Skip to main content
PMC home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2019 Feb 10.
Published in final edited form as: Drug Alcohol Depend. 2015 Mar 6;150:156–163. doi: 10.1016/j.drugalcdep.2015.02.031

Risk factors for high levels of prescription drug misuse and illicit drug use among substance-using young men who have sex with men (YMSM)

Aleksandar Kecojevic a,∗, Carolyn F Wong b,c, Heather L Corliss a, Stephen E Lankenau d
PMCID: PMC6368938  NIHMSID: NIHMS1009569  PMID: 25772436

Abstract

Background:

Limited research has focused on prescription drug misuse among young men who have sex with men (YMSM), or investigated risk factors contributing to misuse. This study aims to investigate the relationship between multiple psychosocial risk factors (i.e., childhood abuse, discrimination, mental health distress) and prescription drug misuse among YMSM who are current substance users.

Methods:

YMSM (N = 191) who reported prescription drug misuse in the past 6 months were recruited in Philadelphia between 2012 and 2013 to complete an anonymous survey assessing demographic information, substance use, and psychosocial factors.

Results:

High levels of childhood physical abuse and perceived stress were associated with higher opioid misuse, while high levels of depression were associated with lower misuse of opioids. Those with higher levels of perceived stress were more likely to report higher tranquilizer misuse, while those with more experiences of social homophobia/racism and higher levels of depression and somatization reported higher stimulant misuse. Regarding demographic correlates, older participants were more likely than younger participants to report higher opioid misuse, while racial minorities were less likely than White participants to report higher misuse of tranquilizers, stimulants, and illicit drug use. Bisexual/heterosexual/other identified participants were more likely than gay identified participants to report higher misuse of all three classes of prescription drugs.

Conclusions:

Associations of risk factors with substance use among YMSM are complex and offer opportunities for additional research. Our findings show that prevention efforts must address substance use among YMSM in sync with psychosocial stressors.

Keywords: Prescription drug misuse, Illicit drug use, YMSM, Childhood abuse, Minority stress, Mental health distress

1. Introduction

Prescription drug misuse (i.e., opioids, tranquilizers, stimulants), defined as use of prescription drugs when not prescribed by a health care provider or taken only for the feeling or effect caused, is a serious public health problem among young adults in the US, with 31.4% of individuals between ages 18 and 29 years reporting misuse at some point in their lifetime (Substance Abuse and Mental Health Services Administration (SAMHSA), 2010). Most of the research on prescription drug misuse among young adults has focused on students, or general populations of adolescents and young adults (Arria et al., 2008; Corliss et al., 2010; Teter et al., 2010). Recently, researchers have made attempts to understand patterns of prescription drug misuse among high-risk young adults such as homeless, injection drug users, club youth, or polydrug users (Daniulaityte et al., 2009; Kelly et al., 2013; Lankenau et al., 2012). While some of these studies included high-risk lesbian, gay, bisexual, and transgender (LGBT) individuals, and while there is emerging literature on prescription drug misuse among men who have sex with men (MSM; Kelly and Parsons, 2011), limited data exist on prescription drug misuse among high-risk (i.e., drug-using) young MSM (YMSM). This represents a significant gap in the public health literature because YMSM who use drugs are at increased risk for negative health outcomes, including substance dependence (Russell et al., 2002), violence and victimization (Wong et al., 2010), and HIV exposure (Centers for Disease Control and Prevention (CDC), 2010). Identifying factors contributing to prescription drug misuse among high-risk YMSM can shed light on complex relationship between substance use and negative health outcomes, making substance-using YMSM a focal point of public health research.

Although little is known about usage patterns, risk factors, or concomitant health concerns of YMSM who misuse prescription drugs, a substantial body of research on substance use among YMSM informs the current study. Past research has shown that exposure to childhood abuse (i.e., sexual, physical, emotional), discrimination, and stress increases risk of drug use and/or developing substance use disorders (Afifi et al., 2012; Dube et al., 2003; Marshal et al., 2008; Rosario et al., 2014). While YMSM likely misuse prescription drugs for many of the same reasons as other people do, these factors may be particularly relevant for prescription drug misuse in this population because of their increased likelihood of being exposed to these situations.

1.1. Childhood abuse

Several notable studies have documented higher rates of childhood abuse among lesbian, gay, bisexual and transgender (LGBT) individuals relative to heterosexuals (Austin et al., 2008; Corliss et al., 2002; Friedman et al., 2011; Kecojevic et al., 2012; Schneeberger et al., 2014). Childhood abuse has potentially serious negative effects on psychosocial and behavioral functioning in adulthood. For example, the YMSM literature documents the association of childhood abuse with adverse adult mental health outcomes including depression, anxiety, and increased stress (Balsam et al., 2010; Huebner et al., 2004; McLaughlin et al., 2012). Moreover, in previous studies of general young adult populations, experiences of sexual victimization (Young et al., 2011) and witnessing violence (McCauley et al., 2013) have been identified as important risk factors for subsequent prescription drug misuse. The results of multiple studies present an emerging picture in which YMSM who report childhood maltreatment are more likely to report high risk behaviors, including substance use and disorders (Brennan et al., 2007; Kalichman et al., 2001). Based on these findings, it is reasonable to suggest that greater childhood abuse experiences would be linked to higher mental health distress, which together would be associated with greater misuse of prescription drugs in a sample of high-risk YMSM.

1.2. Minority status

Minority Stress Theory (Meyer, 2003) posits that mental health disparities among members of stigmatized minority groups such as YMSM may be explained by the chronic stress produced by living in social environments characterized by discrimination directed toward sexual minorities. Indeed, many YMSM report discrimination experiences such as homophobia and racism, social disapproval, and rejection (Bontempo and D’Augelli, 2002; Friedman et al., 2011; Wong et al., 2010). However, YMSM are a very diverse group and some are at even greater risk for victimization and social marginalization. For example, YMSM who are racial/ethnic minorities are subjected to additional stressors, including prejudice and discrimination directed at their race/ethnicity within gay community (Cochran and Mays, 1994; Diaz et al., 2001). Additionally, many YMSM may internalize society’s negative attitudes toward gay people; this phenomenon referred to as internalized homophobia (IH), represents an internal form of stress (Meyer and Dean, 1998). Experiences of discrimination and victimization have been found to be significantly associated with poor mental health (Meyer, 2003; Ross et al., 2008). In addition, past experiences of prejudice, stigma or rejection have been linked with mental health distress (Courtenay-Quirk et al., 2006; Preston et al., 2007). Even though the Minority Stress Theory does not directly stipulate how experiences of adverse childhood experiences, minority stress, mental health distress, and general stress appraisals directly impact substance use, it is reasonable to expect that greater experiences of different types of stressors and higher mental distress would also lead to increases in prescription drug misuse and illicit drug use. Multiple experiences of discrimination and IH are of particular concern among substance-using YMSM, who may be exposed to more difficult life circumstances than the general YMSM population. Researchers have found that multiple minority status contributes to substance use, with higher rates of substance use reported among YMSM experiencing minority-related discrimination (Goldbach et al., 2015; Marshal et al., 2008; McCabe et al., 2010; Ross et al., 2001; Wong et al., 2010). However, less is understood as to how these minority stressors are associated with prescription drug misuse among YMSM.

