliable transportation | 0.18 | * | (0.01) | 0.25 | (0.07) | |||||||
| Sometimes have reliable transportation | 0.97 | (0.96) | 0.93 | (0.91) | ||||||||
| Worried about not having a place to stay | 0.48 | (0.23) | ||||||||||
| N | 271 | 268 | 268 | 248 | ||||||||
| (3) Drug treatment | ||||||||||||
| Venue (reference = after-hours/social) | ||||||||||||
| Street | 0.44 | * | (0.03) | 0.48 | (0.07) | 0.64 | (0.30) | 0.77 | (0.57) | |||
| Low-end strip club | 0.75 | (0.61) | 0.68 | (0.50) | 0.58 | (0.35) | 0.53 | (0.31) | ||||
| High-end strip club | 0.67 | (0.53) | 0.56 | (0.37) | 0.47 | (0.26) | 0.37 | (0.16) | ||||
| Gender identity (reference = cisgender women) | ||||||||||||
| Transgender women | 1.52 | (0.40) | 1.45 | (0.46) | 1.49 | (0.44) | 1.07 | (0.90) | ||||
| Cisgender men | 0.66 | (0.33) | 0.59 | (0.23) | 0.56 | (0.18) | 0.47 | (0.11) | ||||
| Racial identity (reference = white) | ||||||||||||
| Black/African American | 1.00 | (1.00) | 0.95 | (0.92) | 0.88 | (0.79) | 0.68 | (0.47) | ||||
| Latino/a | 0.54 | (0.35) | 0.48 | (0.28) | 0.46 | (0.25) | 0.29 | (0.11) | ||||
| Mixed/Asian/other | 1.43 | (0.57) | 1.36 | (0.63) | 1.40 | (0.60) | 1.08 | (0.92) | ||||
| Reliable transportation (reference = always) | ||||||||||||
| Never have reliable transportation | 0.22 | ** | (0.00) | 0.27 | * | (0.02) | 0.47 | (0.22) | ||||
| Sometimes have reliable transportation | 0.76 | (0.47) | 0.90 | (0.80) | 1.11 | (0.81) | ||||||
| Currently working or has other work | 2.18 | (0.06) | 2.09 | (0.13) | ||||||||
| Worried about not having a place to stay | 0.40 | * | (0.02) | |||||||||
| Exchange for a place to stay | 1.02 | (0.97) | ||||||||||
| N | 269 | 269 | 268 | 248 | ||||||||
| (4) Clinic/hospital/health center | ||||||||||||
| Venue (reference = after-hours/social) | ||||||||||||
| Street | 0.24 | ** | (0.00) | 0.40 | (0.15) | 0.46 | (0.28) | 0.61 | (0.51) | |||
| Low-end strip club | 0.55 | (0.41) | 0.55 | (0.51) | 0.42 | (0.37) | 0.39 | (0.36) | ||||
| High-end strip club | 1.51 | (0.64) | 1.00 | (.) | 1.00 | (.) | 1.00 | (.) | ||||
| Gender identity (reference = cisgender women) | ||||||||||||
| Transgender women | 1.06 | (0.91) | 0.37 | (0.21) | 0.38 | (0.22) | 0.21 | (0.09) | ||||
| Cisgender men | 0.92 | (0.87) | 0.45 | (0.28) | 0.42 | (0.24) | 0.21 | (0.07) | ||||
| Racial identity (reference = white) | ||||||||||||
| Black/African American | 3.60 | * | (0.01) | 1.36 | (0.74) | 1.78 | (0.55) | 1.67 | (0.63) | |||
| Latino/a | 2.02 | (0.34) | 2.81 | (0.42) | 2.71 | (0.44) | 1.00 | (.) | ||||
| Mixed/Asian/other | 4.62 | * | (0.04) | 3.54 | (0.27) | 4.94 | (0.18) | 6.73 | (0.18) | |||
| Daily use of drugs other than marijuana and alcohol | 0.57 | (0.51) | 0.94 | (0.94) | 0.46 | (0.45) | ||||||
| Ever used hallucinogens | 0.63 | (0.43) | 0.69 | (0.55) | 0.58 | (0.38) | ||||||
