Polydrug use and its association with drug treatment outcomes among primary heroin, methamphetamine, and cocaine users (original) (raw)

. Author manuscript; available in PMC: 2018 Nov 1.

Abstract

Background

Polydrug use may challenge effective treatment for substance use disorders. We evaluate whether secondary substance use modifies the association between treatment and primary drug use among primary heroin, cocaine and methamphetamine (MA) users.

Methods

Data were obtained from prospective cohort studies on people who use illicit drugs (PWUD) in California, USA. Using repeated monthly data on self-reported secondary substance use (heroin, cocaine, MA, alcohol or marijuana; ≥1 day in a month), primary drug use (≥1 day in a month), and treatment participation, collected via timeline follow-back, we fitted generalized linear mixed multiple regression models controlling for potential confounders to examine the interactions between treatment and secondary substance use on the odds of primary heroin, cocaine and MA use, respectively.

Results

Included in our study were 587 primary heroin, 444 primary MA, and 501 primary cocaine users, with a median of 32.4, 13.3 and 18.9 years of follow-up, respectively. In the absence of secondary substance use, treatment was strongly associated with decreased odds of primary drug use (adjusted odds ratios (aORs): 0.25, 95% CI: 0.24, 0.27, 0.07 (0.06, 0.08), and 0.07 (0.07, 0.09)) for primary heroin, MA, and cocaine users, respectively. Secondary substance use of any kind moderated these associations (0.82 (0.78, 0.87), 0.25 (0.21, 0.30) and 0.53 (0.45, 0.61), respectively), and these findings were consistent for each type of secondary substance considered. Moreover, we observed different associations in terms of direction and magnitude between secondary substance use and primary drug use during off-treatment periods across substance types.

Conclusions

This study demonstrates secondary substance use moderates the temporal associations between treatment and primary drug use among primary heroin, MA and cocaine users. Disparate patterns of polydrug use require careful measurement and analysis to inform targeted treatment for polydrug users.

Keywords: Polydrug use, heroin, cocaine, methamphetamine, alcohol, marijuana, treatment

1. Introduction

Heroin, cocaine, and methamphetamine (MA) have among the highest global burden of disease among illicit drugs (Degenhardt et al., 2013) and are associated with severe public health and social consequences such as mortality, morbidity, and criminality (Y. I. Hser, Evans, Huang, Brecht, & Li, 2008; UNODC, 2012; Wang et al., 2016). In addition, high levels of polydrug use have been reported among people who use illicit drugs (PWUD) in a wide variety of treatment and community settings internationally (Ball & Ross, 1991; Booth, Leukefeld, Falck, Wang, & Carlson, 2006; Byqvist, 2006; Darke & Hall, 1995; Ives & Ghelani, 2006; Leri, Bruneau, & Stewart, 2003; Leri et al., 2005). Compared to mono-drug use, polydrug use has been associated with greater psychopathology (Booth, et al., 2006; Medina & Shear, 2007; Sumnall, Wagstaff, & Cole, 2004); higher levels of risky health behaviors (Patterson, Semple, Zians, & Strathdee, 2005); decreased cognitive functioning (Dillon, Copeland, & Jansen, 2003); poorer treatment engagement (John, Kwiatkowski, & Booth, 2001) and treatment outcomes (Bovasso & Cacciola, 2003; DeMaria, Sterling, & Weinstein, 2000; Williamson, Darke, Ross, & Teesson, 2006b); and increased non-fatal overdoses as well as drug-related deaths (Coffin et al., 2003; Strang et al., 1999).