1.3. Mental health distress and perceived stress

In addition to childhood abuse and minority stress experiences, poorer mental health is closely related to, and is an additional risk factor for substance use among YMSM (Rosario et al., 1996, 2006a). Ample research has documented that sexual minority youth are at elevated risk for depression (Cochran, 2001; Fergusson et al., 2005), anxiety (Fergusson et al., 1999; Lock and Steiner, 1999) and suicidality (Cochran and Mays, 2000) compared to their heterosexual peers. YMSM suffering from mental health distress have been found to self-medicate with alcohol or drugs (Ford and Schroeder, 2009; Sullivan et al., 2006). Furthermore, mental health distress can impact self-esteem, and self-protective behaviors contributing to elevated substance use among YMSM (Salomon et al., 2009; Perdue et al., 2003). YMSM are also likely to face other interrelated life challenges (i.e., housing, work, family) which further can contribute to increased levels of stress and subsequent substance use. Prescription drugs, in particular, may have appeal among YMSM as a means of self-medication or self-treatment in stressful situation, due to their perceived safety and psychopharmacological specificity (Cicero and Inciardi, 2005; McCabe et al., 2009; Quintero, 2009).

1.4. Current study

Our cross-sectional study was conducted with a higher-risk population of YMSM. While prior research suggests a relationship between experiences of childhood abuse, social discrimination, mental health distress and substance use among sexual minority males, studies examining how these factors contribute to different levels of prescription drug misuse among substance-using YMSM are lacking. The current investigation had two primary aims. First, we sought to determine the associations between childhood abuse, experiences of minority stress (e.g., racism, homophobia, social discrimination, internalized homophobia), and current mental distress. We also examined how general stress appraisals are associated with these psychosocial stressors and mental health factors. We hypothesized that YMSM who report high levels of childhood abuse and minority stress would report increased levels of mental health distress and higher appraisal of general stress in their lives. Second, building on previous conceptualizations of multiple stressors influencing behaviors of YMSM (Meyer, 2003; Rosario et al., 2002; Wong et al., 2010) our study examines the influence of these stressors on levels of prescription drug misuse, and on illicit drug use.

2. Methods

2.1. Sample

Participants for this study were recruited in Philadelphia, PA from November, 2012 to July, 2013. Eligible participants were between the ages of 18 and 29 years; misused a prescription drug (i.e., opioid, tranquilizer, stimulant) in the last 6 months; had sex (oral or anal) with a male partner during the past 6 months; were English speaking; and resided in Philadelphia area. “Misuse” was defined as taking prescription drugs “when they were not pre- scribed for you or that you took only for the experience or feeling it caused” (SAMHSA, 2010).

The recruitment strategy used techniques (i.e., targeted, chain-referral sampling) for reaching hidden population in a variety of settings (Biernacki and Waldorf, 1981; Watters and Biernacki, 1989). Sampling was stratified by age (three age ranges: 18–21, 22–25, 26–29) in order to ensure equal representation of different age groups. Major efforts were undertaken to maximize the diversity of the sample. Of 191 participants, 52 (27.2%) were recruited on the streets and in parks across the city; 40 (20.9%) called the study office after seeing advertisements in the community; 50 (26.2%) were identified through LGBT community based organizations (CBO); 16 (8.5%) in gay bars and/or clubs; 14 (7.3%) at university campuses across Philadelphia; 10 (5.2%) were referred by other participants; and 9 (4.7%) responded to advertisements placed in electronic media. A brief screening tool was used to determine eligibility in person or over the phone. Those found to meet the study criteria were given a detailed description of the study, and those who expressed interest provided a verbal informed consent. To protect participant confidentiality no identifying information was collected, and a federal certificate of confidentiality was obtained. Participants were compensated $25 cash for their participation. Additional descriptions of recruitment strategy and sampling methods are reported elsewhere (Kecojevic et al., 2014).

A cross-sectional survey was developed using iSurvey Software (Contact Software Ltd., Wellington, New Zealand) and loaded onto iPads. The instrument was administered during face-to-face interviews by either the first author or a research assistant. Interviews were conducted in a private office at Drexel University, or in natural settings (i.e., fast food restaurants, cafes, parks) and lasted approximately 60 min. Participants were provided with cards containing response options to facilitate standardization on some interview questions. Referral information, such as resources for HIV testing, was offered to participants following the interview. The institutional review board at Drexel University approved the research protocol.

2.2. Measures

2.2.1. Demographics.

Demographic characteristics included age (recoded as age categories), race/ethnicity (recoded dichotomously as 0 = “White/Caucasian” or 1 = “non-White/racial/ethnic minorities”), and sexual orientation (dichotomized into 0 = “gay identified” and 1 = “non-gay identified, i.e., bisexual, heterosexual, other”).

2.2.2. Childhood abuse.

Questions defining emotional, physical, and sexual abuse were adopted from the Childhood Trauma Questionnaire (CTQ) (Bernstein et al., 1994). Fifteen items assessed abuse during childhood. Participants were asked to indicate how often they were abused when growing up (e.g., “When I was growing up … someone tried to make me do sexual things or watch sexual things”). Responses were scored on a Likert-type scale that ranged from 1 = “never” to 5 = “very often”. Cronbach’s alpha for the overall scale was 0.93 (α = 0.90, 0.88, 0.93 for the emotional, physical, and sexual subscales, respectively). In order to examine the presence and impact of threshold levels of abuse, and as recommended by prior reports (Simon et al., 2009), we utilized cut points previously established by Walker et al. (1999) for CTQ subscales with the following scores marking the threshold for the presence of abuse: emotional abuse ≥10, physical abuse ≥8, and sexual abuse ≥8.

2.2.3. Discrimination.

Items that measured lifetime experiences of racism, homophobia, and social racism/homophobia, were adapted from Diaz et al. (2001) and Wong et al. (2014). Higher scores indicate greater experiences of discrimination.

2.2.3.1. Racism.

The composite measure of racism was a four-item Likert-type scale (α = 0.74), ranging from 0 = “never” to 3 = “many times”, describing lifetime experience of verbal harassment, verbal threats, physical attack, and police harassment due to race or ethnicity (e.g., “How often in your lifetime have you been verbally threatened because of your race or ethnicity?”).

2.2.3.2. Homophobia.

The measure of experienced homophobia was a four-item scale (α = 0.77), ranging from 0 = “never” to 3 = “many times”, describing lifetime experience of verbal harassment and threats, physical attack, police harassment because of sexual orientation, how frequently their friends and family had made fun of gay people around them, and if a participant had ever needed to move in order to avoid harassment or attacks due to their sexual orientation (e.g., “How often in your lifetime have you been physically threatened or attacked because of your sexual orientation?”).

2.2.3.3. Social racism/homophobia.

The composite measure of socio-sexual racism was a four-item scale (α = 0.74) that assessed whether respondents felt uncomfortable in gay-identified spaces, or on-line due to their race or ethnicity, whether they were ever rejected for sex, or felt objectified by sexual partners because of their race or ethnicity (e.g., “I have been turned down for sex because of my race or ethnicity”). These experiences were assessed with a four-item Likert-type scale ranging from 0 = “strongly disagree” to 3 = “strongly agree”.