| Exchange for drugs | 0.66 | (0.51) | 0.61 | (0.45) | 0.75 | (0.69) | ||||||
| Exchange for a place to stay | 0.42 | (0.16) | 0.58 | (0.39) | 0.61 | (0.48) | ||||||
| Exchange for something else (not listed) | 1.17 | (0.80) | 1.15 | (0.82) | 1.24 | (0.74) | ||||||
| Each additional exchange in the past month | 0.99 | (0.18) | 0.99 | (0.10) | 0.98 | (0.07) | ||||||
| Reliable transportation (reference = always) | ||||||||||||
| Never have reliable transportation | 0.14 | * | (0.02) | 0.20 | (0.09) | |||||||
| Sometimes have reliable transportation | 0.39 | (0.14) | 0.36 | (0.14) | ||||||||
| Worried about not having a place to stay | 1.67 | (0.43) | ||||||||||
| N | 271 | 158 | 158 | 135 | ||||||||
| (5) Mental health services | ||||||||||||
| Venue (reference = after-hours/social) | ||||||||||||
| Street | 0.47 | * | (0.02) | 0.59 | (0.12) | 0.74 | (0.43) | 0.82 | (0.62) | |||
| Low-end strip club | 0.92 | (0.87) | 0.93 | (0.88) | 0.82 | (0.70) | 0.76 | (0.63) | ||||
| High-end strip club | 0.87 | (0.79) | 0.66 | (0.44) | 0.56 | (0.30) | 0.48 | (0.23) | ||||
| Gender identity (reference = cisgender women) | ||||||||||||
| Transgender women | 1.36 | (0.45) | 1.16 | (0.73) | 1.31 | (0.54) | 1.02 | (0.97) | ||||
| Cisgender men | 0.87 | (0.71) | 0.85 | (0.68) | 0.78 | (0.54) | 0.68 | (0.37) | ||||
| Racial identity (reference = white) | ||||||||||||
| Black/African American | 1.47 | (0.33) | 1.06 | (0.89) | 1.24 | (0.62) | 0.98 | (0.97) | ||||
| Latino/a | 1.16 | (0.80) | 0.99 | (0.98) | 1.03 | (0.97) | 0.90 | (0.89) | ||||
| Mixed/Asian/other | 1.45 | (0.45) | 1.14 | (0.81) | 1.34 | (0.60) | 0.87 | (0.82) | ||||
| Daily use of drugs other than marijuana or alcohol | 0.48 | (0.15) | 0.75 | (0.60) | 0.72 | (0.58) | ||||||
| Exchange for a place to stay | 0.49 | * | (0.03) | 0.59 | (0.14) | 0.81 | (0.59) | |||||
| Exchange for something else | 0.74 | (0.32) | 0.75 | (0.37) | 0.77 | (0.43) | ||||||
| Reliable transportation (reference = always) | ||||||||||||
| Never have reliable transportation | 0.18 | ** | (0.00) | 0.27 | * | (0.03) | ||||||
| Sometimes have reliable transportation | 0.57 | (0.10) | 0.65 | (0.24) | ||||||||
| Currently working or has other work | 1.48 | (0.31) | 1.40 | (0.43) | ||||||||
| Worried about not having a place to stay | 0.32 | *** | (0.00) | |||||||||
| N | 271 | 271 | 270 | 250 | ||||||||
Exponentiated coefficients; p values in parentheses
*p < 0.05; **p < 0.01; ***p < 0.001
The results of the multivariable models of lack of health care due to cost are shown in Table 6. Although racial and ethnic identity were initially significant, they were attenuated to nonsignificance once worry about housing was added into the model. The only significant predictor that remained was exchanging sex for shelter. Worrying about a place to stay was marginally significant.
Table 6.