Treatment for opioid use disorders in the form of non-time limited opioid agonist treatment has been shown to be effective in numerous randomized trials, meta-analyses, and large-scale longitudinal studies (Amato et al., 2005; Faggiano, Vigna-Taglianti, Versino, & Lemma, 2003; Mattick, Kimber, Breen, & Davoli, 2008); however, the evidence for effective treatment of cocaine or MA use disorders is not as clear (Fischer et al., 2015). While psychosocial treatment has shown varying degrees of promise in clinical trials (Courtney & Ray, 2014; Pérez-Mañá, Castells, Vidal, Casas, & Capellà, 2011), the search for pharmacological treatment for cocaine or MA use has yet to produce an effective medication (Brensilver, Heinzerling, & Shoptaw, 2013; Kishi, Matsuda, Iwata, & Correll, 2013; Minozzi et al., 2015). Development of effective treatment strategies for people who use multiple illicit drugs, or polydrug users, is further challenged by the variety of substances combinations and patterns of use (European Monitoring Centre for Drugs and Drug Addiction, 2009; Ives & Ghelani, 2006). As a consequence, clinical guidelines provide minimal guidance on the management and impact of polydrug use (American Psychiatric Association, 2006; Management of Substance Use Disorders Working Group, 2015; National Institute on Drug Abuse, 2012). For instance, the only suggestion found in treatment guidelines from the US Veterans Health Department and the National Institute on Drug Abuse was to manage multiple substance use disorders according to the recommendations made for each of those individual disorders (Management of Substance Use Disorders Working Group, 2015; National Institute on Drug Abuse, 2012).

Previous studies have mainly evaluated the relationships between cocaine use and treatment outcomes among heroin dependent individuals. For example, prior observational studies have shown that cocaine use was associated with increased heroin use at treatment entry, poorer treatment outcomes including retention, and subsequent relapse into heroin use (Hartel et al., 1995; Sullivan et al., 2010; Termorshuizen, Krol, Prins, & van Ameijden, 2005; Williamson, Darke, Ross, & Teesson, 2006a; Williamson, et al., 2006b) among patients receiving opioid agonist treatment. However, there is substantially less evidence regarding polydrug use and treatment outcomes among individuals primarily use cocaine or MA. Furthermore, the majority of PWUD concurrently use alcohol or marijuana (Brecht, Huang, Evans, & Hser, 2008). One review study revealed that alcohol use post-drug treatment increased relapse to drug use, but evidence regarding whether alcohol could become a substitute addiction remained inconclusive(Staiger, Richardson, Long, Carr, & Marlatt, 2013). Evidence on the association between marijuana use and drug treatment outcomes also produced conflicting results, with some demonstrating beneficial effects and others showing an adverse impact (Zielinski et al., 2016)

We take advantage of a unique set of California-based prospective cohort studies, which tracked monthly drug use and treatment receipt for as long as three decades for PWUD characterized by the primary use of heroin, cocaine and MA. We considered the use of any substance other than the primary drug as secondary substance use, including heroin, cocaine, MA, alcohol and marijuana. Polydrug use was thus defined as self-reported use of any two substances in a given month during study follow-up. We conducted this study to test the hypothesis that secondary substance use would moderate the associations, if treatment was associated with decreased odds of primary drug use. In addition, we examined the relationships between secondary substance use and primary drug use in the absence of treatment to investigate natural polydrug use patterns.

2. Methods

2.1 Study design and subjects

Data were derived from four non-overlapping studies that collected monthly information of adult PWUD in California using the Natural History Interview (NHI): (1) the 33-year Civil Addict Program (CAP) (Y. I. Hser, Hoffman, Grella, & Anglin, 2001); (2) the cocaine treatment evaluation study (CTE) (Y. I. Hser et al., 2006); (3) the methamphetamine natural history study (METH) (Brecht, O’Brien, von Mayrhauser, & Anglin, 2004); and (4) the treatment process study (TXPR) (Y. I. Hser, Huang, Teruya, & Anglin, 2004), with baseline assessments executed in 1964, 1988–1989, 1995–1997, and 1995 respectively, and the last year of follow-up in 1997, 2002–2003, 1999–2002, and 1996 respectively. All studies recruited subjects from treatment settings only, and baseline drug use profiles across studies were previously presented(Nosyk et al., 2014).

