Abstract
Justice-involved individuals with alcohol and drug use problems reoffend at higher rates than their non-using counterparts, with alcohol and drug use serving as an important vector to recidivism. At the daily level, exposure to stressors may exacerbate problematic alcohol and drug use; at the individual-level, prior treatment experiences may mitigate substance use as individuals adapt to and learn new coping mechanisms. We conducted a daily diary study using Interactive Voice Response (IVR) technology over 14 consecutive days with 117 men on probation or parole participating in a community-based treatment program (n = 860 calls) and referred to medication-assisted treatment. Participants reported daily stressors, craving for alcohol and illegal drugs, and use of alcohol and illegal drugs one time each day. Results of multilevel models showed significant day-to-day fluctuation in alcohol and drug craving and use. In concurrent models, increases in daily stressors were associated with increases in cravings and use of illegal drugs. Prior treatment experience modified many of these relationships, and additional lagged models revealed that those with less treatment experience reported an increase in next-day alcohol craving when they experienced increases in stressors on the previous day compared to those with more treatment experience. Collectively, these findings highlight the importance of tailoring treatment as a function of individual differences, including prior treatment experiences, and targeting daily stressors and subsequent cravings among justice-involved adults with alcohol and drug use problems.
Keywords: Daily stressors, alcohol, cravings, drug use, IVR, justice-involved individuals
Alcohol and drug use problems are well-established risk factors for criminal behavior (Stevens, Laursen, Mortensen, Agerbo, & Dean, 2015; Walters, 2016), with high rates of problematic alcohol and drug use found in correctional populations. In the United States, for example, estimates suggest that approximately eight out of 10 justice-involved individuals in state prisons have a history of illicit drug use, with two-thirds reporting a history of regular illicit drug use (Mumola & Karberg, 2006). Despite successful completion of treatment programs while incarcerated and/or participation in ongoing community-based treatment after release, many justice-involved individuals with alcohol and drug use problems relapse within months of reentry (Binswanger et al., 2012; Merrall et al., 2010). Relapse, in turn, is associated with increased risk of re-offending and re-incarceration (Dowden & Brown, 2002; Stahler et al., 2013). Further, there has been a shift away from incarceration towards alternative sanctions, including diversion programming and community-based supervision, for justice-involved individuals with alcohol and drug use problems. Although there has been considerable empirical attention focused on determining the effectiveness of various treatment programs in preventing relapse and/or reducing recidivism, there have been very few studies of the mechanisms underlying these processes in this particular population. Thus, identifying the mechanism(s) underlying return to alcohol and drug use among justice-involved individuals in the community, specifically, may inform treatment strategies that not only reduce the likelihood of relapse, but also of recidivism. Doing so has the potential to promote successful community reintegration among justice-involved individuals with alcohol and drug use problems (Resor & Blume, 2008), thereby reducing the burden of substance use on the criminal justice system.
One of the key predictors of continued problematic alcohol and drug use among justice-involved individuals and others with alcohol and drug use problems is cravings for alcohol and drugs (Simpson, Stappenbeck, Varra, Moore, & Kaysen, 2012). Research demonstrates that cravings, defined as an unwanted desire or urge to use while abstinent (Weiss, 2005), predict relapse among individuals with alcohol and drug dependencies (Fatseas et al., 2015; Sinha et al., 2011), as well as the amount of substance consumed (Flannery, Poole, Gallop, & Volpicelli, 2003). Research also shows that alcohol and drug use, in turn, increase subsequent cravings, resulting in further use. For example, a recent study of 165 individuals seeking treatment for concurrent posttraumatic stress disorder (PTSD) and alcohol dependence showed that alcohol use had a lagged effect on alcohol craving (Kaczkurkin, Asnaani, Alpert, & Foa, 2016). For these reasons, the management of alcohol and drug use cravings is a primary focus of many substance use treatment programs (Tiffany & Wray, 2012).
We consider cravings and use of alcohol and drugs separately. The brain structures involved in addiction are different for alcohol and drugs. Drug cues elicit grater amygdala activation compared to neutral stimuli in individuals addicted to cocaine (Bonson et al., 2002), but there is a notable lack of consistent amygdala activation associated with alcohol use disorder across 28 alcohol cue reactivity studies (Schacht et al., 2013). There are also different mechanisms involved in accessing the reward centers for alcohol and drugs (Karoly, YorkWilliams, & Hutchinson, 2015). Additionally, the cues for alcohol, including stressors, may differ from cues for drugs, especially within a justice-involved population. Access to alcohol is legal so stressors may be more likely to activate a craving for alcohol, whereas stressors associated with drug use may need to be more severe because drug use is grounds for parole violation.