Multivariable model of lack of health care due to cost
| Skipped health care due to cost | [1] | [2] | [3] | [4] | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| OR | (p) | OR | (p) | OR | (p) | OR | (p) | |||||
| Venue (reference = after-hours/social) | ||||||||||||
| Street | 1.27 | (0.45) | 0.26 | (0.08) | 0.23 | (0.10) | 0.30 | (0.21) | ||||
| Low-end strip club | 1.54 | (0.34) | 0.22 | (0.10) | 0.26 | (0.18) | 0.14 | (0.09) | ||||
| High-end strip club | 0.75 | (0.59) | 0.37 | (0.36) | 0.67 | (0.73) | 0.60 | (0.68) | ||||
| Gender identity (reference = cisgender women) | ||||||||||||
| Transgender women | 1.31 | (0.51) | 0.99 | (0.99) | 1.18 | (0.85) | 0.84 | (0.85) | ||||
| Cisgender men | 1.24 | (0.57) | 0.62 | (0.55) | 0.79 | (0.77) | 0.30 | (0.23) | ||||
| Racial identity (reference = white) | ||||||||||||
| Black/African American | 0.15 | *** | (0.00) | 1.09 | (0.93) | 1.00 | (1.00) | 1.68 | (0.65) | |||
| Latino/a | 0.06 | *** | (0.00) | 0.02 | ** | (0.01) | 0.03 | * | (0.02) | 1.00 | (.) | |
| Mixed/Asian/other | 0.34 | * | (0.02) | 1.49 | (0.71) | 1.85 | (0.62) | 3.43 | (0.37) | |||
| Daily drug use other than marijuana or alcohol | 4.42 | (0.16) | 2.61 | (0.43) | 4.37 | (0.33) | ||||||
| Ever used prescription drugs without a prescription | 3.08 | (0.13) | 4.33 | (0.09) | 4.38 | (0.13) | ||||||
| Ever used methamphetamines | 1.05 | (0.96) | 0.91 | (0.92) | 1.22 | (0.87) | ||||||
| Exchange for drugs | 1.48 | (0.50) | 1.49 | (0.52) | 1.48 | (0.56) | ||||||
| Exchange for a place to stay | 5.34 | ** | (0.01) | 5.01 | * | (0.02) | 5.16 | * | (0.04) | |||
| Exchange for something else | 1.54 | (0.42) | 1.36 | (0.60) | 1.26 | (0.72) | ||||||
| Work under a “manager” | 4.04 | (0.08) | 5.29 | * | (0.05) | 3.94 | (0.13) | |||||
| Each additional exchange in the past month | 1.00 | (0.75) | 1.00 | (0.73) | 1.00 | (0.95) | ||||||
| Reliable transportation (reference = always) | ||||||||||||
| Never have reliable transportation | 6.20 | (0.11) | 3.55 | (0.31) | ||||||||
| Sometimes have reliable transportation | 0.96 | (0.95) | 0.55 | (0.44) | ||||||||
| Currently working or has other work | 0.40 | (0.24) | 0.64 | (0.63) | ||||||||
| Worried about not having a place to stay | 3.22 | (0.06) | ||||||||||
| N | 270 | 123 | 122 | 108 | ||||||||
Exponentiated coefficients; p values in parentheses
*p < 0.05; **p < 0.01; ***p < 0.001
The venue-specific QCA solutions for the crisp sets of respondents with complete health care access and with incomplete health care access are shown in Table 7. There was only one solution set for complete health care, which included respondents from the low-end strip club venue. These respondents were Black/African American cisgender women who had completed high school, had access to reliable transportation, and also reported having current work or other work. They did not have concerns about housing, were not responsible to a manager for their sex exchange, and were not daily drug users. The solution sets for incomplete health care access included only respondents from the street venue who were exchanging for shelter, worried about having a place to stay, without access to reliable transportation, and without current work or other work. The first were Black/African American transgender women and cisgender men who had not completed high school but were not daily drug users. The second solution set were white, non-Latina cisgender women who had completed high school or a GED, but were daily drug users. None of the solution sets included respondents who were working under a manager.
Table 7.