We included all participants from the four studies and classified them into primary heroin, cocaine, and MA use categories according to the definitions adopted by the original studies (Brecht, et al., 2004; Y. I. Hser, et al., 2001; Y. I. Hser, et al., 2004; Y. I. Hser, Huang, Teruya, & Douglas Anglin, 2003; Y. I. Hser, et al., 2006). The primary drug use classification was determined at baseline by the drug for which the subject was receiving treatment. Such classification of primary drug use was found to present valid information about drug use patterns over time (Brecht, et al., 2008). In the current study, we analyzed participants’ drug use histories from their first use of the primary drug. We excluded observations (15.5%) during incarceration because drug use information was not available for all studies during these periods.

Use of these data for the current analysis was reviewed and approved by the University of California Los Angeles Institutional Review Board.

2.2 Instruments/Measures

All four studies collected information using the NHI, which was adapted from instruments designed by Nurco and colleagues (Nurco, Bonito, Lerner, & Balter, 1975) and has been used with various drug-using populations. It was designed to collect retrospective longitudinal quantitative data on drug use and related behaviors. The instrument consists of “static” and “dynamic” forms that permit the capture of longitudinal, sequential data on drug use, employment, criminal involvement, treatment, and other behaviors over the life course of research participants (McGlothlin, Anglin, & Wilson, 1977). Using an illustrated time-line, the interviewee notes major life events and then identifies time periods associated with specific behaviors, with periods delineated by changes in behavior. These reported data are translated to longitudinal data of behaviors for each month. The NHI has been shown to have generally high reliability; correlation coefficients of inter-variable relationships, based on 46 variables measured at two interviews 10 years apart, ranged as high as 0.86 and 0.90 (Anglin, Hser, & Chou, 1993; Chou, Hser, & Anglin, 1996; Y.I. Hser, Anglin, & Chou, 1992).

For the purpose of the current study, self-reported substance use and treatment information within the same month based on the NHI data was used to construct the variables of interest. The primary outcome of interest was a binary variable capturing any primary drug use (use for ≥1 day in a month) for the primary heroin, MA and cocaine use categories, respectively. Given the short timeframe between repeated measures, primary drug use measured in the current and subsequent month were highly correlated (Pearson correlation coefficient >0.95), and analyses performed using outcomes measured at both time points provided similar results.

The main covariates of interest were treatment participation and secondary substance use. Treatment participation was a binary variable capturing monthly self-reported treatment for the primary drug of use ≥1 day in the treatment, including all treatment modalities: opioid agonist treatment with methadone, inpatient, outpatient and residential treatment. We constructed binary variables capturing secondary substance use, defined as use for ≥1 day in a month of any other substance than the primary drug, including heroin, cocaine, MA, alcohol, and marijuana. In addition, several binary variables were created to capture secondary alcohol use (≥1 day in a month), secondary marijuana use (≥1 day in a month), and secondary illicit drug use (≥1 day in a month). Secondary illicit drugs including heroin, MA or cocaine were grouped together due to the small numbers of observations available.

In addition, we considered a range of individual characteristics as potential confounders, including age of first drug use, gender, race/ethnicity, marital status, highest completed level of education and employment. We also considered any history of incarceration as it has been shown to influence drug use behaviors (Farrington, 2003), calendar year (1960’s, 1970’s, 1980’s, 1990’s and after) and study cohort from which the individual-level data was drawn.

2.3 Statistical analysis

Our statistical analysis proceeded in four steps. First, we assessed the characteristics of study participants by primary drug type. Second, we described substance use patterns by examining the frequency of possible combinations of secondary substance use among all available monthly observations, stratified by treatment participation.

Third, we employed generalized linear mixed multiple regression models (GLMMs) (Raudenbush & Bryk, 2002) with a logit link, binomial distribution and random intercept to investigate the association between treatment participation and primary drug use for each primary drug group, measured within the same month. We first examined the association between treatment and primary drug use adjusting for the secondary substance use. To examine whether secondary substance use modified the association, we conducted the following models. First, we examined the interaction between treatment and any secondary substance use; we then examined the interaction between treatment and each type of secondary substance use, adjusting for the use of other secondary substances. In each model, we controlled for the covariates described above.