In addition to cravings, exposure to traumatic or stressful events may exacerbate problematic alcohol and drug use among justice-involved individuals (Simpson, Stappenbeck, et al., 2012). For instance, research shows high rates of comorbidity between PTSD and alcohol and drug use disorders (Kessler, Chiu, Demler, & Walters, 2005; Reynolds, Mezey, Chapman, Wheeler, Drummond, & Baldacchino, 2005). Beyond exposure to traumatic events, daily stressors – that is, routine tangible events of day-to-day living (e.g., arguments) – may be associated with both alcohol and drug cravings and use (e.g., Armeli, Dehart, Tennen, Todd, & Affleck, 2007). While daily stressors may seem relatively inconsequential compared to traumatic events, they can have immediate negative impacts on physical and psychological well-being (Almeida, 2005; Almeida, Wethington, & Kessler, 2002). Daily stressors can accumulate over days to create persistent irritations and overloads that may result in more negative affect, physical health symptoms (Almeida et al., 2002), memory failures (Neupert, Almeida, Mroczek, & Spiro, 2006), and serious stress reactions, such as anxiety and depression (Lazarus, 1999; Zautra, 2003). Among justice-involved individuals with alcohol and drug use problems, daily stressors may be particularly numerous and salient during the first few months of community reintegration, as a result of difficulties in finding housing and employment, low social support, re-unification with family, and the transition to a considerably less structured and more self-directed environment (Binswanger et al., 2012; Western, Braga, Davis, & Sirois, 2015). Community reintegration also marks the reintroduction of alcohol and drug use-related triggers (e.g., alcohol and drug using peers), which may further contribute to drug and alcohol cravings and subsequent use (Stahler et al., 2013).
Although there is empirical evidence linking PTSD to cravings and alcohol and drug use, as well as previous work on daily stressors and tobacco use (e.g., Cohn et al., 2014; Shiffman 2005; Volz et al., 2014), there have been notably fewer studies on the effect of naturally-occurring daily stressors on daily alcohol and/or drug cravings and use (e.g., Preston & Epstein, 2011) and none in justice-involved individuals (Serre, Fatseas, Swendsen, & Auriacombe, 2015 for a review). Research on the associations between stressors and alcohol and drug cravings has typically relied on laboratory-based paradigms or retrospective reconstruction of individuals’ day-to-day lives (Ouimette, Read, Wade, & Tirone, 2010; but see Simpson, Stappenback et al., 2012 for an exception). Although laboratory-based paradigm studies have helped to establish factors that do and do not elicit craving, they are limited in their ecological validity. Studies that rely on retrospective reports are similarly limited due to potential biases and recall errors (Almeida, 2005; McKay, 1999). Thus, neither approach can capture the cyclical process of craving and alcohol and drug use as they unfold over time (Simpson, Stappenback et al., 2012). Consequently, researchers have increasingly used daily monitoring to examine predictors of cravings and use (Armeli, Conner, Cullum, & Tennen, 2010; Serre et al., 2015; Shiffman et al., 2007). These studies show that cravings and use are not just stable individual difference variables, but also have important dynamic aspects. A clear advantage of daily diary assessments is that they allow for close to real-time examination of temporally ordered event-level relationships and are less subject to recall errors than traditional retrospective methods (Almeida, 2005; Galloway, Didier, Garrison, & Mendelson, 2009).
The Present Study
In a sample of offenders referred to community-based treatment for alcohol and drug use problems, the present study extends previous work by using a daily experience paradigm to examine within-person coupling between daily stressors and daily alcohol and drug cravings and use over time to establish temporal links between these constructs (see Shiffman & Stone, 1998; Tennen, Suls, & Affleck, 1991). We hypothesized positive associations between concurrent (i.e., same day) daily stressors and cravings and use; on days with increases in daily stressors we expected concurrent increases in cravings and use on that same day (Hypothesis 1). We examined the role of daily stressors as antecedents and consequences of cravings and use with lagged models and hypothesized that increases in previous day stressors would be associated with an increase in cravings and use the next day (Hypothesis 2a). We also hypothesized that increases in previous day cravings and use would be associated with an increase in stressors the next day (Hypothesis 2b).
In addition to dynamic, contextual factors such as daily stressors, we sought to examine the individual contextual factor of prior treatment. Recovery in the context of substance abuse treatment depends on many factors, especially among justice-involved individuals for whom participation is typically not voluntary (Chandler, Fletcher, & Volkow, 2009). Intrinsic factors, such as low motivation and engagement in treatment (Simpson et al., 2012), and extrinsic factors, such as negative peer contagion (Stahler et al., 2013), along with severity of disorder (McCarty et al., 2014), can decrease the likelihood of successful recovery. Serial attempts at recovery (e.g., multiple treatment episodes) may indicate greater treatment need associated with increased severity of disorder as well as the presence of multiple, intrinsic and extrinsic risk factors. Thus, we hypothesized that the within-person relationships between stressors and use/cravings would depend on individual differences in prior number of times in treatment (Hypothesis 3).
Method
Study Design
As a supplement to a national research project examining the impact of an organizational linkage intervention among agencies delivering Medication-Assisted Treatment (MAT) services (NIDA U01DA016190-S1; P.I.-K. Knight), the current study recruited male probationers and parolees enrolled in an outpatient alcohol and drug abuse treatment program in a large Midwest region of the United States. The data collection consisted of a baseline assessment and once daily surveys via telephone calls made to an interactive voice response system (IVR) over a 14-day period. The baseline assessment queried risk factors, such as personal and criminal history, alcohol and drug use disorder indicators, and prior substance abuse treatment episodes. The daily IVR surveys gathered daily alcohol and drug use, events, and stressors. Further details on the study participants, procedures, and measures are presented in the sections that follow and descriptive statistics for all study variables can be found in Table 1.
Table 1.