2.2.4. Internalized homophobia (IH).

IH was measured using a short version of Ross and Rosser’s (1996) instrument, rated on a four-item Likert’s scale (α = 0.92) ranging from 0 = “strongly disagree” to 3 = “strongly agree”. This short scale has been previously used in a similar population by Wong et al. (2014). The items assessed the extent to which participants indicated disliking themselves for being sexually attracted to men, wished they were not sexually attracted to men, felt guilty for having sex with men, and felt stress or conflict as a result of having sex with men (e.g., “Sometimes I feel guilty about having sex with men”).

2.2.5. Mental health distress.

The Brief Symptom Inventory, BSI-18 (Derogatis, 2000) was used to assess levels of depression, anxiety and somatization. The BSI is a self-reported measure of psychological distress in the prior week, and it has been previously used in YMSM (Mustanski et al., 2010). Participants were asked to indicate how much something has distressed or bothered them in the past 7 days (e.g., “In the past 7 days, how much were you distressed by feeling lonely?”) on a 5-point scale that ranges from 1 = “not at all” to 5 = “extremely”. Following the BSI-18 scoring instructions, raw scores were converted to T scores using gender specific community norms. Higher scores indicate more depression, anxiety, or distress arising from perceptions of bodily dysfunctions, i.e., somatization. Because the BSI clinical case cutoff likely has a low positive predictive value in this population (Mustanski et al., 2010), we are not reporting on prevalence of psychological distress in our sample. Cronbach’s alpha for the overall scale was 0.93 (α = 0.88, 0.82, 0.84 for the depression, anxiety, and somatization subscales, respectively).

2.2.6. General stress appraisal.

General stress appraisal was assessed using the Perceived Stress Scale (PSS), a 10-item self- report questionnaire with strong reliability and validity (Cohen et al., 1983). Respondents were asked to indicate how often they have felt or thought a certain way in the past month (e.g., “In the last month, how often have you been upset because of something that happened unexpectedly?”) on a 5-point scale ranging from 0 = “never” to 4 = “very often”. Some of the items were scored in reverse. Responses are then summed to indicate the level of perceived stress. The PSS is not a diagnostic instrument and there are no score cut-offs. However, higher scores indicate more perceived stress. Cronbach’s alpha was 0.86.

2.2.7. Drug use outcomes.

The study focused on the misuse of three most commonly misused classes of prescription drugs, by young adults: opioids, tranquilizers, and stimulants (SAMHSA, 2010). Participants were asked to respond to a Yes/No question asking if they misused opioids, tranquilizers, and stimulants in the past 6 months. Participants were read a list of the prescription drugs for each of drug classes, and Yes/No question was asked for each drug on the list. The list included the following types of drugs: opioids such as Vicodin, Loritab, OxyContin, etc.; tranquilizers-benzodiazepines such as Ativan, Xanax, Klonopin, etc., and stimulants such as Adderall, Ritalin, Desoxyn, etc. To assess the severity of current misuse for each drug class, participants were then asked, “Approximately, how many pills (opioids/tranquilizers/stimulants) did you use in the past 6 months, that were not prescribed to you, or that you took only for the experience or feeling it caused?”. Answers were self-reported in continuous fashion. Because the majority of prescription drug misusers have also been found to have high rates of illicit drug use (Lankenau et al., 2012) we also assessed illicit drug use in the past 6 months by asking participants whether they used ecstasy, mushrooms, μ-hydroxybutyric acid (GHB), lysergic acid diethylamide (LSD), heroin, cocaine, crack, PCP/angel dust, crystal methamphetamine, or ketamine. If they answered “Yes” to any, they were coded as 1 = “Yes” to illicit drug use, and if they answered “No” to all of them, they were coded as 0 = “No”. We excluded marijuana from our illicit drug use category since it is readily available and commonly used within the general population of young adults.

2.3. Data analysis

Statistical analyses were conducted using the STATA 13.1 (StataCorp., College Station, TX, 2013). Descriptive analyses examined the distribution of demographics, risk factors, and substance use outcomes. Correlation analyses examined associations of risk factors with each other. To adjust for the effects of all variables of interest, multivariable regression analyses were conducted to evaluate the predictive nature of each risk factor on different classes of prescription drug misuse and on illicit drug use, while controlling for the potentially confounding effects of variables in models. We ran a negative binomial regression to examine predictors of past 6-month quantity of use (a number of pills) separately for opioids, tranquilizers and stimulants. Poisson regression models were used because the outcome variable constitutes a count variable and Poisson models are most appropriate (Coxe et al., 2009). Since initial Poisson analyses indicated significant overdispersion, a negative binomial regression model was calculated using maximum likelihood estimation with robust error estimators to estimate the dispersion parameter (Coxe et al., 2009; Long, 1997). Logistic regression model was used to examine association of risk factors with illicit drug use. All independent continuous variables were mean-centered (Kraemer and Blasey, 2004). Incidence rate ratios (IRR), odds ratios (ORs), and 95% confidence intervals (CIs) were calculated.

3. Results

3.1. Descriptive findings

As summarized in Table 1, the average age was 23.7 (standard deviation, SD = 3.3), with approximately equal number of participants in three age categories. The sample was one-third White, and two-thirds non-White. Over half of the sample self-identified as gay/homosexual. Substantial proportions of participants reported experiences of different forms of childhood abuse above standardized thresholds (emotional 65.4%, physical 54.5%, and sexual 37.8%). While the participants reported lower levels of experiences of discrimination and IH, as indicated by the ranges, there was considerable variability around the mean, as indicated by the SDs. Levels of depression, anxiety, somatization, and perceived stress experienced by participants were relatively high compared to previous reports in general YMSM populations. The most commonly misused prescription drug classes in the past 6 months were tranquilizers (80.6%, range 0–1800 pills) and opioids (78.5%, range 0–2980 pills), while slightly over half of the sample misused stimulants (52.4%, range 0–850 pills). Marijuana was used by 76.5%, and illicit drugs by 56.5% of participants.

Table 1.

Descriptive statistics for demographics, observed indicators of risk factors, prescription drug misuse, and illicit drug use (N = 191).

Variable Demographics Categories  N (%)
Age group 18–21 years 60 (31.4)
22–25 years 66 (34.6)
26–29 years 65 (34.0)
Race White/Caucasian 64 (33.5)
Non-Whites/racial/ethnic minorities: 127 (66.5)
  Black/African American 71 (37.2)
  Multiracial 35 (18.3)
  Other (incl. Hispanic) 17 (8.9)
  Asian/Pacific Islander 2 (1.0)
  Native American 2 (1.0)
Sexual identity Gay/homosexual 109 (57.1)
Bisexual/heterosexual/other 82 (42.9)
Risk factors Mean (SD)
Childhood abuse (CTQ) Emotional 13.1 (6.1)
Above threshold (N; %) 125 (65.5)
Physical (n = 189) 10.0 (5.2)
Above threshold (N; %) 103 (54.5)
Sexual (n = 188) 8.6 (5.2)
Above threshold (N; %) 71 (37.8)
Discrimination Experience of homophobia (n = 189) 7.5 (4.3)
Experience of racism (n = 189) 2.9 (2.9)
Experience of social 2.5 (2.9)
racism/homophobia
Internalized homophobia (n = 183) 5.3 (5.3)
Mental health distress (BSI) Depression 66.8 (12.2)
Anxiety 66.5 (12.5)
Somatization 59.9 (12.3)
General stress appraisal 20.9 (7.5)
Substance use in past 6 months N (%)
Opioids (range of misused pills: 0–2890) 150 (78.5)
Tranquilizers (range of misused pills: 0–1800) 154 (80.6)
Stimulants (range of misused pills: 0–850) 100 (52.4)
Illicit drugs 108 (56.5)
Marijuana 146 (76.5)
Open in a new tab

Notes: CTQ – Childhood Trauma Questionnaire; BSI – Brief Symptoms Inventory 18, values represent T scores based on gender specific community norms.