Venue-specific QCA solutions for crisp sets of respondents with complete and incomplete health care access
| Venue | Present | Absent | Number | Consistency | Coverage |
|---|---|---|---|---|---|
| Solution sets for complete access | |||||
| Street | No solution sets | – | – | – | |
| Low-end strip clubs | Black | 5 | 0.88 | 0.1 | |
| Women | Exchanging for shelter | ||||
| Completed high school | Working for a manager | ||||
| Have transportation | Daily drug use | ||||
| Currently working or have other work | Worried about a place to stay | ||||
| High-end strip clubs | No solution sets | – | – | – | |
| After-hours | No solution sets | – | – | – | |
| Solution sets for incomplete access | |||||
| Street | High school education | 3 | 1 | 0.068 | |
| Black | Working with a manager | ||||
| Transwomen | Daily drug users | ||||
| Exchanging for shelter | Access to reliable transit | ||||
| Worried about a place to stay | Currently working or has other work | ||||
| Street | High school education | 3 | 1 | 0.05 | |
| Black | Working with a manager | ||||
| Cisgender men | Daily drug users | ||||
| Exchanging for shelter | Access to reliable transit | ||||
| Worried about a place to stay | Currently working or has other work | ||||
| Street | White | 3 | 1 | 0.04 | |
| Cisgender women | |||||
| High school education | Working with a manager | ||||
| Exchanging for shelter | Access to reliable | ||||
| Daily drug use | transportation | ||||
| Worried about a place to stay | Currently working or has other work | ||||
| Low-end strip clubs | No solution sets | – | – | – | |
| High-end strip clubs | No solution sets | – | – | – | |
| After-hours | No solution sets | – | – | – | |
Discussion
This study highlights the complexity of access to health care among diverse young people exchanging sex in Detroit. The results emphasize the interactions between intersecting identities and the intertwined epidemics connected to sex exchange, drug use, and poverty, which each emerge as determinants of access to health care under varying conditions of health and social disparity.
The stress of unstable housing, particularly for street-based sex workers, has been identified in many studies of sex workers across urban areas as a contributor to poor access to health care services [2]. Both the regression and QCA results echo this finding, demonstrating the importance of meeting basic needs for the health of vulnerable populations. The need for stable housing likely both competes for time and other resources that might go toward efforts to obtain needed health care services and also physically locates an individual in spaces that are underserved, such as single-room occupancy hotels, streets and parks, and communal living spaces such as brothels or drug houses [3, 26].
The findings of this study are also consistent with others which have identified transportation and geographical drivers of health care access as important determinants of access to health care [4, 13, 27]. In many urban areas, and in Detroit in particular, forces such as segregation, challenges in coordination of services across urban and suburban areas, and the influence of the auto industry have often aligned leaving poor individuals in the urban core without options for reliable transit to the most vital services [28]. A lack of reliable transportation may leave an individual choosing between going without health care, or spending time waiting on an unpredictable bus schedule, using financial resources paying for a taxi, or exhausting limited social capital asking friends and family for rides.
Drug use did not initially appear significantly associated with access to health care, apart from crack cocaine use decreasing access to HIV/STI testing; however, in the qualitative comparative analysis, daily use of drugs precluded complete health care access. One of the subgroups with poor access to health care was also defined in part by daily use of drugs other than marijuana and alcohol. This may reflect a multidimensional relationship within this subset of respondents, with drug use limiting the ability to identify, access, and pay for health care resources, and with some organizations precluding access to or not providing appropriately sensitive services for individuals who are currently using [7, 27].
Gender identity was a notable determinant of access to family planning services. Cisgender men and transgender women were markedly less likely than cisgender women to report having access. Although many individuals perceive that family planning clinics exclusively provide birth control and abortion services, and conclude that they are intended for use by individuals with internal female reproductive organs, these spaces could serve as potential sources of birth control, reproductive health counseling, health screening, and primary care services for cisgender men and transgender women. It may be difficult for potential clients to distinguish from the outside a clinic whose mission includes a broad range of reproductive health-focused services from one whose mission does not.
The study described here is not without limitations. The data set is a small, nonrandom sample from a highly specific population of street-, strip club-, and after-hours party-based sex workers. Although an increasing amount of sexual exchange is negotiated and takes place online, a venue not included in our study, a significant number of sex workers continue to meet clients on the street and in brick-and-mortar businesses [3, 29]. Because the group was so diverse, and the sample size within each venue was relatively small, analyses of subgroups within the data set is also limited. In an effort to minimize the effects of these sampling limitations, all analyses were stratified by venue, and two complementary analysis strategies were undertaken (regression and qualitative comparative analysis). Thus, results from this sample may not be directly generalizable to individuals exchanging sex in other cities. Access to this difficult-to-reach and vulnerable urban population in Detroit, however, was also an important strength of this research, identifying needs and potential points of intervention that would not otherwise have been uncovered. The consistency of the results with prior studies and with earlier descriptions of the syndemic related to sex work, drug use, and poverty also hint at a broader generalizability, and this study contributes additional evidence to the growing body of research connecting poor access to housing and transportation with poor access to health care [4, 7, 30].