Descriptive Statistics of Study Variables
M/% | SD | Range | |
---|---|---|---|
Daily Cravings | |||
Alcohol | 0.34 | 0.88 | 0–4 |
Illegal drugs | 0.88 | 1.26 | 0–4 |
Daily Alcohol and Drug Use | |||
Alcohol (# of drinks) | 0.19 | 0.69 | 0–6 |
Illegal drugs to get high (1=yes) | 23% | 0–1 | |
Daily stressors (Total number) | 2.53 | 2.44 | 0–8 |
Number of prior drug treatments | 3.88 | 4.73 | 0–30 |
Covariates | |||
Ethnicity (1 = Black) | 78% | 0–1 | |
Lifetime arrests | 18.29 | 19.83 | 1–150 |
Age at first arrest | 16.86 | 3.53 | 9–32 |
Age | 35.65 | 9.41 | 20–66 |
Legal status (1=parole) | 23% | 0–1 | |
Living with spouse (1=yes) | 26% | 0–1 | |
Employment (1=yes) | 26% | 0–1 | |
Education (1=HS/GED) | 67% | 0–1 | |
Alcohol use (prior 30 days) | 1.40 | 1.72 | 0–6 |
Drug use (prior 30 days) | 1.98 | 1.21 | 0–3 |
Number of IVR days completed | 7.35 | 4.58 | 1–14 |
Participants
The sample consisted of 117 offenders (100% men) participating in a community-based alcohol and drug abuse treatment program (76% probationers, 24% parolees). Approximately 78% were African American/Black, 19% were Caucasian/White, and 3% “other” race/ethnicity, which is representative of the population demographics for that region (U. S. Census, 2012). All were screened for alcohol and drug dependency and were referred to MAT services for alcohol or opioid dependency. 81% of the sample reported heroin or other opiate as the substance that caused the most problem, 11% reported alcohol, and 13% reported other. Most participants reported that they had received treatment for alcohol and drug use problems prior to the current episode (88%).
Procedures
Potential study participants were recruited at the time of their referral to MAT. The referral to MAT was made by his substance abuse counselor, in consultation with the offender’s probation or parole officer; this was not a RCT with random assignment to treatment conditions. The treatment agency provided a list of referred probation and parolees to the research team, and research staff contacted the clients apart from group or individual counseling meetings. Informed consent was obtained in the manner approved by the Institutional Review Board (IRB) of both Texas Christian University (TCU) and Missouri Department of Mental Health (MDMH). Following written informed consent, in-person baseline interviews were completed prior to the scheduled appointment with the MAT service provider (typically within seven days). This baseline interview lasted approximately an hour and included responses to a set of questionnaires, followed by training on how to make a call to the IVR survey line.
After this short IVR training, participants called the toll-free phone number to access the IVR system and completed their first call-in IVR survey with the help of the study staff. The IVR system used recorded voice prompts to ask questions and participants answered the questions by pressing numbers on the telephone keypad. Participants were provided with a pocket reference card that included the toll-free number to call each day and several of the response stems to assist when making the daily call. On the card, the research staff wrote the client’s participant identification number (used to ensure confidentiality of responses when they called the IVR system) and the dates to begin and end calls to the IVR survey line. If the participant had a personal cell phone, the research staff assisted him in programming the number into his phone’s contact list. The ability to understand the training and complete the first IVR call counted as inclusion criteria. Each IVR call lasted approximately 5 minutes. 13% of the calls were made to the survey line before 8 AM, 55% of the calls were made during the standard workday (i.e., 8AM – 5PM), and 32% of the calls were made in the evening hours from 5PM to midnight. The peak calling days were Tuesday and Wednesday (this corresponded to the days that were scheduled for most baseline surveys). Participants were instructed to answer the daily IVR questions using the timeframe of “since this time yesterday”. After the 14 days of daily calls, treatment continued as usual. (Further details are available on the treatment provider and services in Desmarais et al., 2016 and Gray et al., 2015)
Participants received gift cards for attending each research interview. These incentives were tied to completion milestones: baseline ($10), week 1 IVR ($10), week 2 IVR ($10). A total of $30 in gift cards (per participant) was possible for these phases of the study.
Measures
Daily Measures
Daily cravings were assessed one time each day, with separate items querying cravings for alcohol and illegal drugs (i.e., “How strong is your craving to [drink alcohol, use illegal drugs]?). Responses ranged from 0 (not at all strong) to 4 (extremely strong). Daily scores were used in the current analyses.
Daily alcohol and drug use were assessed one time each day, with separate items querying use of alcohol and illegal drugs. Participants were asked how many drinks of alcohol they had in the past 24 hours, with a reminder that “a standard drink is 12 ounces of beer or 1 bottle, 5 ounces of wine, or 1 ½ ounces of liquor (or 1 shot)”. Use of illegal drugs “to get high” was based on a dichotomous report (0 = no, 1 = yes). Daily scores were used in the current analyses (drinks consumed on 10% of the days; illegal drugs 23% of the days).
Daily stressors were assessed one time each day across eight domains. Participants were asked if they had stress about: an argument with someone (16.84% of the days); work or unemployment (41.87% of the days); money problems (52.83% of the days); their health (19.03% of the days); probation or parole (42.91% of the days); other legal issues (24.11% of the days); where they were living (19.26% of the days); or transportation (37.83% of the days) (0 = no, 1 = yes). The total number of stressors was calculated for each day for each participant by summing the affirmative responses (1 or more stressors were experienced on 66% of the days).
Baseline Measures
The baseline assessment was based in part on the TCU Criminal Justice Comprehensive Intake, first used in 1968 in a national evaluation of community-based systems for treating heroin addiction and more recently in three national treatment effectiveness studies funded by NIDA (see Simpson, Joe, Knight, Rowan-Szal, & Gray, 2012; Simpson & Knight, 2007). This assessment interview covered personal and family background, friends, criminal and legal involvements, health and psychological status, and drug use and drug treatment history.