3.2. Bivariate associations

Table 2 presents associations between experiences of childhood abuse, minority identity related stressors, mental health burden, and perceived stress. Associations among various psychosocial variables were consistently significant and positive. For instance, higher levels of childhood abuse were significantly associated with increased mental health distress.

Table 2.

Associations between childhood abuse, discrimination, internalized homophobia, mental health distress, and general stress appraisal.

1 2 3 4 5 6 7 8 9 10 11
1. Emotional abuse 1.00 0.68*** 0.52*** 0.47*** 0.31*** 0.36*** 0.31*** 0.42*** 0.37*** 0.37*** 0.39***
2. Physical abuse 1.00 0.52*** 0.42*** 0.39*** 0.36*** 0.28*** 0.38*** 0.30*** 0.37*** 0.39***
3. Sexual abuse 1.00 0.47*** 0.31*** 0.31*** 0.17* 0.30*** 0.25*** 0.26*** 0.26***
4. Racism 1.00 0.29*** 0.36*** 0.21** 0.25*** 0.26*** 0.23*** 0.29***
5. Homophobia 1.00 0.45*** 0.15* 0.25*** 0.22*** 0.28*** 0.18*
6. Social homophobia/racism 1.00 0.26*** 0.29*** 0.24*** 0.15* 0.21**
7. Internalized homophobia 1.00 0.39*** 0.33*** 0.21** 0.42***
8. Depression 1.00 0.76*** 0.53*** 0.69*
9. Anxiety 1.00 0.56*** 0.62***
10. Somatization 1.00 0.46***
11. General stress appraisal 1.00
Open in a new tab
*

<0.05.

**

<0.01.

***

<0.001.

3.3. Multivariable associations

In Table 3, we present estimates for the adjusted IRR and OR for outcomes of interest. Among demographic variables, older compared to younger age categories were significantly correlated with higher opioid misuse. Parameter estimates also indicated that those who identified as racial/ethnic minority reported lower misuse of tranquilizers, stimulants, and illicit drug use compared to White YMSM. Finally, parameter estimates indicated that participants who self-identified as bisexual/heterosexual/other report higher misuse of opioids, tranquilizers, and stimulants compared to participants identifying as gay.

Table 3.

Multivariable regression analyses of risk factors for increased prescription drug misuse and illicit drug use.

Rx opioids
(IRR, 95% CI)		 Rx tranquilizers
(IRR, 95% CI)		 Rx stimulants
(IRR, 95% CI)		 Illicit drugs use
(OR, 95% CI)
Demographics
 Agea 1.90 (1.26–2.89)** 1.38 (0.92–2.06) 1.27 (0.83–1.94) 1.42 (0.91–2.21)
 Race – non-White 0.72 (0.34–1.55) 0.46 (0.22–0.99)* 0.35 (0.24–0.94)* 0.38 (0.17–0.85)*
 Sex identity – non-Gay 2.69 (1.21–5.98)** 2.91 (1.33–6.35)** 3.04 (1.16–7.92)* 1.18 (0.53–2.65)
Risk factors
Childhood Abuse
 Emotional abuse 0.97 (0.36–2.62) 0.67 (0.27–1.67) 1.19 (0.45–3.15) 1.43 (0.60–3.42)
 Physical abuse 2.60 (1.07–6.28)* 1.94 (0.86–4.39) 0.68 (0.20–1.69) 0.65 (0.27–1.55)
 Sexual abuse 1.07 (0.49–2.33) 0.99 (0.43–2.24) 0.95 (0.38–2.37) 1.06 (0.41–2.73)
Discrimination
 Racism 0.99 (0.89–1.10) 0.89 (0.79–1.01) 1.01 (0.85–1.22) 1.15 (1.00–1.32)*
 Homophobia 1.06 (0.97–1.15) 1.07 (0.97–1.18) 1.09 (0.98–1.22) 0.98 (0.89–1.07)
 Social homophobia/racism 0.96 (0.83–1.11) 1.11 (0.96–1.28) 1.16 (1.00–1.34)* 1.01 (0.86–1.18)
Internalized homophobia 0.97 (0.92–1.03) 0.95 (0.89–1.01) 0.95 (0.87–1.04) 1.03 (0.95–1.11)
Mental health distress
 Depression 0.96 (0.92–1.00)* 1.00 (0.96–1.05) 1.04 (1.00–1.09)* 1.03 (0.98–1.08)
 Anxiety 1.02 (0.99–1.06) 1.00 (0.98–1.03) 0.96 (0.91–1.01) 0.97 (0.92–1.01)
 Somatization 1.01 (0.98–1.04) 1.02 (0.99–1.05) 1.08 (1.03–1.12)*** 1.05 (1.01–1.09)*
General stress appraisal 1.09 (1.02–1.17)** 1.07 (1.00–1.14)* 0.95 (0.88–1.02) 1.00 (0.94–1.07)
Open in a new tab

Bolded values denote statistical significance.

a

Age represents the increase in likelihood of the outcome for each age category.

*

≤0.05.

**

≤0.01.

***

≤0.001.

Parameter estimates indicated that childhood physical abuse and higher levels of perceived stress remained significant correlates of higher opioid misuse. Those reporting higher levels of depression reported lower quantities of opioid misuse. Regarding tranquilizer misuse, participants with higher levels of perceived stress were more likely to report higher misuse. In addition, greater experiences of social homophobia/racism, depression, and in particular, somatization were significantly associated with higher misuse of stimulants. Illicit drug use remained more likely among those who were exposed to higher levels of racism and somatization.

4. Discussion

Despite the fact that high levels of substance use is significantly associated with numerous negative consequences, little is known about the psychosocial and mental health risk factors associated with prescription drug and illicit drug misuse among drug-using YMSM, who represent an under-researched population in need of services and interventions. The current study examines the role of experiences of childhood abuse, lifetime discrimination related to racial and sexual orientation status, and current mental health distress on levels of misuse of three classes of prescription drugs in the past 6 months. While many of our participants misused prescription drugs infrequently, the range of use varied considerably, with some misusing prescription drugs (in particular opioids and tranquilizers) rather heavily. Consistent with previous research (Fields et al., 2008; Meyer, 2003; Wong et al., 2010), experiences of childhood abuse and discrimination were significantly correlated with each other and participants’ levels of mental health distress and general stress appraisal. In the present study, we found significantly elevated rates of all three forms of childhood abuse. Previous research suggested that high rates of childhood abuse among YMSM could be attributed to insufficient family and peer support during sexual identity development (Relf, 2001), and related to family’s discontent with gender nonconformity (Harry, 1989). In addition, previous studies of YMSM linked childhood abuse to depression, suicide attempts, gay-related victimization, and high-risk sexual activity (Bontempo and D’Augelli, 2002; Rosario et al., 2006b).