Fourth, we investigated the associations between secondary substance use and primary drug use in the absence of treatment using GLMMs, to explore natural polydrug use patterns. The models included each type of secondary substance use as a binary variable, adjusting for each other and the same set of covariates as previous models.

Finally, in order to assess the robustness of the results, we repeated analyses in steps three and four on daily primary drug use (≥28 days vs <28 days in a month). All statistical analyses were executed in SAS version 9.4.

3. Results

A total of 1,532 participants were included in the study, including 587 primary heroin users, 444 primary MA users, and 501 primary cocaine users, with 159,240, 27,573 and 131,667 monthly observations, respectively.

As shown in Table 1, primary heroin users were mainly Hispanic (53.7%), male (91.5%), divorced or separated (57.0%), with less than a high school education (47.5%), and initiated heroin use prior to 1970 (84.0%) at a median age of 18. In contrast, primary MA users were primarily white (52.5%), male (52.0%), divorced or separated (95.0%), with a college education (34.5%), and initiated MA use in the 1980’s and 1990’s (80.8%) at a median age of 18. Most primary cocaine users were black (66.1%), male (75.6%), divorced or separated (56.8%), with a college education (45.2%), and initiated cocaine use in the 1970’s and 1980’s (90.2%) at a median age of 22. Over a median follow-up of 32.4, 13.3 and 18.9 years respectively, 91.8% of primary heroin users, 57.2% of primary MA users, and 42.3% of primary cocaine users had ever been incarcerated.

Table 1.

Characteristics of study participants at baseline by primary drug type (N=1532).

The patterns of substance use among each primary drug group were summarized in Table 2. The majority of primary heroin, MA and cocaine users (89.6%, 95.0% and 93.4% respectively) concurrently used two or more substances in the same month over study follow-up. For primary heroin users, the most common drug use pattern was heroin use only, followed by the concurrent use of heroin and alcohol at the same month, each comprising 27.4% and 17.2% of all monthly observations. For primary MA users, the most common drug use pattern was concurrent use of MA, alcohol and marijuana (15.2%), followed by the use of MA only (10.9%), and the concurrent use of MA and alcohol (10.2%). For primary cocaine users, concurrent use of cocaine, alcohol and marijuana was the most common (18.0%), followed by the concurrent use of cocaine and alcohol (16.2%), and the concurrent use of alcohol and marijuana (11.3%). Table 2 also showed that a small proportion of time was spent on treatment among each primary drug group, with the proportion on treatment among all monthly observations being 17.3%, 5.3% and 5.7% for primary heroin, MA, and cocaine users, respectively.

Table 2.

Patterns of self-reported monthly substance use by primary drug type and treatment participation (N=1532, monthly observations=340,926) a.

Results of the multiple regression analyses are presented in Table 3. We found secondary substance use consistently moderated associations between treatment and primary drug use. During months without any secondary substance use, treatment was strongly associated with decreased odds of primary drug use. The adjusted odds ratios (aORs) were 0.25 (95% confidence interval (CI): 0.24, 0.27), 0.07 (0.06, 0.08), and 0.07 (0.07, 0.09) for primary heroin, MA, and cocaine users, respectively. With the use of any secondary substance, the adjusted odds ratios were 0.82 (0.78, 0.87), 0.25 (0.21, 0.30) and 0.53 (0.45, 0.61) for primary heroin, MA, and cocaine users, respectively. In addition, the findings were consistent for each type of secondary substance use. Without secondary use of alcohol, marijuana or secondary illicit drugs, respectively, treatment was associated with decreased odds of primary drug use; however, these associations were smaller or non-significant in the presence of the secondary use of alcohol, marijuana or other drugs (Table 3). Moreover, results from our sensitivity analyses on daily primary drug use supported the main findings (Table S1).