Number of prior drug treatment episodes was measured with a single item during the intake interview. Covariates included ethnicity (Black = 1, other = 0; coded this way because Black/African-American participants represented the majority of cases), age, age at first arrest, number of lifetime arrests, legal status (probation = 0, parole = 1), living with spouse (0 = no, 1 = yes), employment (0 = no, 1 = yes), education (High School or GED = 1, other = 0), frequency of alcohol use in the prior 30 days, and frequency of drug use in the prior 30 days. Participants self-reported past 30-day frequency of using alcohol and each of 19 drugs excluding tobacco (i.e., heroin, heroin and cocaine combined, other street opiates, and prescription medications not prescribed by a doctor) on a 4-point scale (1 = none / not at all, 2 = occasional, 3 = weekly, or 4 = daily). Responses then were coded to capture the highest reported frequency of use to create the alcohol use and drug use variables.
Data Cleaning and Analyses
All baseline interviews were electronically scanned and verified by project staff to ensure accuracy. A two-stage data validity check was used for the IVR dataset. At the first step, each case was required to have a valid study code. Based on this criterion 103 calls were dropped. Many of these calls were more than 95% incomplete and assumed to be either wrong numbers or they were known to be tests/demonstrations of the system by the research staff. At the second step, incomplete cases were checked to see if the survey was restarted by calling back (e.g., cases of a cell phone problem such as low battery or an accidental end to the call). Based on this criterion, 34 calls were dropped because the client called back and completed a subsequent call (within minutes of the incomplete). This resulted in the current analytical sample of 117 MAT-referred participants who collectively made 860 (out of 1638 possible) calls to the IVR survey line. We conducted analyses to see if the compliance rates were associated with any study variables. Those who were more compliant reported lower craving for illegal drugs, r(114) = −.31, p = .0007 and lower use of illegal drugs, r(114) = −.31, p = .0006. Older participants were also more compliant than younger participants, r(114) = .34, p = .0002. No other correlations were significant.
To maximize the IVR data that were gathered through a daily diary design, multilevel modeling (MLM) was used. MLM is frequently used to model intraindividual variability (e.g., Grzywacz, Almeida, Neupert, & Ettner, 2004). This technique was especially useful because we sought to examine intraindividual variability in daily stressors and alcohol and drug use/craving and estimates are adjusted for the amount of data available from each participant. In this framework, individual change or variability is represented by a two-level hierarchical model (Raudenbush & Bryk, 2002). At Level 1, each person’s variability is expressed as an individual regression equation that depends on a set of parameters (intercept and slope). These individual parameters become the outcome variables in a Level 2 model, where they may depend on some person-level characteristics.
The following model was used to examine the within-person covariation (Hypothesis 1) between the number of stressors and alcohol use (number of drinks) and whether the association depended upon the number of prior treatment episodes (Hypothesis 3):
Level 1: ALCOHOLit = β0it + β1it(NUMBER OF STRESSORS) + rit
- Level 2: β0i = γ00 + γ001(ETHNICITY) + γ002(AGE) + γ003(AGE AT FIRST ARREST) + γ004(# OF LIFETIME ARRESTS) + γ005(LEGAL STATUS) + γ006(LIVING WITH SPOUSE) + γ007(EMPLOYMENT) + γ008(EDUCATION) + γ009(FREQ. OF ALCOHOL USE AT INTAKE) + γ010(FREQ. OF DRUG USE AT INTAKE) + γ011(# OF PRIOR TREATMENT EPISODES) + u0i
In Level 1, alcohol for person i on day t is a function of the intercept, β0it, which is defined as the number of drinks for person i on stressor-free days (i.e., STRESSORS = 0) adjusted for all covariates. β1it is the expected change or shift in drinks associated with the occurrence of stressors. The error term, rit, represents a unique effect associated with person i (i.e., individual fluctuation around the mean). In the Level 2 equations, γ00 is the mean number of drinks for the sample on stressor-free days (i.e., STRESSORS = 0) adjusted for all covariates (γ001 through γ011), and γ10 is the average change in drinks associated with changes in stressors. The cross-level interaction is represented by γ11 and tests whether the within-person relationship between stressors and alcohol depends on individual differences in the number of prior treatment episodes. We included random effects to assess the degree to which people vary from the sample mean of drinks (u0i), and the degree to which people vary from the slope (u1i).
Hypothesis 1 was addressed through separate multilevel models for each dependent variable with daily stressors as the occasion-level predictor. Hypothesis 2 was examined using lagged effects models where the previous occasion of stressors and the dependent variable was used to predict the current occasion of the given dependent variable (e.g., number of drinks). By controlling for prior-day values for the dependent variable when predicting the current day values, the specification is equivalent to (but more flexible than) a change score model (Grzywacz et al., 2004; Raudenbush & Bryk, 2002). Hypothesis 3 was examined through the cross-level interaction of Daily Stressors X Number of Prior Treatment Episodes and then Daily Use/Craving X Number of Previous Treatment Episodes in the lagged models predicting daily stressors. Models with continuous dependent variables were analyzed using SAS Proc Mixed. In models where the dependent variable was dichotomous (i.e., use of illegal drugs), we implemented a logistic MLM using the GLIMMIX macro in SAS. Given that we tested nine models, we used a stringent alpha of .006 (.05/9).
