Abstract
Background
Studies of individuals in treatment for substance use have found high rates of psychiatric disorders, however little is known about the mental health of drug users not in treatment. This study aimed to assess the prevalence of lifetime and recent substance use and psychiatric disorders among young injection drug users (IDU) outside of a treatment setting.
Methods
Participants were recruited through outreach and respondent-driven sampling. Trained interviewers administered the Psychiatric Research Instrument for Substance and Mental Disorders. Interviews were conducted at two field stations operated by Community Outreach Intervention Projects in Chicago. Participants were 570 young adults (18-25 years) who injected drugs in the previous 30 days. Heroin was the primary drug used in this sample. Past 12-month and lifetime substance use disorders and primary and substance-induced mental disorders were based on DSM-IV diagnostic criteria.
Results
Nearly all participants met the criteria for heroin dependence. Multiple substance use disorders were common; cannabis was the most common substance involved after heroin, followed by alcohol and cocaine. Major depression, alcohol dependence, antisocial personality disorder, and borderline personality disorder were highly prevalent. Other psychiatric disorders were observed at levels consistent with other young adult samples.
Conclusions
Young IDU experience major depression, alcohol dependence, anti-social personality disorder, and borderline personality disorder at high rates, and multiple substance use disorders are common. Anxiety disorders in this population appear to be similar in prevalence to young adults in general.
Keywords: injection drug use, mental health, comorbidity, respondent-driven sampling
1. Introduction
The co-occurrence of substance use and mental disorders in general is well-documented (Crawford et al., 2003; Grant et al., 2004b; Kessler, 2004; Kessler et al., 2005b; Myrick and Brady, 2003; Regier et al., 1990), and studies of individuals in drug treatment have consistently found high rates of psychiatric comorbidity (Compton et al., 2000; Havassy et al., 2004; Mason et al., 1998; Weaver et al., 2003). In particular, studies of heroin users have documented elevated rates of antisocial personality disorder (Brooner et al., 1997; Craig et al., 1997), major affective disorders (Brooner et al., 1997; Darke and Ross, 1997; Rabkin et al., 1997; Tondo et al., 1999), and anxiety disorders (Bremner et al., 1996; Callaly et al., 2001; Darke et al., 1994; Milby et al., 1996) including post-traumatic stress disorder (PTSD).
In a study of methadone maintenance patients in Baltimore, for example, Brooner et al. (1997) found that 25% of the sample met the criteria for anti-social personality disorder (ASPD), and 16% met the criteria for major depression. These rates were significantly higher than general population age-stratified rates observed in the Epidemiological Catchment Area (ECA) study (Robins et al., 1984). Women were more likely than men to have a lifetime Axis I diagnosis (33% vs. 16%), while men were more likely to have a personality disorder, particularly ASPD (34% vs. 15%). More recently, Chen et al. (2011) reported high rates of psychiatric disorders among opioid-dependent patients recruited from an inpatient substance use treatment facility in Washington, D.C.; 44% having a mood disorder, 36% anxiety disorder, 33% ASPD, and 29% borderline personality disorder. A number of Australian studies have also found high rates of depressive and anxiety disorders, as well as ASPD, among heroin users in methadone maintenance treatment and other treatment modalities (Callaly et al., 2001; Darke et al., 1998; Darke et al., 1994; Mills et al., 2004; Teesson et al., 2005).
Less is known, however, about the prevalence of co-morbid disorders among street populations of heroin users, especially younger (under 30) heroin users. A recent study of heroin users participating in a syringe exchange program in Baltimore showed that psychiatric and substance abuse co-morbidity were highly prevalent (Kidorf et al., 2004). Over 50% of the sample was diagnosed with at least one non-substance use Axis I disorder or ASPD. In a comparison of methadone maintenance treatment program (MMTP) enrollees and needle exchange participants, Brienza and colleagues (2000) found that major depression was more prevalent in the needle exchange group (54%) than in the MMTP group (42%). Both of these studies drew on samples of primarily older (> 30) injection drug users (IDU).
A study of Australian methadone maintenance clients (Darke et al., 1994) suggests that co-morbid disorders may be more prevalent among younger compared to older heroin users. Although the levels of psychopathology were high for the entire sample, younger age was associated with a greater likelihood of a current diagnosis of ASPD. Another study of 210 young heroin users (most entering treatment) in Australia showed even higher levels of psychopathology using structured clinical interviews (Mills et al., 2004). Current major depression was identified in 23% of the sample; 75% had a lifetime diagnosis of ASPD; and 37% had a lifetime diagnosis of PTSD.
Though recent studies of young heroin users are few, the data strongly suggest that, compared to older heroin users, they are more likely to be women (Inciardi et al., 1998), Caucasian (Broz and Ouellet, 2008; Des Jarlais, 1992), and suburban (Thorpe et al., 1998). Changes in the demographics of IDU have implications for the prevalence of comorbid conditions. For example, women heroin users have been found to be two to three times as likely as men to be diagnosed with PTSD (Kidorf et al., 2004; Mills et al., 2004). Brady and colleagues (Brady et al., 1998) found that those with a primary or first diagnosis of PTSD, primarily women, had a higher prevalence of other psychiatric disorders and higher rates and levels of opiate use.
The present study was conducted to assess psychiatric morbidity in a sample of young injection drug users recruited outside of a treatment setting. While we expect to find elevated rates of psychiatric problems compared to the general population of young adults, we are uncertain how this sample might compare with treatment samples of mostly older substance users. Users with psychiatric problems might be more likely to seek drug abuse treatment, in which case those in treatment would show higher levels of psychopathology than those not in treatment. On the other hand, users with multiple problems may be more isolated and less likely to seek help, or may delay seeking help, in which case we might expect to see even higher rates of psychiatric problems among young out-of-treatment heroin users. The findings from this study may help drug treatment providers and criminal justice systems better meet the particular needs of young IDU for dual methods of drug and psychiatric treatments.