Results indicate that participants who experienced childhood abuse reported higher prevalence of substance use compared to those who have not been victimized which is consistent with previous findings (Friedman et al., 2011). Notably, our results indicate that those who experienced physical childhood abuse are more likely to indicate heavy misuse of opioids, providing additional evidence that some forms of childhood abuse can be powerful antecedents of higher risk behaviors. Opioid misuse can serve as a means to cope with, or dissociate from the pain, anxiety, and negative feelings that may accompany past experiences of childhood physical abuse (Bensley et al., 1999; Dube et al., 2003). Our findings are also consistent with prior research showing that experiences of discrimination can lead to discomfort with one’s sexual identity and may act as a significant psychosocial stressor linked to substance use disorders (McCabe et al., 2010; Mizuno et al., 2012). Study findings indicate that greater experiences of social discrimination were related to higher stimulant use, while greater experiences of racism were related to illicit drug use. These findings confirm some previous studies that found associations between gay-related stressful experiences and increased use of some types of drugs (Rosario et al., 1996), in particular stimulants (Colfax et al., 2005). The experience of discrimination and stigma from heterosexual and within gay communities may lead to elevated stimulant misuse, which may be conceived of as an avoidant coping strategy among YMSM. Similarly, race/ethnicity-related discrimination may increase the odds of substance use, particularly among YMSM who endorse substance use as coping mechanism (Gerrard et al., 2012). Our results point to the need for more research on the role that the different aspects of minority stress may play in substance use among high-risk YMSM.

Consistent with our hypothesis, mental health stressors also played a significant role in levels of prescription drug misuse among high-risk YMSM. Higher levels of perceived stress were significantly associated with increased levels of opioid and tranquilizer misuse. Individuals may use opioids and tranquilizers to self-medicate physical or mental pain (Sullivan et al., 2006), or to escape from a current stressful state since opioids provide general calming and normalizing effects (Khantzian, 1997). Higher level of distress arising from perceptions of bodily dysfunctions, i.e. somatization, was significantly associated with increased stimulant misuse, and with illicit drug use. However, it should be noted that some of the problematic bodily processes assessed by the somatization scale can also be side effects of stimulant use, and thus, could be consequences rather than predictors of stimulant misuse. In addition, our results suggest that there are differentiated effects of depression, with increased levels of depression being associated with higher stimulant misuse but with less opioid misuse. While this finding necessitates further investigation, this may not be as surprising given different pharmacological properties of stimulants and opioids. Stimulants are known to be used to enhance cognitive performance and feelings of well-being (Lakhan and Kirchgessner, 2012), while opioids are known to increase the risk for development of depression (Scherrer et al., 2014). Therefore, it is possible that YMSM may use drugs strategically, to produce, enhance or avoid specific effects of these drugs.

Some important demographic associations deserve mention. In multivariable models, older YMSM were more likely to engage in higher opioid misuse, while no age difference was observed for other drugs. Possible explanations include that older YMSM have an easier access to settings where opioids are available. Relative to gay/homosexual YMSM, those who identified as bisexual/heterosexual/other were at considerable risk for high misuse of all three classes of prescription drugs. For these youth, having sex with men may be at odds with their sexual identities (Gwadz et al., 2004). Hence, high prescription drug misuse may represent a maladaptive coping mechanism for managing negative feelings about sexual identity. Racial minority YMSM were significantly less likely than White YMSM to misuse higher quantities of tranquilizers and stimulants or to use illicit drugs confirming some of the previous findings that racial minority YMSM have lower prevalence of drug use than White YMSM (Bavarian et al., 2013; Kipke et al., 2007).

Our findings have implications for future research and interventions aimed at reducing health risks in substance-using YMSM. For researchers, prescription drug misuse is another important factor to consider when investigating substance use in this population. Additional research with other YMSM communities and longitudinal studies are needed to investigate causal paths and stability of these findings over the time. It is likely that there are other risk factors to be considered beyond those investigated here. For example, substance use behaviors have been found to be associated with impulsivity (Semple et al., 2000), and sensation seeking (Newcomb et al., 2011). In addition, qualitative studies involving YMSM may offer more contextual insights into phenomena of prescription drug misuse. For practitioners, our findings emphasize the importance of recognizing the patterns of interrelated risk factors that contribute to substance use among YMSM. For example, our data suggest a relatively high incidence of childhood abuse in substance-using YMSM, and suggest that screening for childhood abuse is clinically prudent in treatment with these individuals. Besides promoting personal skills such as behavioral self-management, inclusion of strategies for coping with negative life experiences merits a strong consideration in the development and delivery of risk reduction interventions for substance-using YMSM. Gaining a better understanding of the nuances of relationships among abuse, discrimination, distress and prescription drug misuse could enable clinicians and public health researchers to provide more refined harm reduction efforts in this population.

We acknowledge several study limitations. Although self-reported data on risky behaviors and substance use are generally considered valid (Ford, 2008), limitations include recall bias, and social desirability. Despite the use of validated assessments, our findings are limited by the retrospective and subjective nature of self-report measures used to assess predictors and outcome variables. Use of computer assisted interview technology may have minimized the bias of socially desirable responding. While findings from this study provide some insight into risk factors for prescription drug misuse, the cross-sectional analysis does not allow for inference about causality. These results may not generalize to YMSM who do not engage in substance use behaviors, or are not from Philadelphia. This analysis does not adjust for other potential covariates possibly associated with substance use such as peer influence (Stein et al., 2005), or social network characteristics (Kapadia et al., 2013).

In conclusion, our findings corroborate other studies that link contextual stress with substance use among YMSM (Wong et al., 2010). YMSM who are exposed to adverse childhood experiences, discrimination, or who experience higher levels of mental health distress may be at particular risk for prescription drug misuse as a means of avoidance or alleviation of these experiences. The strength of this analysis is that we found differential effects of different risk factors between subtypes of prescription drug misuse. With a host of negative consequences associated with prescription drug misuse and substance use in general, further research is needed to address additional risk factors that are associated with misuse among YMSM. Nevertheless, our findings suggest that clinicians and service providers need to consider YMSM’s broader psychosocial history and current mental health as one of the important ways to intervene with and reduce risk-taking among this high-risk population.

Acknowledgments

Authors gratefully acknowledge Jonathan Newman for his help during recruitment of study participants. We also acknowledge the young men who participated in this study, and all of LGBT community organizations and business in Philadelphia, which allowed recruitment at their premises.

Role of the funding source

This research was supported by funding from the National Institute of Drug Use (NIDA, Grant No. R36DA034543). NIDA had no role in the study design, collection, analysis or interpretation of the data, writing the manuscript, or the decision to submit the paper for publication.

Footnotes

Conflict of interest

No conflicts of interest to be declared.