Results
Results from a fully unconditional model (no predictors included in the model) for each continuous dependent variable was conducted to obtain estimates of intraindividual (σ2) and between-person (τ00) variability. The intraindividual variability for each model was significant (all ps <.001), indicating significant day-to-day fluctuation in each variable: 54% of the variability in alcohol craving, 30% of the variability in craving to use illegal drugs, and 69% of the variability in the number of alcoholic drinks were within people. These results suggest that there is a significant amount of fluctuation around each person’s own mean in terms of cravings and use and that cravings and use are not stable, between-person differences. We conducted additional models to examine the potential overlap of cravings and use, given our goals to treat them as separate outcomes. As would be expected, increases in cravings for both alcohol (γ10 = .19, t = 6.73, p <.0001) and illegal drugs (γ10 = .96, t = 10.63, p <.0001) were associated with increases in use. However, cravings only explained 5% of the within-person variance in alcohol use and only 6% of the within-person variance for illegal drugs. Thus, the amount of overlap between cravings and use was quite low and suggests treating them as separate outcomes is justified.
Hypothesis 1
Results from models used to test the concurrent (same day) within-person relationship between daily stressors and alcohol and drug use/craving can be found in Tables 2 and 3. Number of daily stressors was not associated with the craving (γ10, Table 1, Model 1) or use (γ10, Table 2, Model 3) of alcoholic drinks. However, increases in daily stressors were associated with increases in cravings to use illegal drugs (γ10, Table 3, Model 1). Daily stressors were also associated with an increase in the likelihood of using illegal drugs (γ10, Table 3, Model 3).
Table 2.
Unstandardized Estimates (Standard Error) from Concurrent and Lagged Multilevel Models of Daily Stressors and Alcohol Craving and Use
Fixed Effects | Model 1 | Model 2 | Model 3 | Model 4 |
---|---|---|---|---|
Craving Intensity | Use | |||
Alcohol craving/use, β0 | ||||
Intercept, γ00 | 0.13 (.46) | 0.16 (.47) | 0.26 (.32) | 0.17 (.39) |
Ethnicity, γ001 | −0.06(.17) | 0.10(.17) | 0.01(.12) | 0.01 (.14) |
Age, γ002 | 0.00(.01) | −0.00(.01) | 0.01(.01) | 0.01 (.01) |
Age at first arrest, γ003 | 0.01(.01) | 0.01 (.02) | −0.01(.01) | −0.00 (.02) |
# of lifetime arrests, γ004 | −0.00(.00) | −0.00 (.00) | −0.00 (.00) | −0.00 (.00) |
Legal status, γ005 | 0.13 (.16) | 0.09 (.16) | −0.08 (.11) | −0.10 (.13) |
Living with spouse, γ006 | 0.05 (.15) | 0.14 (.15) | −0.11 (.10) | −0.15 (.12) |
Employment, γ007 | −0.12 (.15) | −0.08 (.15) | −0.07 (.10) | −0.43 (.12) |
Education, γ008 | −0.22 (.14) | −0.21 (.14) | −0.08 (.09) | −0.06 (.11) |
Freq. of alcohol use, γ009 | 0.10 (.04) | 0.06 (.04) | −0.07 (.03) | 0.06 (.03) |
Freq. of drug use, γ010 | −0.01 (.06) | −0.00(.06) | −0.04 (.04) | −0.03 (.05) |
# of prior Tx episodes, γ011 | 0.00 (.02) | 0.02(.02) | −0.02 (.01) | 0.01 (.01) |
Slope, β1 | ||||
# of Stressors, γ10 | 0.04(.02) | 0.02(.02) | ||
# of prior Tx episodes, γ11 | −0.00(.00) | 0.004(.002) | ||
Slope, β1 | ||||
Previous Day’s | ||||
# of Stressors, γ10 | 0.05 (.02) | 0.02 (.02) | ||
# of prior Tx episodes, γ11 | −0.01* (.00) | −0.00 (.00) | ||
Slope, β2 | ||||
Previous day’s alcohol | −0.14* (.04) | 0.05 (.04) | ||
craving/use, γ20 | ||||
| ||||
Random Effects | ||||
| ||||
Alcohol craving/use (τ00) | 0.34* (.06) | 0.29* (.07) | 0.14* (.03) | 0.19* (.04) |
Within-person | 0.43* (.02) | 0.40* (.02) | 0.32* (.02) | 0.31* (.02) |
fluctuation (σ2) | ||||
R2 between-person | 5% | 20% | 10% | 0% |
R2 within-person | 0% | 7% | 1% | 5% |
Notes. Tx = treatment.
p <.006.
Dependent variables were alcohol craving intensity (Models 1 and 2) and alcohol use (Models 3 and 4)
Table 3.
