Study description

The analytic sample was drawn from a previously described study of 817 adult (≥ 18 years) patients receiving treatment for drug or alcohol use disorders in a residential addiction treatment program located in a suburban area of Southeast Michigan during October 2014–January 2016 [35]. This facility served patients living throughout Michigan and received client referrals through contracts with the Michigan Department of Corrections. The typical treatment duration for patients was 60–90 days and patients were separated by gender. Research assistants approached eligible patients (who were aged ≥ 18 years and able to provide informed consent) about their interest in completing a self-administered survey to assess eligibility for enrollment in a randomized controlled trial. Interested participants provided informed consent, completed a paper and pencil survey that took approximately 1 h to finish, and received $5 for participating. This analysis uses data from the cross-sectional survey and is not restricted to those who participated in the randomized controlled trial. The University of Michigan Institutional Review Board approved the study protocol.

We restricted the analytic sample to participants whose treatment was prompted by the justice system (excluded n = 40 participants), had used opioids (heroin or prescription opioids not prescribed by a doctor) in their lifetime (excluded n = 237 participants), and who had non-missing responses to the measures described below (excluded n = 26 participants, see Additional file 1: Figure S1). Our analytic sample included 514 PWUO.

Measures

Justice involvement

We quantified participants’ cumulative and recent pre-treatment justice system interactions using five items: age at first arrest (median 18, range 9–59 years), number of past-year arrests (median 1, range 0–42 arrests), number of lifetime arrests (mode 6–10, categories 1–2, 3–5, 6–10, 11–49, 50–99, or 100 or more arrests), number of months during the past year spent in jail or prison (median 5.3, range 0–12 months), and lifetime number of years spent in jail or prison (median 3.5, range 0–41.3 years). We formed categorical variables using quartile or tertile breaks from distributions in the analytic sample, with modifications when appropriate to enhance interpretability (e.g., juvenile versus adult age at first arrest). Categorical variables included age at first arrest (9–17, 18–20, or 21–59 years), past-year arrests (0, 1–2, 3–42), lifetime arrests (1–5, 6–10, ≥ 11), past-year time spent in jail or prison (0–1.9, 2–5.9, 6–10.9, 11–12 months), and total time spent in jail or prison (0–0.9, 1–3.4, 3.5–7.4, 7.5–41.3 years).

Personal overdose experiences and witnessed overdose

Before answering questions, participants read the following definition of an overdose: “The following questions are about experiences with taking too much drugs or medications/pills. This is sometimes called ‘poisoning,’ ‘nodding out,’ or an ‘overdose’ or ‘OD.’” Participants reported the number of overdoses experienced, timing of their most recent overdose, and substances used during the most recent overdose. Participants then read the definition of a witnessed overdose: “The following questions are about times you have seen someone else taking too much drugs or medications/pills, and/or drinking too much alcohol. This is sometimes called an ‘overdose.’ When someone has an overdose, they might have blue skin color, convulsions, or difficulty breathing, lose consciousness, collapse, cannot be woken up, or have a heart attack or die.” [36] and reported the number of overdoses they witnessed and drugs used by the victim during the most recently witnessed overdose. We formed binary variables for ever experiencing an overdose, experiencing an overdose in the past year, and ever witnessing an overdose. We assessed the number of lifetime personal and witnessed overdoses as three-level categorical variables (0, 1–5, or ≥ 6) and summarized whether the participant’s most recent overdose experience and witnessed overdose involved heroin or prescription opioids.

Covariates

Participants reported whether they had heard of naloxone and identified its purpose as an overdose treatment, drug treatment for opioid dependence, detox, other, or do not know (multiple responses were allowed). For the analysis, we defined naloxone knowledge as having heard of naloxone and correctly identifying its purpose as an overdose treatment. We also examined demographic characteristics, including age (18–29, 30–44, 45–67 years), housing (dichotomized into temporary housing [rooming house/hotel, halfway house/group home, inpatient treatment facility/hospital, jail, shelter, or homeless] vs. stable housing [house/apartment or friend/family member’s house]), education (less than high school/GED or high school/GED or higher), race (black, white, other, or multiple), and ethnicity (Hispanic vs. non-Hispanic). We also summarized substance use characteristics in several time frames, including lifetime and past-year heroin and illicit prescription opioid use (defined as use that was not as prescribed by a doctor). Additionally, we summarized whether participants used heroin for ≥ 7 consecutive days or injected any substance in the month prior to entering treatment or jail. Finally, we described nonmedical prescription opioid use in the month before entering treatment or jail using four items from the Current Opioid Misuse Measure found to describe nonmedical prescription opioid use in the addiction treatment setting [37, 38]. Specifically, we summarized whether participants reported engaging in any of the following when using prescription opioids: taking prescription opioids belonging to someone else, borrowing prescription opioids from someone else, using more than they were prescribed, or using prescription opioids to treat symptoms other than pain.