Table 3.

Adjusted associations between self-reported treatment participation and primary drug use, stratified by secondary substance use (N=1532, monthly observations=340,926) a.

Table 4 shows the relationship between secondary substance use and the use of primary drug, in the absence of treatment. We found secondary cocaine and MA use were associated with increased odds of primary heroin use (3.60 (3.30, 3.91), 1.16 (1.07, 1.27), respectively), while secondary alcohol and marijuana use were associated with decreased odds of primary heroin use (0.77 (0.73, 0.80)), 0.92 (0.87, 0.98), respectively). In contrast, secondary heroin, alcohol and marijuana use were associated with increased odds of primary MA use (6.72 (5.71, 7.91), 3.31 (3.12, 3.52) and 5.95 (5.57, 6.36), respectively). Similarly, secondary heroin, alcohol and marijuana use were associated with increased odds of primary cocaine use (4.21 (3.78, 4.68), 9.56 (9.06, 10.1) and 1.09 (1.04, 1.14), respectively), although effect sizes differed greatly. Our results from the sensitivity analyses on daily primary drug use generally supported the main findings, except that secondary MA use was associated with increased odds of primary heroin use (1.16 (1.07, 1.27)), but associated with decreased odds of daily primary heroin use (0.52 (0.48, 0.56)). In addition, any MA use and any cocaine use were not associated among both primary MA and primary cocaine users, but they were associated with decreased odds of daily use of the other drug (0.78 (0.73, 0.83), 0.65 (0.57, 0.74), respectively).

Table 4.

Adjusted associations between self-reported secondary substance use and primary drug use, in the absence of treatment (N=1532, monthly observations=303,211) a.

4. Discussion

To summarize, despite observing varying associations between secondary substance use and primary use of heroin, MA or cocaine, secondary substance use consistently moderated the temporal associations between treatment and primary drug use.

Our findings suggest that secondary substance use compromises the effects of drug treatment. Existing evidence supports that use of cocaine compromises treatment outcomes among heroin users, including poorer treatment retention and increased risk of heroin use relapse (Bovasso & Cacciola, 2003; DeMaria, et al., 2000; Downey, Helmus, & Schuster, 2000; Hartel, et al., 1995; Sullivan, et al., 2010; Termorshuizen, et al., 2005; Williamson, et al., 2006a, 2006b). Brecht et al. found secondary use of cocaine or heroin was associated with not completing treatment among MA users (Brecht, Greenwell, & Anglin, 2005), which was in line with our results, although treatment participation rather than completion was measured in the current study. Evidence was less consistent, however, regarding the impact of alcohol use or marijuana use on drug treatment outcomes (Ottomanelli, 1999; Staiger, et al., 2013; Zielinski, et al., 2016). Ottomanelli conducted a literature review on alcohol use by methadone patients and found no evidence that alcohol abuse diminished treatment retention (Ottomanelli, 1999). On the other hand, a more recent review revealed that alcohol use increased the likelihood of relapse to drug use (Staiger, et al., 2013). Similarly, Wasserman et al. found marijuana use was associated with greater risk of relapse to heroin use among individuals on opioid agonist treatment (Wasserman, Weinstein, Havassy, & Hall, 1998), while Epstein and Preston did not observe an association between marijuana use and treatment retention(Epstein & Preston, 2003). The heterogeneous findings might be partially explained by the different treatment outcome measures used. Regardless, our findings serve to further underline the importance of screening for ongoing multiple substances use among PWUD who initiate treatment. Existing drug treatment programs should be equipped to deal with people who use polydrugs.