Concurrent and Lagged Multilevel Models of Daily Stressors and Illegal Drug Craving and Use
Fixed Effects | Model 1 | Model 2 | Model 3 | Model 4 |
---|---|---|---|---|
Craving Intensity | Use | |||
Drug craving/use, β0 | ||||
Intercept, γ00 | 0.42 (.70) | 1.13 (.72) | 0.09(.00, 4.74) | 0.80(.01, 105.65) |
Ethnicity, γ001 | −0.22(.26) | 0.07(.26) | 1.10(.26, 4.69) | 1.07(.20, 5.80) |
Age, γ002 | −0.01(.01) | −0.01(.01) | 0.95 (.89, 1.02) | 0.94 (.86, 1.03) |
Age at first arrest, γ003 | 0.01(.03) | −0.01 (.03) | 1.00(.85, 1.19) | 0.95(.77, 1.17) |
# of lifetime arrests, γ004 | 0.00(.01) | 0.00 (.01) | 1.01(99, 1.04) | 1.01(.98, 1.05) |
Legal status, γ005 | 0.10 (.24) | 0.13 (.24) | 2.67(.76, 9.38) | 2.77(.60, 12.82) |
Living with spouse, γ006 | 0.13 (.22) | 0.04 (.23) | 1.47(.43, 4.97) | 1.84(.42, 8.16) |
Employment, γ007 | 0.12 (.22) | 0.11 (.22) | 1.05(31, 3.58) | 1.19(28, 5.13) |
Education, γ008 | −0.08 (.21) | −0.11 (.22) | 0.70(.22, 2.25) | 0.70 (.17, 2.92) |
Freq. of alcohol use, γ009 | 0.02 (.06) | −0.01 (.06) | 1.15(81, 1.63) | 0.95(.62, 1.44) |
Freq. of drug use, γ010 | 0.25* (.08) | 0.18 (.09) | 2.15* (1.28, 3.60) | 1.75(.95, 3.20) |
# of prior Tx episodes, γ011 | 0.04 (.02) | 0.02(.02) | 1.06(.93, 1.20) | 1.02(.88, 1.17) |
# IVR days completed, γ012 | −.05 (.02) | −.07 (.03) | 0.82(.72, .93) | 0.83(.70, 1.00) |
Slope, β1 | ||||
# of Stressors, γ10 | 0.17* (.03) | 1.66* (1.42, 1.94) | ||
# of prior Tx episodes, γ11 | −0.01* (.00) | 0.98* (.97, .99) | ||
Slope, β1 | ||||
Previous day’s | ||||
# of stressors, γ10 | 0.03 (.03) | 1.25(1.06, 1.49) | ||
# of prior Tx episodes, γ11 | −0.00 (.00) | 1.02(.88, 1.17) | ||
Slope, β2 | ||||
Previous Day’s Drug | 0.17* (.04) | 2.04* (1.32, 3.17) | ||
Craving/Use, γ20 | ||||
| ||||
Random Effects | ||||
| ||||
Drug craving/use (τ00) | 0.88* (.15) | 0.76* (.17) | ||
Within-person | 0.51* (.03) | 0.52* (.03) | ||
fluctuation (σ2) | ||||
R2 between-person | 27% | 37% | Not applicable (Guo & Zhao, 2000) | |
R2 within-person | 4% | 2% | Not applicable (Guo & Zhao, 2000) |
Notes. Tx = treatment.
p <.006.
Dependent variables were drug craving intensity (Models 1 and 2) and drug use (Models 3 and 4). Models 1 and 2 report unstandardized estimates (and standard errors). Models 3 and 4 report odds ratios (lower confidence interval, upper confidence interval) because the drug use variable was dichotomous.
Hypothesis 2
Results from models used to test the lagged relationship between previous-day stressors and next-day outcomes can be found in Tables 2 and 3. Previous-day stressors were not associated with an increase in next-day cravings for alcohol (γ10, Table 2, Model 2) or for illegal drugs (γ10, Table 3, Model 2). Previous-day stressors were not associated with the number of alcoholic drinks the next day (γ10, Table 2, Model 4) or the increase in the likelihood of next-day illegal drug use (γ10, Table 3, Model 4).
We also tested a single model where all of the previous-day alcohol and drug use/craving variables predicted next-day stressors. No significant effects emerged (all ps > .006).
Hypothesis 3
Tables 2 and 3 also displays the results for the cross-level interactions (γ11, Models 1 and 3) testing whether between-person differences in the number of prior treatment episodes moderated the within-person relationships between daily stressors and use/cravings. For concurrent models, daily stressors interacted with the number of prior treatment episodes when predicting cravings for illegal drugs, such that people who had low (M − 1SD) prior treatment experienced a steeper increase in their cravings for illegal drugs with increasing stressors compared to those with high (M + 1SD) prior treatment experience (see Figure 1a). The interaction between daily stressors and prior treatment on illegal drug use suggest that those with low prior treatment experienced a steeper increase in the likelihood of illegal drug use with increasing stressors compared to those with high prior treatment experience (see Figure 1b). There were no other significant interactions for the concurrent day models.
Figure 1.
a. Cross-level interaction of number of Daily Stressors X Prior Treatment predicting daily illegal drug craving intensity. Predicted points were plotted by using the minimum (0) and maximum (8) values of daily stressors (because we modeled a linear effect) and low (M − 1SD) and high (M + 1SD) values of prior treatment experience.
b. Cross-level interaction of number of Daily Stressors X Prior Treatment predicting daily illegal drug use. Predicted points were plotted by using the minimum (0) and maximum (8) values of daily stressors (because we modeled a linear effect) and low (M − 1SD) and high (M + 1SD) values of prior treatment experience for the raw estimates. Results were transformed into likelihood scores by using the EXP function in Excel which returns e raised to the power of a number (the constant e equals 2.72, the base of the natural logarithm).