References

  1. Afifi TO, Henriksen CA, Asmundson GJ, Sareen J, 2012. Childhood maltreatment and substance use disorders among men and women in a nationally representative sample. Can. J. Psychiatry 57, 677–686. [DOI] [PubMed] [Google Scholar]
  2. Arria AM, Calderina KM, O’Grady KE, Vincent KB, Fitzelle DB, Johnson EP, Wish ED, 2008. Drug exposure opportunities and use patterns among college students: results from a longitudinal cohort study. Subst. Abuse 29, 19–38. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Austin SB, Jun H, Jackson B, Spiegelman D, Rich-Edwards J, Corliss HL, Wright RJ, 2008. Disparities in child abuse victimization in lesbian, bisexual, and heterosexual women in the Nurses’ Health Study II. J. Womens Health 4, 597–606. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Balsam KF, Lehavot K, Beadnell B, Circo E, 2010. Childhood abuse and mental health indicators among ethnically diverse lesbian, gay, and bisexual adults. J. Consult. Clin. Psychol 78, 459–468. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Bavarian N, Flay BR, Ketcham PL, Smit E, 2013. Illicit use of prescription stimulants in a college student sample: a theory-guided analysis. Drug Alcohol Depend 132, 665–673. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Bensley LS, Van Eenwyk J, Spieker SJ, Schoder J, 1999. Self-reported abuse history and adolescent problem behaviors: antisocial and suicidal behaviors. J. Adolesc. Health 24, 163–172. [DOI] [PubMed] [Google Scholar]
  7. Bernstein D, Fink L, Handelsman L, Foote J, Lovejoy M, Wenzel K, Sapareto E, Ruggiero J, 1994. Initial reliability and validity of a new retrospective measure of child abuse and neglect. Am. J. Psychiatry 151, 1132–1136. [DOI] [PubMed] [Google Scholar]
  8. Biernacki P, Waldorf D, 1981. Snowball sampling: problems and techniques of chain referral sampling. Soc. Methodol. Res 10, 141–163. [Google Scholar]
  9. Bontempo DE, D’Augelli AR, 2002. Effects of at-school victimization and sexual orientation on lesbian, gay, or bisexual youths’ health risk behavior. J. Adolesc. Health 30, 364–374. [DOI] [PubMed] [Google Scholar]
  10. Brennan DJ, Hellerstedt WL, Ross MW, Welles SL, 2007. History of childhood sexual abuse and HIV risk behaviors in homosexual and bisexual men. Am. J. Public Health 97, 1107–1112. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Centers for Disease Control and Prevention (CDC), 2010. Prevalence and awareness of HIV infection among men who have sex with men – 21 cities, United States, 2008. MMWR 59, 1201–1207. [PubMed] [Google Scholar]
  12. Cicero TJ, Inciardi JA, 2005. Potential for abuse of buprenorphine in office-based treatment of opioid dependence. N. Engl. J. Med 27, 1863–1865. [DOI] [PubMed] [Google Scholar]
  13. Cochran SD, Mays VM, 1994. Depressive distress among homosexually active African-American men and women. Am. J. Psychiatry 151, 524–529. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Cochran SD, Mays VM, 2000. Lifetime prevalence of suicide symptoms and affective disorders among men reporting same-sex sexual partners: results from NHANES III. Am. J. Public Health 90, 573–578. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Cochran SD, 2001. Emerging issues in research on lesbians’ and gay men’s mental health: does sexual orientation really matter? Am. Psychol 56, 931–947. [DOI] [PubMed] [Google Scholar]
  16. Cohen S, Kamarck T, Mermelstein R, 1983. A global measure of perceived stress. J. Health Soc. Behav 24, 385–396. [PubMed] [Google Scholar]
  17. Colfax G, Coates TJ, Husnik MJ, Huang Y, Buchbinder S, Koblin B, Chesney M, Vittinghoff E, EXPLORE Study Team, 2005. Longitudinal patterns of methamphetamine, popper (amyl nitrite), and cocaine use and high-risk sexual behavior among a cohort of San Francisco men who have sex with men. J. Urban Health 82 (Suppl. 1), i62–i70. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Corliss HL, Cochran SD, Mays VM, 2002. Reports of parental maltreatment during childhood in a United States population-based survey of homosexual, bisexual, and heterosexual adults. Child Abuse Negl 26, 1165–1178. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Corliss HL, Rosario M, Wypij D, Wylie SA, Frazier AL, Austin SB, 2010. Sexual orientation and drug use in a longitudinal cohort study of U.S. adolescents. Addict. Behav 35, 517–521. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Courtenay-Quirk C, Wolitski RJ, Parsons JT, Gómez CA, Seropositive Urban Men’s Study Team, 2006. Is HIV/AIDS stigma dividing the gay community? Perceptions of HIV-positive men who have sex with men. AIDS Educ. Prev 18, 56–67. [DOI] [PubMed] [Google Scholar]
  21. Coxe S, West SG, Aiken LS, 2009. The analysis of count data: a gentle introduction to Poisson regression and its alternatives. J. Pers. Assess 91, 121–136. [DOI] [PubMed] [Google Scholar]
  22. Daniulaityte R, Falck RS, Wang J, Carlson RG, 2009. Illicit use of pharmaceutical opioids among young polydrug users in Ohio. Addict. Behav 34, 649–653. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Derogatis LR, 2000. The Brief Symptom Inventory-18 (BSI-18): Administration, Scoring and Procedures Manual National Computer Systems, Minneapolis, MN. [Google Scholar]
  24. Diaz RM, Ayala G, Bein E, Jenne J, Marin BV, 2001. The impact of homophobia, poverty, and racism on the mental health of gay and bisexual Latino men: findings from 3 US cities. Am. J. Public Health 91, 927–932. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Dube SR, Felitti VJ, Dong M, Chapman DP, Giles WH, Anda RF, 2003. Childhood abuse, neglect, and household dysfunction and the risk of illicit drug use: the adverse childhood experiences study. Pediatrics 111, 564–572. [DOI] [PubMed] [Google Scholar]
  26. Fergusson DM, Horwood LJ, Beautrais AL, 1999. Is sexual orientation related to mental health problems and suicidality in young people? Arch. Gen. Psychol 56, 876–880. [DOI] [PubMed] [Google Scholar]
  27. Fergusson DM, Horwood LJ, Ridder EM, Beautrais AL, 2005. Sexual orientation and mental health in a birth cohort of young adults. Psychol. Med 35, 971–981. [DOI] [PubMed] [Google Scholar]
  28. Fields SD, Malebranche D, Feist-Price S, 2008. Childhood sexual abuse in black men who have sex with men: results from three qualitative studies. Cultur. Divers. Ethnic Minor. Psychol 14, 385–390. [DOI] [PubMed] [Google Scholar]
  29. Ford JA, 2008. Nonmedical prescription drug use among college students: a comparison between athletes and nonathletes. J. Am. Coll. Health 57, 211–219. [DOI] [PubMed] [Google Scholar]
  30. Ford JA, Schroeder RD, 2009. Academic strain and non-medical use of prescription stimulants among college students. Deviant Behav 30, 26–53. [Google Scholar]