In addition, we found high levels of alcohol and marijuana use among all groups. Both secondary alcohol and marijuana use were positively associated with primary MA use, and primary cocaine use. These results were consistent with several previous studies (Barrett, Darredeau, & Pihl, 2006; Brecht, et al., 2008; Bujarski et al., 2014; Furr, Delva, & Anthony, 2000; McKay, Alterman, Rutherford, Cacciola, & McLellan, 1999) and might indicate users’ desire to counterbalance the effects of stimulant use (Brecht, et al., 2008). In contrast, there appeared to be negative associations between secondary alcohol and marijuana use with primary heroin use. One recent study also reported reduced opioid use frequency among people who inject drugs in California who also used marijuana compared to who did not (Kral et al., 2015). However, the potential role of medical marijuana use as a harm reduction approach among opioid-using populations is still under debate and requires more research (Lucas et al., 2016). Anglin et al. (Anglin, Almog, Fisher, & Peters, 1989) and Almog et al. (Almog, Anglin, & Fisher, 1993) found an inverse relationship between alcohol use and heroin use among heroin dependent individuals in opioid treatment programs in California and the pattern was evident in addiction, treatment and post-discharge stages of individuals’ drug use career (Anglin, et al., 1989). The inverse pattern suggested that primary heroin users might substitute alcohol for heroin to compensate for a reduction in their use of heroin (Almog, et al., 1993). The strong negative association between secondary alcohol use and daily primary heroin use observed in our study further supported this.

Moreover, our results of the positive association between heroin and cocaine use are consistent with heroin-cocaine co-use patterns previously reported in the literature (Brecht, et al., 2008; Kreek, 1997; Leri, et al., 2003; Leri, et al., 2005; Roy, Richer, Arruda, Vandermeerschen, & Bruneau, 2013). Brecht et al. (Brecht, et al., 2008) found the average days of use per month of heroin and cocaine were positively related for primary heroin and cocaine users. Simultaneous use of heroin and cocaine in the form of “speedball” is also common(Leri, et al., 2003). Moreover, heroin users often report the sequential use of cocaine after heroin to enhance euphoria or to reduce the withdrawal symptoms experienced during their typical day (Leri, et al., 2003) or to counteract the physical depressive effects of opioids (Roy, et al., 2013). Cocaine users also sequentially use cocaine followed by heroin to self-mediate side effects of chronic cocaine administration (Kreek, 1997).

Cocaine and MA are the most prevalent stimulants among those with use disorders (Degenhardt et al., 2014) and are both characterized by intense and short-lived effects (National Institute on Drug Abuse; National Institute on Drug Abuse). While we found both drugs were associated with increased odds of any heroin use, MA use was strongly associated with decreased odds of daily heroin use as opposed to the positive association between cocaine and daily heroin use. We postulate that these differences could be partially driven by either or both following facts: (i) the drug half-life for MA is substantially longer than that of cocaine (National Institute on Drug Abuse; National Institute on Drug Abuse), which could explain different patterns of strategic co-use of these stimulants with heroin; and (ii) the expansion of widespread availability of MA in the U.S. and California occurred during the 1990s (Gonzales, Mooney, & Rawson, 2010), a period after a vast majority of individuals in our study sample initiated first use of either substance.

Furthermore, our sensitivity analyses suggested that primary users of MA or cocaine may substitute one stimulant for another, findings that would appear intuitive given that the physical and mental effects of cocaine and MA use are similar (National Institute on Drug Abuse; National Institute on Drug Abuse). Markedly, secondary use of cocaine or MA was associated with decreased odds of daily primary use of the other. Our study represents a significant contribution to a previously-scarce evidence base on co-use patterns of these drugs (Cox, Jacobs, Leblanc, & Marshman, 1983; Ellinwood, Eibergen, & Kilbey, 1976) and helps clarify patterns of substitution and co-use among polydrug users.