Tables 2 and 3 also display the results for the cross-level interactions for the lagged models (γ11, Models 2 and 4). When predicting next-day alcohol craving, there was a significant interaction of previous-day stressors and prior treatment episodes; those with low prior treatment experienced an increase in alcohol craving with increases in stressors but those with high prior treatment experienced a decrease in alcohol craving with increases in stressors (see Figure 2a). There were no other significant interactions for the lagged models predicting use or craving.
Figure 2.
a. Cross-level interaction of number of Previous-Day Daily Stressors X Prior Treatment predicting next-day alcohol craving. Predicted points were plotted by using the minimum (0) and maximum (8) values of daily stressors (because we modeled a linear effect) and low (M − 1SD) and high (M + 1SD) values of prior treatment experience.
b. Cross-level interaction of Previous-Day Drug Use X Prior Treatment predicting the number of next-day stressors, controlling for the interactions of Previous-Day Alcohol Use X Prior Treatment and Previous-Day Alcohol Craving X Prior Treatment. Predicted points were plotted by using low (M − 1SD) and high (M + 1SD) values of drug use and prior treatment.
When predicting next-day stressors, we conducted a single model with each of the previous-day craving and use variables as predictors in order to reduce the number of total models. A significant interactions emerged for prior treatment experience by illegal drug use (γ31 = 0.10, t = 3.27, p = .001); those with higher prior treatment experience reported an increase in next-day stressors when they experienced an increase in previous-day drug use, whereas those with low prior treatment experience reported a decrease in next-day stressors when they experienced an increase in previous-day drug use (see Figure 2b).
Discussion
The goal of the current study was to examine the role of daily stressors as antecedents, correlates, and consequences of daily alcohol and drug use/cravings among community-based offenders with alcohol and drug use problems. We additionally explored whether the relationships would vary by prior treatment experience. Overall, findings show that daily stressors—such as interpersonal arguments, financial-related stressors, etc.—can act as antecedents, correlates, and consequences of daily alcohol and drug craving and use. These results extend previous research, in justice-involved individuals as well as non-justice-involved populations more broadly, that has primarily focused on PTSD symptoms, rather than daily stressors, as they relate to alcohol and drug cravings and use (Serre et al., 2015).
Summary of Findings
Results from the fully unconditional models for each of the alcohol and drug craving and use variables suggest that they are dynamic within-person processes that can change on a daily basis. Though the importance of examining alcohol and drug cravings and use daily to capture such change has been well-established, to our knowledge this is the first study of variables related to alcohol and drug use to employ a prospective daily diary design in a sample of offenders. Analyses revealed significant day-to-day fluctuations in cravings and use, even though offenders were participating in community-based MAT for alcohol and drug use problems and subject to the conditions of their parole or probation orders. These findings are consistent with those of prior research in non-offender samples (e.g., Preston & Epstein, 2011) and support a harm reduction, rather than abstinence only, approach to alcohol and drug treatment.
Individuals involved with the criminal justice system who have alcohol and drug use problems reoffend at much higher rates than their non-using counterparts, with alcohol and drug use serving as an important vector to recidivism (Dowden & Brown, 2002). As such, there is a need to identify factors that trigger relapse among justice-involved individuals undergoing community-based substance abuse treatment. The results from the within-person coupling models suggest that daily stressors may not have a concurrent relationship with alcohol craving or use, but that they are associated with increases in craving and use of illegal drugs. These findings have important implications for research and practice. First, findings underscore the importance of investigating alcohol use and drug use separately, rather than considering them together under a broader measure of “substance use”. Indeed, across analyses, associations among daily stressors, cravings, and use differed in meaningful ways. Such differences have similarly been found in other studies of justice-involved individuals examining associations of alcohol and drug use with other correlates and outcomes, such as treatment adherence (e.g., Desmarais et al., 2016), violence (Johnson et al., 2016), and recidivism (e.g., Dowden & Brown, 2002).
In the concurrent models, alcohol craving and use were not associated with daily stressors, but increases in daily stressors were associated with increases in illegal drug craving and use. This pattern of results for illegal drug craving and use is consistent with both the self-medication theory (Khantzian, 2003) and stress-response dampening (SRD) theory, which posit that drugs may be used to mitigate negative reactions to trauma (Armeli et al., 2003), extending their application from traumatic life events (Cohn et al., 2014) to daily stressors. Moreover, the process of drug seeking itself may increase exposure to triggers and stressful or risky environments, thereby increasing cravings. When focusing exclusively on within-person effects, no significant associations were found for the lagged models predicting next-day cravings, use, or stressors.
Based on these within-person findings, one might assume that daily stressors only act as correlates but not antecedents or consequences of craving and use. However, Hypotheses 1 and 2 focused on within-person relationships, but Hypothesis 3 integrated individual differences in prior treatment experience in the within-person relationships. For the concurrent models, offenders with fewer prior treatment experiences appeared to be more reactive to stressors when examining craving for and use of illegal drugs than those with more prior treatment experiences; that is, there was a steeper increase in craving and use for those with fewer prior treatment experiences. Further, interactions within the lagged models suggested that the temporal ordering of the within-person relationships also depends on individual differences in prior treatment experience. Offenders with fewer prior treatment experiences were more reactive to previous-day stressors with respect to their alcohol craving compared to those with more prior treatment experience. Taken together, these findings are consistent with evidence that points toward a mostly positive, cumulative effect of treatment (Hser et al., 1998; Hubbard, Craddock & Anderson, 2003; Longshore & Hsieh, 1998).