2. Methods
Study procedures were approved by the Institutional Review Board of the University of Illinois at Chicago.
2.1. Sample recruitment
The study was conducted at two field sites of the Community Outreach Intervention Projects in West and Northwest Side neighborhoods in Chicago. These sites provide a variety of services including HIV testing and counseling, hepatitis (HBV and HCV) testing, substance abuse treatment referrals, and needle exchange. The neighborhood populations are largely Black and Latino, however young White suburban drug users come to these neighborhoods to buy drugs.
Participants were eligible for the study if they had injected drugs at least once in the past 30 days, and were age 18 to 25. Current injection was verified by trained counselors who inspected for injection stigmata, and age was verified with a driver’s license or state identification card.
Study participants were recruited using outreach and respondent driven sampling (RDS) methods (Heckathorn, 1997; Heckathorn et al., 2002). RDS is predicated on the recognition that those best able to access members of hidden populations are their own peers. RDS motivates peers through a dual incentive system that offers rewards both for being interviewed and for recruiting others. Initial participants were recruited by outreach workers at the two field sites. After completing their interview, these participants were given coupons to recruit other young injection drug users, serving as “seeds” for the RDS chains. Seeds were selected to be heterogeneous with respect to gender, race/ethnicity, socioeconomic status, and sexual orientation. Because recruitment chains tended to be short, and many seeds were not productive, outreach workers also continued to recruit participants directly throughout the study. Forty percent of enrolled participants were recruited by outreach.
Each seed, and each recruited participant that followed, received up to four recruitment coupons after completing the interview. Coupons included a map to the field site from which they were issued, a toll-free phone number for obtaining more information or for arranging appointments, and a unique serial number, with the first digit identifying the site. The coupon numbers were entered into a computer database that established links between seeds and the chains of recruits and recruiters that followed. Participants received compensation for each coupon that was brought in by a person eligible to participate in the study. Compensation began at $15 and was later increased to $20 in an effort to increase recruitment.
To reduce the chance for coercion, the recruit was not required to enroll in the study in order for the recruiter to receive compensation. Fifteen eligible recruits declined to participate. Participants who distributed coupons had to return to the field site to receive compensation, and were paid $10-$15 for the coupon review session, independent of compensation for coupons redeemed. Those who successfully recruited eligible potential participants, and returned to the field site for a coupon review, were given additional coupons. Lost coupons were replaced upon request, and the original coupons voided.
2.2. Procedures
After screening for eligibility, and completing informed consent procedures, participants completed a brief computer-based assessment including questions about the size and composition of their injection drug-using network, and the nature of their relationship with their recruiter. The responses to these questions are used to assess sampling biases. Participants then completed an audio computer-assisted self-interview (ACASI) to assess socio-demographic background, family background, drug use, injection risk behavior, sexual risk behavior, recent mental health and substance use treatment services, HIV and hepatitis testing, HIV/hepatitis knowledge, attitudes regarding and subjective norms for HIV risk behavior, and self-efficacy for sex- and injection-related HIV risk reduction behaviors.
Following the ACASI, a trained interviewer administered the Psychiatric Research Interview for Substance and Mental Disorders (PRISM, version 6). On request, or if no interviewer was immediately available, participants were allowed to make an appointment to return for the PRISM interview. Participants were compensated for completing the interviews. Compensation was initially set at $50 and was later increased to $75 due to the demanding nature of the PRISM interview.
2.2.1. PRISM Instrument
The PRISM is a semi-structured clinical interview that provides diagnoses based on DSM-IV criteria, and is specifically designed to differentiate between the expected effects of intoxication and withdrawal, and between primary (independent) and substance-induced psychiatric disorders (Caton et al., 2005; Hasin et al., 2006; Hasin et al., 1996). The PRISM places sections on substance use at the beginning of the interview to enable the patient’s substance use history to be understood before psychiatric disorders are assessed. An independent disorder is diagnosed when a significant portion of the episode occurred when the expected effects of intoxication or withdrawal could not have occurred, i.e., during a period of sustained abstinence or only occasional use, beginning at least two weeks prior to the onset of heavy use, or continuing at least four weeks after cessation of use. A “substance-induced” disorder occurs during a period of excessive substance use or in the 4 weeks following the cessation of use and requires the full criteria to be met. For example, substance-induced major depressive disorder must have a duration of at least two weeks, and five of the nine depression symptoms, including depressed mood or anhedonia, must be present. Furthermore, the substance consumed must be “relevant” to the disorder (i.e., its effects can cause symptoms identical to the disorder that is being assessed), and each of the component symptoms entering into a diagnosis must be clearly excessive in comparison to the expected effects of intoxication or withdrawal. For example, insomnia during a period of stimulant intoxication could be counted toward the diagnosis of substance-induced depression if it occurred during depressed mood in excess of the level experienced during the non-depressed substance using baseline. Disorders that may be diagnosed as substance-induced include major depression, mania, dysthymia, psychosis, panic disorder, and generalized anxiety disorder.
For both substance use and psychiatric disorders, diagnoses were made using two time frames: “past year” (criteria were met within the past 12 months) and “prior” (criteria were met before the previous 12 months). Lifetime prevalence takes into account both past year and prior diagnoses.
2.2.2. PRISM Retest
As part of the procedures to continuously monitor and improve interviewer performance, a subset of participants (1 in 7) was randomly selected to be retested by a different interviewer. These participants were given an appointment to return for a second interview within two weeks. Participants were compensated $45-$50 for the second PRISM interview.
2.2.3. Statistical Analysis
Data analyses were conducted using Stata 11 (StataCorp, 2009). RDS analyses were conducted using the user-written Stata program -rds- (Schonlau, 2010; Schonlau and Liebau, 2010).
2.2.4. Sample Composition
The sample composition was analyzed to assess productivity of seeds and referral chains; affiliation across gender, race/ethnicity, and urban/suburban residence; and degree (size) of IDU networks.