  31. Friedman MS, Marshal MP, Guadamuz TE, Wei C, Wong CF, Saewyc E, Stall R, 2011. A meta-analysis of disparities in childhood sexual abuse, parental physical abuse, and peer victimization among sexual minority and sexual nonminority individuals. Am. J. Public Health 101, 1481–1494. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Gerrard M, Stock ML, Roberts ME, Gibbons FX, O’Hara RE, Weng CY, Wills TA, 2012. Coping with racial discrimination: the role of substance use. Psychol. Addict. Behav 26, 550–560. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Goldbach JT, Schrager SM, Dunlap SL, Holloway IW, 2015. The application of minority stress theory to marijuana use among sexual minority adolescents. Subst. Use Misuse 50, 366–375. [DOI] [PubMed] [Google Scholar]
  34. Gwadz MV, Clatts MC, Leonard NR, Goldsamt L, 2004. Attachment style, childhood adversity, and behavioral risk among young men who have sex with men. J. Adolesc. Health 34, 402–413. [DOI] [PubMed] [Google Scholar]
  35. Harry J, 1989. Parental physical abuse and sexual orientation in males. Arch. Sex. Behav 18, 251–261. [DOI] [PubMed] [Google Scholar]
  36. Huebner DM, Rebchook GM, Kegles SM, 2004. Experiences of harassment, discrimination, and physical violence among young gay and bisexual men. Am. J. Public Health 94, 1200–1203. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Kalichman SC, Benotsch E, Rompa D, GoreAFelton C, Austin J, Luke W, DiFonzo K, Buckles J, Kyomugisha F, Simpson D, 2001. Unwanted sexual experiences and sexual risks in gay and bisexual men: associations among revictimization, substance use, and psychiatric symptoms. J. Sex Res 38, 1–9. [Google Scholar]
  38. Kapadia F, Siconolfi DE, Barton S, Olivieri B, Lombardo L, Halkitis PN, 2013. Social support network characteristics and sexual risk taking among a racially/ethnically diverse sample of young, urban men who have sex with men. AIDS Behav 17, 1819–1828. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Kecojevic A, Wong CF, Schrager SM, Silva K, Bloom JJ, Iverson E, Lankenau SE, 2012. Initiation into prescription drug misuse: differences between lesbian, gay, bisexual, transgender (LGBT) and heterosexual high-risk young adults in Los Angeles and New York. Addict. Behav 37, 1289–1293. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Kecojevic A, Silva K, Sell R, Lankenau SE, 2014. Prescription drug misuse and sexual risk behaviors among young men who have sex with men (YMSM) in Philadelphia. AIDS Behav [Epub ahead of print]. [DOI] [PMC free article] [PubMed]
  41. Kelly BC, Parsons JT, 2011. Prescription drug misuse and sexual risk taking among HIV-negative MSM. AIDS Behav 17, 926–930. [DOI] [PubMed] [Google Scholar]
  42. Kelly BC, Wells BE, Leclair A, Tracy D, Parsons JT, Golub SA, 2013. Prescription drug misuse among young adults: looking across youth cultures. Drug Alcohol Rev 32, 288–294. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Kipke MD, Weiss G, Wong CF, 2007. Residential status as a risk factor for drug use and HIV risk among young men who have sex with men. AIDS Behav 11, S56–S69. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Khantzian EJ, 1997. The self-medication hypothesis of substance use disorders: a reconsideration and recent applications. Harv. Rev. Psychiatry 4, 231–244. [DOI] [PubMed] [Google Scholar]
  45. Kraemer HC, Blasey CM, 2004. Centring in regression analyses: a strategy to prevent errors in statistical inference. Int. J. Methods Psychol. Res 13, 141–151. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Lakhan SE, Kirchgessner A, 2012. Prescription stimulants in individuals with and without attention deficit hyperactivity disorder: misuse, cognitive impact, and adverse effects. Brain Behav 2, 661–677. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Lankenau S, Schrager S, Jackson J, Silva K, Kecojevic A, Wong C, Iverson E, 2012. Misuse of prescription and illicit drugs among high-risk young adults in Los Angeles and New York. J. Public Health Res 1, 22–30. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Lock J, Steiner H, 1999. Gay, lesbian and bisexual youth risks for emotional, behavioral and social problems: results from a community-based survey. J. Am. Acad. Child Adolesc. Psychol 38, 297–304. [DOI] [PubMed] [Google Scholar]
  49. Long JS, 1997. Regression Models for Categorical and Limited Dependent Variables, vol. 7 Sage, Inc., Thousand Oaks, CA. [Google Scholar]
  50. Marshal MP, Friedman MS, Stall R, King KM, Miles J, Gold MA, Bukstein OG, Morse JQ, 2008. Sexual orientation and adolescent substance use: a meta-analysis and methodological review. Addiction 103, 546–556. [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. McCabe SE, Boyd CJ, Teter CJ, 2009. Subtypes of nonmedical prescription drug misuse. Drug Alcohol Depend 102, 63–70. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. McCabe SE, Bostwick WB, Hughes TL, West BT, Boyd CJ, 2010. The relationship between discrimination and substance use disorders among lesbian, gay, and bisexual adults in the United States. Am. J. Public Health 100, 1946–1952. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. McCauley JL, Kilpatrick DG, Walsh K, Resnick HS, 2013. Substance use among women receiving post-rape medical care, associated post-assault concerns and current substance abuse: results from a national telephone household probability sample. Addict. Behav 38, 1952–1957. [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. McLaughlin KA, Hatzenbuehler ML, Xuan Z, Conron KJ, 2012. Disproportionate exposure to early-life adversity and sexual orientation disparities in psychiatric morbidity. Child Abuse Negl 36, 645–655. [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Meyer IH, 2003. Prejudice, social stress, and mental health in lesbian, gay and bisexual populations: conceptual issues and research evidence. Psychol. Bull 129, 674–697. [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Meyer IH, Dean L, 1998. Internalized homophobia, intimacy, and sexual behavior among gay and bisexual men. In: Herek G. (Ed.), Stigma and Sexual Orientation: Understanding Prejudice Against Lesbians, Gay Men and Bisexuals Sage Publications, Thousand Oaks, CA. [Google Scholar]
  57. Mizuno Y, Borkowf C, Millett GA, Bingham T, Ayala G, Stueve A, 2012. Homophobia and racism experienced by Latino men who have sex with men in the United States: correlates of exposure and associations with HIV risk behaviors. AIDS Behav 16, 724–735. [DOI] [PubMed] [Google Scholar]
  58. Mustanski BS, Garofalo R, Emerson EM, 2010. Mental health disorders, psychological distress, and suicidality in a diverse sample of lesbian, gay, bisexual, and transgender youths. Am. J. Public Health 100, 2426–2432. [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Newcomb ME, Clerkin E, Mustanski B, 2011. Sensation seeking moderates the effects of alcohol and drug use prior to sex and sexual risk in young men who have sex with men. AIDS Behav 15, 565–575. [DOI] [PubMed] [Google Scholar]