Offenders with more prior treatment experience, however, were not always at an advantage; in the case of daily drug use, those with more prior treatment experience actually experienced a steeper increase in the number of next-day stressors when drug use increased compared to those with less prior treatment experience. This finding suggests that prior treatment experience may represent a proxy for treatment need and problematic use. That is, those justice-involved individuals with more severe and chronic substance use problems that are more difficult to treat will require more attempts at recovery to be successful (McCarty et al., 2014; Tucker & Simpson, 2011). However, multiple treatment encounters also have been shown to have a potentially cumulative effect and to be related to sustained reductions in alcohol and drug use (Griffin et al., 2014). Overall, findings underscore the importance of tailoring treatment to reflect individual differences, including prior treatment experience. Moreover, findings suggest that efforts targeting daily stress reduction and focused on addressing subsequent cravings may have the greatest potential for effectively treating alcohol and drug use problems among justice-involved adults.
Limitations and Future Directions
The findings of the current study are not without some limitations. For instance, we relied on self-report data. Though self-report may be susceptible to recall bias and errors as well as social desirability effects, it is a valid and reliable method for collecting data on substance use (Darke, 1998). Nonetheless, conclusions would be strengthened through incorporation of data from biological tests (e.g., urine analysis), clinical assessments, and/or collateral informants. Furthermore, although the compliance rate (860/1638; 7.35 days per person, on average) is in line with previous research in similar samples (e.g., cocaine users undergoing outpatient treatment, Lindsay, Minard, Hudson, Green, & Schmitz, 2014), future research with more consecutive study days would be able to track the temporal ordering of these daily relationships over a longer period of time. Further, researchers could employ strategies, such as prize-based incentives, to promote daily IVR compliance (Lindsay et al., 2014). Additionally, all participants were undergoing MAT and the naturalistic design of the study did not allow for randomized assignment to treatment conditions; as such, we cannot speak to the effects of MAT on daily stressors, alcohol and drug use, and cravings. This remains a critical avenue of future research. The role of daily stressors separately for alcohol and drug users is another important avenue of future research, as the qualitative and quantitative nature of the stressors may be differentially important for substance craving and use. Indeed, the MAT itself may have functioned like a stressor for some participants, which would be interesting to examine in future work. We had an all-male sample of community-based offenders in one jurisdiction; generalizability of findings to more diverse samples of justice-involved individuals, notably women and those in other jurisdictions will need to be tested in future research. Given that women report stronger reactions to alcohol and drugs than men (Miller, Weafer, & Filmore, 2009) and our participants were under criminal justice supervision (i.e., parole or probation), future research with men and women who are not under supervision or subject to criminal justice conditions may yield stronger effects. Examining additional individual difference characteristics (e.g., ethnicity, education) as further moderators of the daily context by prior treatment interactions will be another important future direction.
Conclusion
Continued efforts are needed to isolate factors that predict return to alcohol and drug use among justice-involved individuals and to implement evidence-based treatments that interrupt these mechanisms of relapse. We used an innovative data collection strategy, IVR, with a relatively large sample of community-based offenders with alcohol and drug use problems to capitalize on the strengths of a prospective daily diary design. Our results suggest that daily stressors can act as antecedents, correlates, and consequences of daily alcohol and drug craving and use, and as such, represent a key target for intervention. Consequently, efforts to promote adaptive coping with daily stressors, such as anticipatory coping (Neupert, Ennis, Ramsey, & Gall, 2016), have the potential to contribute to sustainable recovery among justice-involved individuals with alcohol and drug use problems. Modifying life conditions is a critical part of successful treatment (Ekhtiari, Nasseri, Yavari, Mokri, & Monterosso, 2016) and interventions that can reduce stress such as mindfulness training may increase positive emotion, improve attention, self-control, self-regulation, and metacognitive awareness which could normalize negative reinforcement processes in substance-using individuals (e.g., stress-induced cravings) (Ekhtiari et al.). Mobile technologies that can provide personalized, real-time interventions may be valuable (Fatseas et al., 2015), especially considering the important individual differences seen with prior treatment experience. Indeed, Lindsay et al. (2014) found a positive association between number of IVR calls and abstinence achievement. However, further research is needed to test the effectiveness of these interventions in preventing relapse—and ultimately, reducing recidivism—in this population (Chandler, Peters, Field, & Juliano-Bult, 2004).
Acknowledgments
This study was funded by a grant to Texas Christian University from the National Institute on Drug Abuse, the National Institutes of Health (DA016190, Kevin Knight, principal investigator), with support from the Center for Substance Abuse Treatment of the Substance Abuse and Mental Health Services Administration, the Centers for Disease Control and Prevention, and the National Institute on Alcohol Abuse and Alcoholism (all part of the U.S. Department of Health and Human Services), and from the Bureau of Justice Assistance of the U.S. Department of Justice. These findings have not been presented or published elsewhere.
Contributor Information
Shevaun D. Neupert, Department of Psychology, North Carolina State University
Sarah L. Desmarais, Department of Psychology; North Carolina State University
Julie S. Gray, Institutional Effectiveness and Reporting, The University of Texas at Arlington
Amy M. Cohn, Schroeder Institute for Tobacco and Policy Studies, Truth Initiative and Department of Oncology, Georgetown University Medical Center
Stephen Doherty, St. Louis, Missouri, Gateway Foundation Corrections.
Kevin Knight, Institute of Behavioral Research, Texas Christian University.
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