2.2.5. PRISM Diagnoses
Sample prevalence and RDS estimates of population prevalence were computed for lifetime and past year abuse and dependence (by substance), major depressive disorder, dysthymia, cyclothymia, mania, hypomania, psychotic disorder (including schizophrenia, schizophreniform disorder, schizoaffective disorder, mood disorder with psychotic features, delusional disorder, brief psychotic disorder, psychotic disorder due to a medical condition, and psychotic disorder not otherwise specified), specific phobia, social phobia, panic disorder, agoraphobia, generalized anxiety disorder, obsessive-compulsive disorder, posttraumatic stress disorder (PTSD), antisocial personality disorder, and borderline personality disorder. RDS estimates are adjusted for bias in the chain-referral sampling process (Heckathorn, 2002; Heckathorn, 2007). Nonparametric bootstrap percentile-based confidence intervals were computed for the RDS estimates.
Seventy-five participants returned for the second PRISM interview within 60 days and were included in the retest reliability analysis. Agreement rates and kappa coefficients were computed.
3. Results
A total of 612 eligible participants were enrolled in the study; 1 ACASI file was lost due to a computer malfunction, and 41 participants did not complete a PRISM interview due to scheduling difficulties or failure to return for an appointment (40), or incoherence (1). The present analyses are based on 570 complete interviews. The sample was 62% male, 78% non-Hispanic White and 14% Hispanic; the median age was 23. The majority of participants (72%) lived outside of the city. A sizeable proportion (44%) had some post-secondary education, while 23% had not completed high school; most (68%) were unemployed.
The final sample consisted of 140 unproductive seeds, 97 productive seeds, and 333 recruits; the maximum depth (levels of recruitment) was 13 and the average depth of recruits was 2.6. RDS estimates of population characteristics are shown in Table 1, as well as mean degree (network size), degree adjusted for differential recruitment, and homophily (a measure of the extent to which group members recruited within their own group). Population estimates for gender and race/ethnicity differed only slightly from the sample proportions, however RDS estimates suggest that urban-dwelling young IDU may be underrepresented.
Table 1. Characteristics of the Sample (N=570).
| Sample | Population Estimate |
Avg. Degree | |||||
|---|---|---|---|---|---|---|---|
| N | % | % | SE1 | Raw | Adj | Homophily | |
| Sex | |||||||
| Male | 353 | 62% | 67% | 3.3% | 7.0 | 4.0 | −0.03 |
| Female | 217 | 38% | 33% | 3.3% | 7.5 | 4.3 | 0.04 |
| Race/Ethnicity | |||||||
| Hispanic | 64 | 14% | 12% | 2.4% | 7.0 | 4.3 | 0.14 |
| NH White | 477 | 82% | 83% | 3.4% | 7.3 | 4.2 | 0.18 |
| NH Black/Other | 29 | 4% | 5% | 2.7% | 5.2 | 3.4 | 0.21 |
| Residence | |||||||
| Urban | 160 | 28% | 37% | 5.7% | 6.7 | 3.8 | 0.51 |
| Suburban | 410 | 72% | 63% | 5.7% | 7.4 | 4.3 | 0.57 |
Bootstrap standard error based on 500 replications
The median age of first injection was 19, and the average duration of injection was three years. Heroin was the primary drug injected; 96% of participants had injected heroin by itself, while 13% had injected heroin combined with cocaine (speedball).
3.1. Substance use disorders
Kappa coefficients for lifetime and past year substance use disorders indicated fair to good reliability, with most in the moderate agreement range of 0.40–0.60.1 Lifetime disorders tended to have better reliabilities.
The sample prevalence and RDS estimates of past year substance use disorders are presented in Table 2. RDS estimates correct for bias in the sampling process (Heckathorn, 2002). For some variables RDS estimates could not be computed. This usually occurs when there are zero-count cells in the transition matrix, as happens when the sample prevalence is close to 0 or 1, or when one group is recruited exclusively by members of the same group.
Table 2. Sample prevalence and RDS population estimates of past year substance use disorders.
| Male | Female | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Estimate | Prev. | 95% Conf. Int. | N/Reps | Prev. | 95% Conf. Int. | N/Reps | |||
| Heroin Abuse | Sample Prev | 0.006 | 0.001 | 0.020 | 353 | 0.005 | 0.000 | 0.025 | 217 |
| RDS Estimate | -- | -- | |||||||
| Heroin Dependence | Sample Prev | 0.966 | 0.941 | 0.982 | 353 | 0.986 | 0.960 | 0.997 | 217 |
| RDS Estimate | -- | -- | |||||||
| Alcohol Abuse | Sample Prev | 0.139 | 0.104 | 0.179 | 353 | 0.088 | 0.054 | 0.133 | 217 |
| RDS Estimate | 0.123 | 0.074 | 0.181 | 4997 | 0.099 | 0.023 | 0.196 | 4997 | |
| Alcohol Dependence | Sample Prev | 0.167 | 0.130 | 0.210 | 353 | 0.129 | 0.087 | 0.181 | 217 |
