Elsevier

Drug and Alcohol Dependence

Volume 192, 1 November 2018, Pages 80-87
Drug and Alcohol Dependence

Full length article
Polydrug use among heroin users in Cleveland, OH

https://doi.org/10.1016/j.drugalcdep.2018.06.039Get rights and content

Highlights

  • Heroin users in Ohio also consumed a variety of other drugs.

  • After alcohol and marijuana crack was the most common co-used drug.

  • Five patterns of polydrug use were identified among heroin users in Cleveland.

  • Patterns were linked to liking and being anxious when cannot obtain the drug.

  • High drug availability and low cost facilitate polydrug use.

Abstract

Background

Since 2000, heroin use patterns have shifted within the United States. How this change may relate to polydrug use among local heroin users is unknown. Although polydrug use has been studied, user perceptions of drug use in terms of health risks, arrest risk, availability, cost, liking, and dependence have not been considered.

Methods

Data are presented from a brief, face-to-face survey conducted in 2016 of 200 non–in-treatment heroin users from Cleveland, OH. We assessed the use of and attitudes on alcohol, marijuana, methamphetamine, heroin, crack cocaine, powder cocaine, and prescription drugs. We estimated polydrug (concurrent past month) use with cluster analysis and latent profiles. Regression analysis estimated the strength of relationships between attitudes and frequency of use.

Results

We identified five clusters: Cluster 1 used heroin concomitantly with alcohol and occasionally crack; Cluster 2 used heroin and crack cocaine daily; Cluster 3 used heroin daily and almost exclusively; Cluster 4 used heroin and marijuana daily; and Cluster 5 were part-time drug users. Drug use frequency was associated with liking and being anxious when drugs could not be obtained. High perceived availability of heroin and cocaine and low cost facilitated polydrug use.

Conclusions

Understanding polydrug use clusters among heroin users is important for addressing the larger opioid epidemic. Users’ perceptions of a drug’s availability and cost appeared to facilitate polydrug use and justify more detailed future research on drug access.

Introduction

The heroin epidemic is rapidly growing in the United States and poses a significant problem in Ohio (Daniulaityte et al., 2017; Penm et al., 2017; Winstanley et al., 2016). In 2016, more than 4000 drug overdose deaths occurred in Ohio, a 33% increase compared to 2015 (Ohio Department of Health, 2018), and Ohio’s age-adjusted drug overdose mortality rate of 39.1 per 100,000 was the second highest the United States (National Center for Health Statistics, 2017). Although overdose deaths from prescription opioids have decreased in recent years in Ohio and the rest of the United States, deaths from heroin use have continued to steadily increase since 2010 (Rudd et al., 2014). Recent surveillance reports across the United States also the have detailed involvement of multiple drugs in opioid-related overdoses. Data from the National Vital Statistics System reveal that between 2000 and 2015, the percentage of cocaine overdose deaths involving any opioid increased from 29% to 63% (McCall Jones et al., 2017). More than 30% of fatal opioid overdoses also involve benzodiazepines (Sun et al., 2017), which are frequently used concurrently with opioids and alcohol (Ogbu et al., 2015; Park et al., 2015).

These data show increased trends in opioid overdose deaths involving multiple drugs; the negative health consequences of polydrug use have been thoroughly investigated in a number of clinical and population-based samples (McCabe et al., 2006). However, comparatively less is known about recent patterns of polydrug use among people who use opioids. In high-risk states like Ohio, these patterns could indicate the emergence of new drug preferences that are influenced by illicit drug markets and social norms (e.g., state overdose policies, syringe services) (Penm et al., 2017). Similarly, documented age and cohort effects in the initiation of prescription opioids and heroin support a hypothesis of pattern variability among people who currently use heroin (Cicero et al., 2014, 2015, 2017). That is, these people may differ by patterns of concurrent drug use and by frequency of heroin and other drug use (Al-Tayyib et al., 2017; Betts et al., 2015, 2016; Jones et al., 2012; Kelly et al., 2017) and pathways that lead to these patterns (Dasgupta et al., 2017). Identification of such patterns is critical to facilitate the implementation of evidence-based interventions for populations who report high-risk polydrug use (Winstanley et al., 2016).

Because the distribution and determinants of polydrug use have relevance for overdose prevention program and policy planning, there has been an increased focus on methodologies that are, person-centered and that statistically uncover subpopulations with distinct combinations of polydrug use (Lanza and Rhoades, 2013; Shaw et al., 2008). Population estimates of these polydrug subpopulations may inform personalization of opioid treatments, community interventions, and best practices (Supplee et al., 2013). Such methods may help differentiate simultaneous use, use at the same time as heroin, and concurrent use, use of heroin and other drugs within the same period (e.g., 1 month) but not necessarily simultaneously (McCabe et al., 2006; Quek et al., 2013). In addition, attitudes and perceptions related to the availability, psychoactive effects, and health consequences of different drugs can provide key insights into factors that drive polydrug use (Pedersen et al., 2017; Votaw et al., 2017).

This study investigated patterns of polydrug use among participants of a syringe service program in Cleveland, Ohio. Unique to this study, we asked about both concurrent and simultaneous drug use and measured the attitudes heroin users had about different drugs they used. We used cluster analytic techniques to identify subpopulations who demonstrate distinct patterns of polydrug use and examined demographic and attitudinal correlates.

Section snippets

Data collection

The data presented in this paper were collected using a brief survey instrument administered to participants of a Syringe Exchange Program (SEP). The SEP is run by Cleveland’s Free Clinic and has been in continuous operation since 1995. Although the SEP operates in three locations, this study only recruited participants from the Westside location. We made this decision based on data from the SEP that indicated that Cleveland’s recent increases in heroin use are most significant in Cleveland’s

Sample

Sample characteristics are presented in Table 1. We collected data from 200 subjects. Most were white (74%), male (68%), and middle-aged (mean and median ages were around 38). Most were unemployed (71%), but almost all subjects in the sample reported at least one source of income. Nineteen percent were homeless. In the past month almost, all subjects (97.5%) used heroin (100% used in the lifetime), 40% used marijuana, 40% used crack, 39% used alcohol, 30% used prescription opioids, and 20% used

Discussion

Our paper uses ethnographic data to estimate distinct clusters among a sample of heroin users and to discuss the role of attitude factors associated with drug use.

The major cluster of heroin users identified in this study (Group 3) use heroin daily and almost exclusively. Considering the overwhelming local trend in heroin use, and that many younger heroin users seem to be initiating heroin use directly (i.e., not first using any other drugs), this group is not surprising. It represents what

Conclusions

Our study shows broad variability between patterns and types of polydrug use among heroin users in Cleveland. Polydrug use patterns are linked to attitudes toward each of the drugs, especially liking and being anxious when the drug is unavailable. Understanding polydrug use clusters among heroin users are important in addressing the larger epidemic from both prevention and treatment perspectives.

Role of funding sources

This work was supported in part by NIDA’s grant R01 DA025163 to Hoffer and Bobashev.

The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Contributors

GB and LH have conceived the study, LH has supervised data collection, GB and KT have analyzed the data, GB, KT, NP, and LH contributed to the interpretation of the results and writing of the manuscript. All authors have approved the final manuscript.

Conflict of interest

No conflict declared

Acknowledgements

We thank Michelle Myers of RTI International for editing the manuscript. This study was funded by a grant from the National Institutes of Health, National Institute on Drug Abuse (5R01 DA025163).

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