Our study had several limitations that require consideration. First, our data came from self-reported interviews at a maximum 10-year intervals from which monthly records of drug use and other variables were constructed. Reports of days of drug use may be influenced by recall bias given the long duration between follow-up interviews, although recall bias was minimized by using records-based anchors. Recall may be differential for different drug types, and in periods with and without multiple drug use. Nonetheless, a comparison of drug use trajectories measured by days of use obtained using Addiction Severity Index and NHI data showed comparable temporal patterns of alcohol, heroin, cocaine, methamphetamine, and marijuana use, supporting these instruments’ reliability for longitudinal examination of self-reported drug use(Murphy, Hser, Huang, Brecht, & Herbeck, 2010). In our analysis, we used binary classifications of drug use rather than the reported exact days of use, which could have reduced the recall bias. Sensitivity analysis on daily primary drug use also supported the findings from the main analysis on any primary drug use, further strengthening our results.

Second, we could not distinguish different treatment modalities in our dataset, a level of measurement error which was likely to differ across drug use groups and patients. We expected there be less variation in treatment programs for opioid users than stimulant users. No pharmacological treatment is available for stimulant use, and a heterogeneous mix of psychosocial treatment was likely captured in the ‘treatment’ indicator. Treatment for opioid use disorders may have incorporated some psychosocial treatment, however pharmacological treatment with methadone was likely a common feature across all programs. Third, although we considered both any and daily primary drug use in the analysis, we did not distinguish the frequency of non-primary drug use and potential impact of the secondary substance use frequency on the outcomes of interest. Fourth, although we controlled for a number of covariates which are potentially associated with the outcome, we could not rule out unmeasured confounding, potentially due to motivational and psychosocial characteristics, as well as measures of treatment adherence. Careful measurement of these factors in future studies may further inform the design and target clientele for multi-drug treatment programs. Lastly, the results might not reflect polydrug use patterns for other drug use populations, as our study samples represented three groups of people who primarily use heroin, cocaine, or MA in California. Our samples might not be representative of PWUD in the current drug use era, as they were primarily recruited in the 1960’s, 1980’s and 1990’s for primary heroin, cocaine, and MA users respectively. However, samples of each primary drug use group were followed for an extended period (Table 1) and the current analysis might have captured the average drug use patterns over time. Our main findings that secondary substance use moderated the temporal associations between treatment and primary drug use were also consistent across three primary drug groups, despite differential recruitment periods. Otherwise, the treatment options for opioid users included primarily methadone maintenance treatment for our study periods, and the findings might not be generalizable to opioid agonist treatment using buprenorphine.

In conclusion, our results indicate that secondary substance use moderates the association between treatment and primary drug use among primary heroin, MA and cocaine users. Diverse polydrug use patterns should be carefully examined and the underlying pharmacological, psychosocial and behavioral reasons for certain substance combinations need to be better understood to inform the development of targeted treatment options for polydrug users.

Supplementary Material

supplement

Acknowledgments

This work was supported by US National Institutes of Health, National Institute on Drug Abuse [R01DA032551]; and for the UCLA ISAP Center for Advancing Longitudinal Drug Abuse Research (CALDAR) [P30 DA016383]. Dr. Bohdan Nosyk is supported by a Michael Smith Foundation for Health Research Scholar award.

Abbreviations

MA

methamphetamine

CAP

the Civil Addict Program

CTE

the cocaine treatment evaluation study

METH

the methamphetamine natural history study

TXPR

the treatment process study

TUE

the treatment utilization and effectiveness study

NHI

Natural History Interview

GLMM

generalized linear mixed multiple regression model

Footnotes

Contributors: LW designed the study, performed analyses, and drafted the manuscript. JEM designed the study, managed data, performed analyses, and revised the manuscript. EK interpreted the results and drafted the manuscript. EE revised the manuscript and provided critical comments. DH revised the manuscript and provided critical comments. LL revised the manuscript and provided critical comments. YH revised the manuscript and provided critical comments. BN designed the study, interpreted the results, revised the manuscript and provided critical comments. LW and JEM had full access to all of the data in the study and can take responsibility for the integrity of the data and the accuracy of the data analysis. All authors approved of the final manuscript.

The authors declared no conflict of interest.

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