| RDS Estimate | 0.124 | 0.085 | 0.170 | 5000 | 0.110 | 0.054 | 0.186 | 5000 | |
| Cannabis Abuse | Sample Prev | 0.210 | 0.168 | 0.256 | 353 | 0.194 | 0.143 | 0.252 | 217 |
| RDS Estimate | 0.207 | 0.145 | 0.275 | 5000 | 0.259 | 0.137 | 0.396 | 5000 | |
| Cannabis Dependence | Sample Prev | 0.187 | 0.148 | 0.232 | 353 | 0.088 | 0.054 | 0.133 | 217 |
| RDS Estimate | 0.227 | 0.144 | 0.332 | 4973 | 0.070 | 0.026 | 0.144 | 4973 | |
| Cocaine Abuse | Sample Prev | 0.034 | 0.018 | 0.059 | 353 | 0.046 | 0.022 | 0.083 | 217 |
| RDS Estimate | 0.020 | 0.006 | 0.039 | 4314 | 0.022 | 0.005 | 0.073 | 4314 | |
| Cocaine Dependence | Sample Prev | 0.161 | 0.125 | 0.204 | 353 | 0.157 | 0.111 | 0.212 | 217 |
| RDS Estimate | 0.119 | 0.071 | 0.182 | 5000 | 0.117 | 0.066 | 0.182 | 5000 | |
| Stimulant Abuse | Sample Prev | 0.014 | 0.005 | 0.033 | 353 | 0.018 | 0.005 | 0.047 | 217 |
| RDS Estimate | 0.010 | 0.002 | 0.024 | 1735 | 0.011 | 0.003 | 0.034 | 1679 | |
| Stimulant Dependence | Sample Prev | 0.028 | 0.014 | 0.051 | 353 | 0.014 | 0.003 | 0.040 | 217 |
| RDS Estimate | -- | -- | |||||||
| Sedative Abuse | Sample Prev | 0.057 | 0.035 | 0.086 | 353 | 0.101 | 0.065 | 0.149 | 217 |
| RDS Estimate | 0.034 | 0.014 | 0.059 | 4998 | 0.060 | 0.021 | 0.127 | 4998 | |
| Sedative Dependence | Sample Prev | 0.091 | 0.063 | 0.126 | 353 | 0.097 | 0.061 | 0.144 | 217 |
| RDS Estimate | 0.047 | 0.022 | 0.090 | 4996 | 0.053 | 0.019 | 0.100 | 4996 | |
| Opiate Abuse | Sample Prev | 0.028 | 0.014 | 0.051 | 353 | 0.060 | 0.032 | 0.100 | 217 |
| RDS Estimate | 0.023 | 0.007 | 0.043 | 4091 | 0.007 | 0.002 | 0.020 | 4091 | |
| Opiate Dependence | Sample Prev | 0.122 | 0.090 | 0.161 | 353 | 0.152 | 0.107 | 0.207 | 217 |
| RDS Estimate | 0.095 | 0.054 | 0.150 | 5000 | 0.118 | 0.053 | 0.207 | 5000 | |
| Hallucinogen Abuse | Sample Prev | 0.031 | 0.016 | 0.055 | 353 | 0.023 | 0.008 | 0.053 | 217 |
| RDS Estimate | 0.019 | 0.005 | 0.042 | 3034 | 0.027 | 0.003 | 0.070 | 3015 | |
| Hallucinogen Dependence |
Sample Prev | 0.028 | 0.014 | 0.051 | 353 | 0.028 | 0.010 | 0.059 | 217 |
| RDS Estimate | -- | -- | |||||||
Note: Confidence intervals are exact binomial for Sample Prevalence and percentile-based bootstrap for RDS Estimates.
Reps: number of successful bootstrap replications; -- : RDS Estimate could not be computed
As expected, nearly all participants (N = 556, 97.5%) met the criteria for heroin dependence in the past year. Forty-four percent of participants (N = 248) met the criteria for dependence on at least one other substance in the past year, and 35% (N = 200) met the criteria for abuse of at least one other substance (in the absence of dependence); 37% (N = 212) had no other substance use disorder in the past year.
Cannabis use disorders were highly prevalent with an estimated 43% of male IDU experiencing cannabis abuse (20.7%, 95% CI 14.5 – 27.5) or dependence (22.7%, 95% CI 14.4 – 33.2) in the past year, and 33% of female IDU experiencing cannabis abuse (25.9%, 95% CI 13.7 – 39.6) or dependence (7.0%, 95% CI 2.6 – 14.4) in the past year. Lifetime rates of cannabis use disorders were 64% for men (abuse 30.5%, 95% CI 23.3 – 38.7, dependence 33.8%, 95% CI 24.1 – 44.2) and 48% for women (abuse 33.1%, 95% CI 20.8 – 45.7, dependence 15.0%, 95% CI 8.1 – 25.6). Estimates of past year alcohol use disorders were 25% for men (abuse 12.3%, 95% CI 7.4 – 18.1, dependence 12.4%, 95% CI 8.5 – 17.0) and 21% for women (abuse 9.9%, 95% CI 2.3 – 19.6, dependence 11.0%, 95% CI 5.4 – 18.6); lifetime prevalence estimates were 49% for men (abuse 23.6%, 95% CI 16.7 – 31.5, dependence 25.0%, 95% CI 18.1 – 32.8) and 42% for women (abuse 14.7%, 95% CI 6.6 – 24.9, dependence 27.5%, 95% CI 17.7 – 39.2). The prevalence of cocaine use disorders in the past year was estimated at 14% for both men (abuse 2.0%, 95% CI 0.6 – 3.9, dependence 11.9%, 95% CI 7.1 – 18.2) and women (abuse 2.2%, 95% CI 0.5 – 7.3, dependence 11.7%, 95% CI 6.6 – 18.2), and opiate use disorders were experienced by an estimated 12% of male (abuse 2.3%, 95% CI 0.7 – 4.3, dependence 9.5%, 95% CI 5.4 – 15.0) and 13% of female IDU (abuse 0.7%, 95% CI 0.2 – 2.0, dependence 11.8%, 95% CI 5.3 – 20.7). Lifetime cocaine use disorders were estimated at 38% for men (abuse 7.3%, 95% CI 4.1 – 11.0, dependence 30.1%, 95% CI 22.2 – 38.8) and 41% for women (abuse 11.1%, 95% CI 3.4 – 21.2, dependence 29.8%, 95% CI 19.3 – 42.3); lifetime opiate use disorders were estimated at 21% for men (abuse 6.0%, 95% CI 2.9 – 10.2, dependence 14.7%, 95% CI 9.9 – 20.4) and 22% for women (abuse 1.2%, 95% CI 0.3 – 3.2, dependence 20.9%, 95% CI 12.4 – 31.5).
3.2. Psychiatric comorbidity
Kappa coefficients for lifetime and past year psychiatric disorders indicated fair to good reliability, with most in the moderate agreement range of 0.40-0.60. Lifetime disorders tended to have better reliabilities.