The data collection also took place in the midst of reforms to health care access driven by the Affordable Care Act. Although exploring the full impact of that legislation in Detroit is beyond the scope of this analysis, changes in government-sponsored programs, and expansions of eligibility for parental insurance likely modified access to care for some of these young respondents. A prior small study of sex workers in Detroit, however, suggested that not knowing how to access health insurance was a substantial barrier to accessing care [31]. Even as expanded access was implemented, few of our respondents, who were not engaged with service organizations in the area, would have been among the early beneficiaries. The effect of these expansions during the period of data collection would likely have been minimal.
The unique population sampled and the multiple methodologies used for data analysis make it an important contribution to our understanding of the health of urban youth, each of whom is situated differently in the syndemic connected to sex work, drug use, and poverty based on their other intersecting identities. The traditional regression analysis highlights the best-fit set of characteristics that predict access to health care, while the comparative qualitative analysis identifies all of the different sets of characteristics that together predict access. Taken together, they allow for identification of subgroups that may be driving overall trends, and for novel interpretation of relationships seen in the data. The collaboration between academic and community partners provided a venue for dissemination of the findings to organizations positioned to take action.
Implications for Policy and Programming
The findings described here provide a variety of insights for coordinated programming and policy intended to improve access to health care services among young people exchanging sex. In this sample, young people who were exchanging for basic needs such as shelter and transportation, young people using drugs, and young people working on the street reported the lowest access to health care services. Where these services are already being provided, there may be a role for integrating health education and connecting young people to services that exist in their communities. Where they are not, policy interventions to incentivize expansion of services to these areas may have a role. Embedding primary care and mental health resources into other community institutions, such as schools, churches, organizations providing harm-reduction services, and other community-based organizations would also help to bridge this gap.
In addition, these results highlight the importance of individuals’ ability to meet basic needs such as housing, transportation, and employment. For example, respondents who reported having a job were more likely to report also having access to services, and even among those who considered themselves unemployed, having access to transportation improved access to health care. This supports the idea that policies and programming that improve the ability of individuals to meet basic needs will also indirectly improve their access to health care services by introducing them to organizations that provide these services and by relieving basic human needs for food, shelter, and safety, and allowing them to focus on higher-order needs such as health care.
These results emphasize a need to focus specifically on access to safe and reliable transportation as a public health intervention. Collaborations between Department of Transportation policy-makers, urban planning professionals, community-based organizations, and public health professionals seem most likely to pave the way toward improved access to health care services. Exploring the role of telemedicine to minimize transportation needs for some types of health services is also warranted for this vulnerable population.
The role of drug use in determining access to care is complex. Programming interventions should prepare community-based organizations and other service providers to engage with young people who use drugs. In addition, policy changes that improve access to drug treatment and support those in recovery to meet their basic needs have the potential to improve access to health care for those most in need.
Respondents in our study were more likely to endorse free or affordable access to HIV and STI testing, as well as family planning services compared with primary care-type services or mental health providers. Although this group may be at higher risk for sexually transmitted infections and unintended pregnancy, prior studies of the health issues affecting sex workers as well as young people in the broader population suggest that there is a great deal of unmet need for mental health care and primary care. Syndemic theory would also suggest that without addressing the broad range of extant health issues, none can be substantially improved. Where primary care and mental health service providers exist, organizations providing sex-related services could benefit the community by referring for categories of care they cannot provide. It is likely, however, that many respondents’ perceptions of access to primary and mental health care were an accurate assessment of the health care landscape around them, and work is needed to increase providers of these services in Detroit and other urban centers.
This study highlights that many young people in Detroit face economic and sexual vulnerability in the context of sex exchange, drug use, and poverty, and are also in need of access to comprehensive health care services and improved knowledge of services that may already be available. Addressing these needs will require a broad and coordinated approach that includes improving access to reliable public transportation, safe and affordable housing, economic opportunity, drug treatment, and increasing the availability of more comprehensive health care services.
Acknowledgements
This work was supported by the Ford Foundation Youth, Sexuality, Health and Rights Initiative. The first author was supported the University of Michigan Medical Scientist Training Program (NIGMS T32GM07863) during portions of this work. Special acknowledgement is due to Allison Brenner for her thoughtful comments on many drafts of this manuscript.
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