Latent class analysis

Latent class measurement model

Latent class analysis (LCA) is a statistical technique used to describe unobserved (i.e., latent) subgroups from patterns of observed variables [39]. It is helpful for identifying clusters (subgroups) of individuals who share patterns of characteristics. Lorvick et al. previously described three classes of justice involvement (low, medium, and high) among women who used drugs in California based on their incarceration history and community corrections involvement [29]. We used LCA to identify subgroups of criminal justice system involvement based on five categorical variables: age at first arrest, past year arrests, lifetime arrests, past year time spent in jail or prison, and total time spent in jail or prison.

We fit LCA models with two to six classes and selected the number of latent classes using a combination of interpretability and model fit indices (Akaike information criterion [AIC], Bayesian information criterion [BIC], adjusted BIC, and entropy). Smaller values of the AIC and BIC, and larger values of entropy indicate better relative model fit [39]. After selecting the number of classes, we ensured convergence to a globally optimal solution using 1000 random start values. Item response probabilities, which reflect the distribution of each observed justice involvement variable within each justice involvement class, provided the basis for investigator-assigned class labels used to describe each latent class. We completed LCA analyses in SAS version 9.4 using PROC LCA [39].

Justice involvement by gender

Men and women have different criminal sentencing patterns [40], and the relationship of offenses with drug-related mortality differs by gender [31]. Additionally, men and women are treated separately in many residential addiction treatment programs, including the facility where these data were collected. Therefore, we assessed whether the justice involvement measurement model operated similarly in groups defined by gender (men vs. women). We fit the LCA model with and without constraints that required item response probabilities to be equal by gender, testing the null hypothesis of measurement invariance (i.e., that item response patterns were the same for men and women) [39]. We used a likelihood ratio test (LRT) to test for measurement invariance. Rejecting the LRT (p < 0.05) implied that the measurement model differed by gender.

Correlates of overdose experience, witnessed overdose, and naloxone knowledge

We examined whether the prevalence of experiencing or witnessing an overdose differed by justice involvement class. We also assessed whether naloxone knowledge was associated with ever experiencing or witnessing an overdose or with justice involvement. We summarized associations using bivariate and adjusted prevalence ratios from quasi-Poisson regression models with robust standard errors, an approach appropriate for highly prevalent binary outcomes [41, 42]. Adjusted models included sociodemographic characteristics (age, race, housing status, education level) and substance use characteristics (heroin use and injection drug use), as these covariates could be associated with naloxone knowledge or related outcomes and the main exposures for this analysis (overdose, witnessed overdose, and justice involvement) [5, 32, 33, 43, 44]. For regression analyses, we formed a categorical justice involvement variable by assigning participants to their most likely latent justice involvement class (i.e., the modal class assignment approach).

Sensitivity analyses

We conducted two sensitivity analyses. First, to assess whether the relationships between justice involvement and experiencing an overdose, witnessing an overdose, and naloxone knowledge were robust to the modal class assignment LCA approach, we used the pseudo-class draws approach [45]. We conducted 20 imputations that each assigned participants to a justice involvement class based on LCA posterior probabilities [45]. We repeated quasi-Poisson regressions for each imputed dataset for all associations between justice involvement and overdose outcomes that reached statistical significance using the modal class assignment approach and pooled results using imputation procedures [46]. Second, to examine whether our findings were similar among people who had used opioids recently relative to when they entered treatment, jail, or prison, we re-analyzed the relationships between justice involvement, experiencing an overdose, and witnessing an overdose with naloxone knowledge after restricting the sample to participants who reported using heroin or prescription opioids not prescribed to them in the past year and/or who reported using prescription opioids nonmedically in the month before entering treatment or jail.