Sample prevalence and RDS estimates of lifetime and past year psychiatric disorders are presented in Tables 3 and 4. Where applicable, substance-induced disorders and primary disorders are reported separately. For major depression and dysthymia we also present the rates for primary and substance-induced disorders combined. For bootstrap confidence intervals on the RDS estimates, the number of successful replications is reported; the estimation procedure frequently failed on replications when the prevalence of the disorder was low (<.01).
Table 3. Sample prevalence and RDS population estimates of lifetime psychiatric disorders.
| Male | Female | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Estimate | Prev. | 95% Conf. Int. | N/Reps | Prev. | 95% Conf. Int. | N/Reps | |||
| Affective disorders | |||||||||
| Primary Major Depression | Sample Prev | 0.088 | 0.060 | 0.122 | 353 | 0.212 | 0.160 | 0.272 | 217 |
| RDS Estimate | 0.096 | 0.050 | 0.164 | 4996 | 0.218 | 0.135 | 0.321 | 4996 | |
| Substance-Induced Major Depression | Sample Prev | 0.221 | 0.179 | 0.268 | 353 | 0.346 | 0.283 | 0.413 | 217 |
| RDS Estimate | 0.194 | 0.124 | 0.288 | 5000 | 0.258 | 0.167 | 0.361 | 5000 | |
| Major Depression (any) | Sample Prev | 0.258 | 0.213 | 0.307 | 353 | 0.401 | 0.335 | 0.469 | 217 |
| RDS Estimate | 0.248 | 0.169 | 0.349 | 5000 | 0.309 | 0.212 | 0.421 | 5000 | |
| Primary Dysthymia | Sample Prev | 0.000 | 0.000 | 0.010 | 353 | 0.032 | 0.013 | 0.065 | 217 |
| RDS Estimate | -- | 0.023 | 0.004 | 0.053 | 4889 | ||||
| Substance-Induced Dysthymia | Sample Prev | 0.037 | 0.020 | 0.062 | 353 | 0.028 | 0.010 | 0.059 | 217 |
| RDS Estimate | 0.023 | 0.004 | 0.051 | 4240 | 0.024 | 0.005 | 0.073 | 4240 | |
| Dysthymia (any) | Sample Prev | 0.037 | 0.020 | 0.062 | 353 | 0.060 | 0.032 | 0.100 | 217 |
| RDS Estimate | 0.023 | 0.004 | 0.051 | 4922 | 0.047 | 0.010 | 0.101 | 4922 | |
| Primary Manic Episode | Sample Prev | 0.020 | 0.008 | 0.040 | 353 | 0.018 | 0.005 | 0.047 | 217 |
| RDS Estimate | 0.013 | 0.003 | 0.025 | 3041 | 0.012 | 0.001 | 0.030 | 3014 | |
| Anxiety disorders | |||||||||
| Primary Panic Disorder | Sample Prev | 0.017 | 0.006 | 0.037 | 353 | 0.032 | 0.013 | 0.065 | 217 |
| RDS Estimate | 0.022 | 0.004 | 0.048 | 4673 | 0.066 | 0.011 | 0.135 | 4672 | |
| Substance-Induced Panic Disorder | Sample Prev | 0.000 | 0.000 | 0.010 | 353 | 0.005 | 0.000 | 0.025 | 217 |
| RDS Estimate | -- | -- | |||||||
| Primary Agoraphobia w/o Panic | Sample Prev | 0.000 | 0.000 | 0.010 | 353 | 0.023 | 0.008 | 0.053 | 217 |
| RDS Estimate | -- | 0.011 | 0.004 | 0.028 | 3785 | ||||
| Primary GAD | Sample Prev | 0.008 | 0.002 | 0.025 | 353 | 0.018 | 0.005 | 0.047 | 217 |
| RDS Estimate | 0.011 | 0.002 | 0.243 | 1942 | 0.040 | 0.032 | 0.136 | 1848 | |
| Substance-Induced GAD | Sample Prev | 0.011 | 0.003 | 0.029 | 353 | 0.014 | 0.003 | 0.040 | 217 |
| RDS Estimate | -- | -- | |||||||
| Specific Phobia | Sample Prev | 0.008 | 0.002 | 0.025 | 353 | 0.028 | 0.010 | 0.059 | 217 |
| RDS Estimate | -- | -- | |||||||
| Social Phobia | Sample Prev | 0.034 | 0.018 | 0.059 | 353 | 0.106 | 0.068 | 0.155 | 217 |
| RDS Estimate | 0.034 | 0.010 | 0.064 | 4757 | 0.059 | 0.021 | 0.112 | 4757 | |
| Obsessive-Compulsive Disorder | Sample Prev | 0.023 | 0.010 | 0.044 | 353 | 0.023 | 0.008 | 0.053 | 217 |
| RDS Estimate | 0.010 | 0.002 | 0.025 | 2941 | 0.023 | 0.005 | 0.048 | 2930 | |
| Post-traumatic Stress Disorder | Sample Prev | 0.059 | 0.037 | 0.090 | 353 | 0.166 | 0.119 | 0.222 | 217 |
| RDS Estimate | 0.038 | 0.011 | 0.106 | 4926 | 0.120 | 0.059 | 0.194 | 4926 | |
| Any primary anxiety disorder | Sample Prev | 0.130 | 0.097 | 0.170 | 353 | 0.295 | 0.235 | 0.360 | 217 |
| RDS Estimate | 0.104 | 0.060 | 0.159 | 5000 | 0.262 | 0.166 | 0.370 | 5000 | |
| Psychotic disorders | |||||||||
| Primary Psychotic Disorder | Sample Prev | 0.000 | 0.000 | 0.010 | 353 | 0.018 | 0.005 | 0.047 | 217 |
| RDS Estimate | -- | 0.010 | 0.002 | 0.030 | 2738 | ||||
| Substance-Induced Psychosis | Sample Prev | 0.020 | 0.008 | 0.040 | 353 | 0.037 | 0.016 | 0.071 | 217 |
| RDS Estimate | 0.012 | 0.004 | 0.028 | 2540 | 0.039 | 0.008 | 0.121 | 2536 | |
| Psychotic Disorder (any) | Sample Prev | 0.020 | 0.008 | 0.040 | 353 | 0.055 | 0.029 | 0.095 | 217 |
| RDS Estimate | 0.012 | 0.004 | 0.027 | 2862 | 0.048 | 0.013 | 0.126 | 2858 | |
| Personality disorders | |||||||||
| Childhood Conduct Disorder | Sample Prev | 0.147 | 0.112 | 0.189 | 353 | 0.129 | 0.087 | 0.181 | 217 |
| RDS Estimate | 0.118 | 0.067 | 0.174 | 4959 | 0.091 | 0.029 | 0.178 | 4959 | |
| Antisocial Personality Disorder | Sample Prev | 0.297 | 0.250 | 0.348 | 353 | 0.226 | 0.172 | 0.287 | 217 |
| RDS Estimate | 0.227 | 0.159 | 0.302 | 5000 | 0.172 | 0.084 | 0.285 | 5000 | |
| Borderline Personality Disorder | Sample Prev | 0.190 | 0.150 | 0.235 | 353 | 0.281 | 0.222 | 0.346 | 217 |
| RDS Estimate | 0.195 | 0.124 | 0.276 | 5000 | 0.253 | 0.152 | 0.363 | 5000 | |
Note: Confidence intervals are exact binomial for Sample Prevalence and percentile-based bootstrap for RDS Estimates.