  60. Perdue T, Hagan H, Thiede H, Valleroy L, 2003. Depression and HIV risk behavior among Seattle-area injection drug users and young men who have sex with men. AIDS Educ. Prev 15, 81–92. [DOI] [PubMed] [Google Scholar]
  61. Preston DB, D’Augelli AR, Kassab CD, Starks MT, 2007. The relationship of stigma to the sexual risk behavior of rural men who have sex with men. AIDS Educ. Prev 19, 218–230. [DOI] [PubMed] [Google Scholar]
  62. Quintero G, 2009. Rx for a party: a qualitative analysis of recreational pharmaceutical use in a collegiate setting. J. Am. Coll. Health 58, 64–70. [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. Relf MV, 2001. Childhood sexual abuse in men who have sex with men: the current state of the science. J. Assoc. Nurses AIDS Care 12, 20–29. [DOI] [PubMed] [Google Scholar]
  64. Rosario M, Rotheram-Borus MJ, Reid H, 1996. Gay-related stress and its correlates among gay and bisexual male adolescents of predominantly black and hispanic background. J. Community Psychol 24, 136–159. [Google Scholar]
  65. Rosario M, Schrimshaw EW, Hunter J, Gwadz M, 2002. Gay-related stress and emotional distress among gay, lesbian, and bisexual youths: a longitudinal examination. J. Consult. Clin. Psychol 70, 967–975. [DOI] [PubMed] [Google Scholar]
  66. Rosario M, Schrimshaw EW, Hunter J, 2006a. A model of sexual risk behaviors among young gay and bisexual men: longitudinal associations of mental health, substance abuse, sexual abuse, and the coming-out process. AIDS Educ. Prev 18, 444–460. [DOI] [PMC free article] [PubMed] [Google Scholar]
  67. Rosario M, Schrimshaw EW, Hunter J, Braun L, 2006b. Sexual identity development among gay, lesbian, and bisexual youths: consistency and change over time. J. Sex Res 43, 46–58. [DOI] [PMC free article] [PubMed] [Google Scholar]
  68. Rosario M, Reisner SL, Corliss HL, Wypij D, Calzo J, Austin SB, 2014. Sexual-orientation disparities in substance use in emerging adults: a function of stress and attachment paradigms. Psychol. Addict. Behav 28, 790–804. [DOI] [PMC free article] [PubMed] [Google Scholar]
  69. Ross MW, Rosser BRS, 1996. Measurement and correlates of internalized homophobia: a factor in analytic study. J. Clin. Psychol 52, 15–21. [DOI] [PubMed] [Google Scholar]
  70. Ross MW, Rosser BR, Bauer GR, Bockting WO, Robinson BE, Rugg DL, 2001. Drug use, unsafe sexual behavior, and internalized homonegativity in men who have sex with men. AIDS Behav 5, 97–103. [Google Scholar]
  71. Ross MW, Rosser BR, Neumaier ER, Positive Connections Team, 2008. The relationship of internalized homonegativity to unsafe sexual behavior in HIV-seropositive men who have sex with men. AIDS Educ. Prev 20, 547–557. [DOI] [PMC free article] [PubMed] [Google Scholar]
  72. Russell ST, Driscoll AK, Truong N, 2002. Adolescent same-sex romantic attractions and relationships: implications for substance use and abuse. Am. J. Public Health 92, 198–202. [DOI] [PMC free article] [PubMed] [Google Scholar]
  73. Salomon EA, Mimiaga MJ, Husnik MJ, Welles SL, Manseau MW, Montenegro AB, Safren SA, Koblin BA, Chesney MA, Mayer KH, 2009. Depressive symptoms, utilization of mental health care, substance use and sexual risk among young men who have sex with men in EXPLORE: implications for age-specific interventions. AIDS Behav 13, 811–821. [DOI] [PMC free article] [PubMed] [Google Scholar]
  74. Schneeberger AR, Dietl MF, Muenzenmaier KH, Huber CG, Lang UE, 2014. Stressful childhood experiences and health outcomes in sexual minority populations: a systematic review Soc. Psychiatry Psychiatr. Epidemiol 49, 1427–1445. [DOI] [PubMed] [Google Scholar]
  75. Scherrer JF, Svrakic DM, Freedland KE, Chrusciel T, Balasubramanian S, Bucholz KK, Lawler EV, Lustman PJ, 2014. Prescription opioid analgesics increase the risk of depression. J. Gen. Intern. Med 29, 491–499. [DOI] [PMC free article] [PubMed] [Google Scholar]
  76. Semple SJ, Patterson TL, Grant I, 2000. Psychosocial predictors of unprotected anal intercourse in a sample of HIV positive gay men who volunteer for a sexual risk reduction intervention. AIDS Educ. Prev 12, 416–430. [PubMed] [Google Scholar]
  77. Simon NM, Herlands NN, Marks EH, Mancini C, Letamendi A, Li Z, Pollack MH, Van Ameringen M, Stein MB, 2009. Childhood maltreatment linked to greater symptom severity and poorer quality of life and function in social anxiety disorder. Depress. Anxiety 26, 1027–1032. [DOI] [PMC free article] [PubMed] [Google Scholar]
  78. Stein JA, Rotheram-Borus MJ, Swendeman D, Milburn NG, 2005. Predictors of sexual transmission risk behaviors among HIV-positive young men. AIDS Care 17, 433–442. [DOI] [PMC free article] [PubMed] [Google Scholar]
  79. Substance Abuse and Mental Health Services Administration, 2010. Results from the 2009 National Survey on Drug Use and Health: Volume I. Summary of National Findings Office of Applied Studies, NSDUH Series H-38A, HHS Publication No. SMA 10–4586 Findings, Rockville, MD, Available from: http://oas.samhsa.gov/NSDUH/2k9NSDUH/2k9ResultsP.pdf [Google Scholar]
  80. Sullivan MD, Edlund MJ, Zhang L, Unutzer J, Wells KB, 2006. Association between mental health disorders, problem drug use, and regular prescription opioid use. Arch. Intern. Med 166, 2087–2093. [DOI] [PubMed] [Google Scholar]
  81. Teter CJ, Falone AE, Cranford JA, Boyd CJ, McCabe SE, 2010. Nonmedical use of prescription stimulants and depressed mood among college students: frequency and routes of administration. J. Subst. Abuse Treat 38, 292–298. [DOI] [PMC free article] [PubMed] [Google Scholar]
  82. Walker EA, Gelfand A, Katon WJ, Koss MP, Von Korff M, Bernstein D, Russo J, 1999. Adult health status of women with histories of childhood abuse and neglect. Am. J. Med 107, 332–339. [DOI] [PubMed] [Google Scholar]
  83. Watters J, Biernacki P, 1989. Targeted sampling: options for the study of hidden populations. Soc. Probl 36, 416–430. [Google Scholar]
  84. Wong CF, Weiss G, Ayala G, Kipke MD, 2010. Harassment, discrimination, violence, and illicit drug use among young men who have sex with men. AIDS Educ. Prev 22, 286–298. [DOI] [PMC free article] [PubMed] [Google Scholar]
  85. Wong CF, Schrager SM, Holloway IW, Meyer IH, Kipke MD, 2014. Minority stress experiences and psychological well-being: the impact of support from and connection to social networks within the Los Angeles House and Ball Communities. Prev. Sci 15, 44–55. [DOI] [PMC free article] [PubMed] [Google Scholar]
  86. Young A, Grey M, Boyd CJ, McCabe SE, 2011. Adolescent sexual assault and the medical and nonmedical use of prescription medication. J. Addict. Nurs 11, 25–31. [DOI] [PMC free article] [PubMed] [Google Scholar]

RESOURCES