Reps: number of successful bootstrap replications; -- : RDS Estimate could not be computed
Table 4. Sample prevalence and RDS population estimates of past year psychiatric disorders.
| Male | Female | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Estimate | Prev. |
95% Conf. Int. |
N/Reps | Prev. |
95% Conf. Int. |
N/Reps | |||
| Affective disorders | |||||||||
| Primary Major Depression | Sample Prev | 0.025 | 0.012 | 0.048 | 353 | 0.065 | 0.036 | 0.106 | 217 |
| RDS Estimate | 0.024 | 0.006 | 0.048 | 4870 | 0.055 | 0.019 | 0.103 | 4870 | |
| Substance-Induced Major Depression | Sample Prev | 0.184 | 0.145 | 0.229 | 353 | 0.295 | 0.235 | 0.360 | 217 |
| RDS Estimate | 0.158 | 0.098 | 0.240 | 5000 | 0.194 | 0.117 | 0.288 | 5000 | |
| Major Depression (any) | Sample Prev | 0.210 | 0.168 | 0.256 | 353 | 0.346 | 0.283 | 0.413 | 217 |
| RDS Estimate | 0.186 | 0.118 | 0.277 | 5000 | 0.240 | 0.156 | 0.341 | 5000 | |
| Primary Dysthymia | Sample Prev | 0.000 | 0.000 | 0.010 | 353 | 0.014 | 0.003 | 0.040 | 217 |
| RDS Estimate | -- | 0.009 | 0.007 | 0.031 | 3178 | ||||
| Substance-Induced Dysthymia | Sample Prev | 0.034 | 0.018 | 0.059 | 353 | 0.023 | 0.008 | 0.053 | 217 |
| RDS Estimate | 0.023 | 0.004 | 0.050 | 4188 | 0.024 | 0.005 | 0.073 | 4187 | |
| Dysthymia (any) | Sample Prev | 0.034 | 0.018 | 0.059 | 353 | 0.037 | 0.016 | 0.071 | 217 |
| RDS Estimate | 0.024 | 0.004 | 0.051 | 4690 | 0.033 | 0.005 | 0.083 | 4690 | |
| Primary Manic Episode | Sample Prev | 0.011 | 0.003 | 0.029 | 353 | 0.009 | 0.001 | 0.033 | 217 |
| RDS Estimate | 0.006 | 0.002 | 0.014 | 2129 | 0.004 | 0.003 | 0.017 | 1853 | |
| Anxiety disorders | |||||||||
| Primary Panic Disorder | Sample Prev | 0.011 | 0.003 | 0.029 | 353 | 0.023 | 0.008 | 0.053 | 217 |
| RDS Estimate | 0.008 | 0.002 | 0.020 | 4415 | 0.056 | 0.007 | 0.124 | 4408 | |
| Substance-Induced Panic Disorder | Sample Prev | 0.000 | 0.000 | 0.010 | 353 | 0.000 | 0.000 | 0.017 | 217 |
| RDS Estimate | -- | -- | |||||||
| Primary Agoraphobia w/o Panic | Sample Prev | 0.000 | 0.000 | 0.010 | 353 | 0.009 | 0.001 | 0.033 | 217 |
| RDS Estimate | -- | -- | |||||||
| Primary GAD | Sample Prev | 0.003 | 0.000 | 0.016 | 353 | 0.014 | 0.003 | 0.040 | 217 |
| RDS Estimate | -- | -- | |||||||
| Substance-Induced GAD | Sample Prev | 0.008 | 0.002 | 0.025 | 353 | 0.000 | 0.000 | 0.017 | 217 |
| RDS Estimate | -- | -- | |||||||
| Specific Phobia | Sample Prev | 0.008 | 0.002 | 0.025 | 353 | 0.018 | 0.005 | 0.047 | 217 |
| RDS Estimate | -- | -- | |||||||
| Social Phobia | Sample Prev | 0.023 | 0.010 | 0.044 | 353 | 0.074 | 0.043 | 0.117 | 217 |
| RDS Estimate | 0.030 | 0.008 | 0.059 | 3213 | 0.038 | 0.008 | 0.081 | 3213 | |
| Obsessive-Compulsive Disorder | Sample Prev | 0.014 | 0.005 | 0.033 | 353 | 0.018 | 0.005 | 0.047 | 217 |
| RDS Estimate | 0.002 | 0.002 | 0.008 | 1846 | 0.015 | 0.004 | 0.035 | 1807 | |
| Post-traumatic Stress Disorder | Sample Prev | 0.037 | 0.020 | 0.062 | 353 | 0.129 | 0.087 | 0.181 | 217 |
| RDS Estimate | 0.014 | 0.003 | 0.027 | 3111 | 0.106 | 0.051 | 0.171 | 3111 | |
| Any primary anxiety disorder | Sample Prev | 0.091 | 0.063 | 0.126 | 353 | 0.244 | 0.189 | 0.307 | 217 |
| RDS Estimate | 0.067 | 0.035 | 0.114 | 4999 | 0.214 | 0.126 | 0.319 | 4999 | |
| Psychotic disorders | |||||||||
| Primary Psychotic Disorder | Sample Prev | 0.000 | 0.000 | 0.010 | 353 | 0.005 | 0.000 | 0.025 | 217 |
| RDS Estimate | -- | -- | |||||||
| Substance-induced Psychosis | Sample Prev | 0.011 | 0.003 | 0.029 | 353 | 0.018 | 0.005 | 0.047 | 217 |
| RDS Estimate | -- | -- | |||||||
| Psychotic Disorder (any) | Sample Prev | 0.011 | 0.003 | 0.029 | 353 | 0.023 | 0.008 | 0.053 | 217 |
| RDS Estimate | -- | -- | |||||||
| Personality disorders | |||||||||
| Antisocial Personality Disorder | Sample Prev | 0.278 | 0.232 | 0.327 | 353 | 0.217 | 0.164 | 0.277 | 217 |
| RDS Estimate | 0.209 | 0.145 | 0.282 | 5000 | 0.163 | 0.076 | 0.273 | 5000 | |
| Borderline Personality Disorder | Sample Prev | 0.153 | 0.117 | 0.195 | 353 | 0.235 | 0.180 | 0.297 | 217 |
| RDS Estimate | 0.146 | 0.080 | 0.221 | 5000 | 0.194 | 0.106 | 0.293 | 5000 | |
Note: Confidence intervals are exact binomial for Sample Prevalence and percentile-based bootstrap for RDS Estimates.
Reps: number of successful bootstrap replications; -- : RDS Estimate could not be computed
The most prevalent disorder was major depression, with estimated lifetime rates of 25% (95% CI 16.9 – 34.9%) for men and 31% (95% CI 21.2 – 42.1%) for women, including both primary and substance-induced episodes. Most past year episodes of major depression and dysthymia were substance-induced. In contrast, there were no reported instances of substance-induced manic episode. Anxiety disorders generally had low prevalence, however for women the estimated rate of PTSD was 10.6% (95% CI 5.1 – 17.1%), and primary panic disorder was 5.6% (95% CI 0.7 – 12.4%).
Antisocial and borderline personality disorders were also highly prevalent with estimated lifetime rates respectively of 23% (95% CI 15.9 – 30.2%) and 20% (95% CI 12.4 – 27.6%) for men and 17% (95% CI 8.4 – 28.5%) and 25% (95% CI 15.2 – 36.3) for women.
3.3. Psychiatric and substance use treatment
The majority of participants (N = 390, 68%) had received some kind of substance use treatment; the mean age of first treatment was 19 (SE = 0.14). Women were more likely than men to have received abstinence medication (54% vs. 45%; OR = 1.46, 95% CI 1.04 – 2.06). Five percent of participants (N = 28) reported current treatment. Over half (N = 335, 59%) of participants had participated in a twelve-step program, with 10% (N = 56) reporting current participation.
Nearly half (N = 281, 49%) of participants had ever received some kind of psychiatric treatment; the mean age of first treatment was 15 (SE = 0.27). Women were more likely to have received psychiatric treatment than men (OR = 1.88, 95% CI 1.33 – 2.65), and were also more likely be currently receiving treatment (12% vs. 4%; OR = 3.41, 95% CI 1.70 – 6.82). About half (N = 12, 52%) of participants with primary major depression in the past year reported psychiatric treatment in the past year. Less than half (N = 59, 44%) of participants with past year substance-induced major depression had received treatment, and 34% (N = 29) of participants with an anxiety disorder in the past year had received treatment in the past year.
4. Discussion
Consistent with other recent studies of injection drug users in this city (Thorpe, et al., 2001; Broz and Ouellet, 2008), the majority of young IDU were White, suburban, and injected primarily heroin. Multiple substance use disorders were common in this sample, with alcohol, cannabis, and cocaine being the most common secondary substances. Prior other opiate dependence was also not uncommon.
With a few exceptions (e.g., cannabis dependence among men), RDS estimates of substance use disorders were smaller than the sample prevalence rates. RDS estimates are adjusted to account for biases in the sampling process, including bias due to differential recruitment and to differential degree (Heckathorn, 2002). It appears that individuals with multiple substance use disorders tend to have larger networks and so have a greater probability of selection.
Major depression and alcohol dependence were clearly elevated, and dysthymia was slightly elevated in this sample compared to lifetime rates for 18-29 year olds in the National Comorbidity Survey Replication (NCS-R; Kessler et al., 2005a). Lifetime alcohol dependence was diagnosed in 6.3% of 18-29 year olds in the NCS-R, compared to 25% of men and 28% of women in the current study. Lifetime major depression was diagnosed in 15.4% of 18-29 year olds in the NCS-R, compared to 25% of men and 31% of women in the current study, while lifetime dysthymia was diagnosed in 1.7% of 18-29 year olds in the NCS-R, compared to 2.3% of men and 4.7% of women in the current study. In contrast, lifetime rates of anxiety disorders were similar to or lower than those in the NCS-R for 18-29 year-olds.
Among female IDU, RDS estimates of 12-month alcohol abuse (9.9%) and dependence (11.0%) were over 70% greater than rates found in the 2001-2002 National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) for White 18-29 year old women (5.6% for abuse, 6.4% for dependence) (Grant et al., 2004a), however the NESARC rates fall within the 95% confidence intervals for the RDS estimates.
Antisocial and borderline personality disorders were prevalent with rates well above those reported for general population samples. The lifetime prevalence of ASPD among 18-29 year old men and women in NESARC was 6.2% (Grant et al., 2004c), and it was 8.5% among non-college-attending 18-24 year olds (Blanco et al., 2008), while it was 23% among men and 17% among women in the current study. Estimates of the prevalence of borderline personality disorder in the U.S. adult population are 1.4% based on the NCS-R full sample, and 1.6% based on a clinical reappraisal sub-sample (Lenzenweger et al., 2007), much lower than the rates of 19.5% for men and 25% for women in the current study. Although the general population rates are likely to be higher for young people, it is not likely that they exceed ten percent. While adult antisocial behavior (stealing, conning, lying, etc.) may be directly related to efforts to acquire drugs, a diagnosis of ASPD also requires some evidence of conduct disorder (three or more symptoms) prior to age 15.
Psychiatric service use by persons affected by a mental disorder appears to be similar to that in the general population. In the NCS-R, 33% of individuals with major depressive disorder in the past 12 months, and 35% of those with panic disorder or PTSD, reported mental health service use in the past year (Wang et al., 2005).
Comparisons with other studies drawing from substance abuse treatment or needle exchange programs (NEP) are complicated by differences in age, race, and substances used, as well as methodological differences. Compared to a sample of opiate-dependent subjects recruited from needle exchange programs in Baltimore, MD (Kidorf et al., 2004) rates of most Axis I disorders were similar. In contrast, compared to a multi-modal treatment sample of drug dependent subjects (about half with opiate dependence) recruited in St. Louis, MO (Compton et al., 2000) lifetime rates of most comorbid psychiatric diagnoses (alcohol dependence, major depression, dysthymia, antisocial personality disorder, phobic disorders, generalized anxiety disorder) were lower in this sample of young IDU. Rates of recent major depression (19% for men and 24% for women) were lower than those found in a Providence, RI sample of older opiate users recruited from methadone maintenance therapy and needle exchange programs (34%-66%; Brienza et al., 2000), even though the current study used a longer time frame (past year versus past six months). Chen et al. (2011) also reported high rates of current mood disorders (37% of men, 65% of women) and anxiety disorders (28% of men, 58% of women) among opiate-dependent subjects in treatment compared to the current study. These results suggest that mood and anxiety disorders are more prevalent among IDU seeking substance abuse treatment.
Borderline personality disorder was less prevalent in the current study (19.5% males, 25.3% females) than in the Mills et al. (2004) study of young IDU entering treatment (46% of males, 59% of females), and among women in the Chen et al. (2011) inpatient substance use sample (26% and 40% among opioid-dependent men and women, respectively), while a study of young heroin users recruited outside of treatment in Spain (Rodriguez-Llera, 2006) found rates similar to the current study.
The lifetime rates of ASPD among men (23%), and lifetime alcohol dependence among men (25%) and women (28%) were consistently lower than those reported in studies drawing from NEPs or treatment programs. Lifetime alcohol dependence in U.S. studies ranged from 50% (Brooner et al., 1997) to 87% (Dinwiddie, 1997); the higher rates in these samples may be related to their older age. Rates of ASPD among men ranged from 32% (Chen et al., 2011) to 52% (Compton et al., 2000) in U.S. studies. Since ASPD is generally more prevalent among younger men, the higher rates found among older IDU suggests that male IDU with ASPD may be more likely to continue to inject drugs. Alternatively, treatment samples may be biased due to court-mandated treatment. Chen et al. (2011), for example, reported that 58% of the study participants were court-mandated to attend treatment.
4.1. Limitations
We experienced significant difficulties in recruiting the sample; only 41% of individuals selected as “seeds” successfully recruited other participants, only 58% of the sample was recruited by RDS, and most recruitment chains did not achieve significant depth. These shortcomings are reflected in wide confidence intervals for the RDS estimates. Consequently, our ability to detect significant differences in comparisons with other samples is limited. These difficulties in recruiting were likely due to the nature of the population being largely suburban, and having small fragmented networks, unlike the older urban population of injection drug users. New strategies will be needed to reach young suburban IDU who do not travel into the city.
There are also limitations associated with the PRISM. Retest reliabilities of the diagnoses derived from the PRISM interview were modest. Participants often had difficulty recalling past feelings and behaviors. However, previous research suggests that recall errors are most likely to involve less severe symptoms and behaviors (Fendrich and Mackesy-Amiti, 2000; Fendrich and Warner, 1994).
4.2. Conclusions
Young IDU experience major depression, alcohol dependence, anti-social personality disorder, and borderline personality disorder at high rates compared to the general population of young adults. Anxiety disorders in this population, on the other hand, appear to be similar in prevalence to young adults in general. Mood and anxiety disorders, as well as anti-social personality disorder and borderline personality disorder, were generally less prevalent in the current study than in samples drawn from treatment programs. Yet, significant proportions of young IDU with psychiatric disorders are in need of treatment.
Supplementary Material
Acknowledgements
We thank study participants for the time and effort they contributed to this study, and acknowledge the dedication of our staff members who administered RDS, collected data, and otherwise operated field sites in a manner welcoming to potential participants.
Role of Funding Source
This publication was made possible by a grant from the National Institute on Drug Abuse (R01DA020368). NIDA had no further role in the design, analysis or writing of this manuscript, or the decision to submit the paper for publication. The contents of this paper are solely the responsibility of the authors and do not necessarily represent the official views of NIDA or the National Institutes of Health.
Footnotes
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Contributors
All authors contributed to and have approved the final manuscript.
Conflict of Interest
All authors declare that they have no conflicts of interest.
Kappa coefficients, and prevalence rates and RDS estimates of lifetime substance use disorders can be found in the Supplementary Material, which can be found by accessing the online version of this article at http://dx.doi.org and by entering doi:…
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