unlocking the black box

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Unlocking the Black Box: Indicators of Treatment

Noncompliance in a Sample of Repeat DWI Offenders

Journal: Journal of Substance Use

Manuscript ID: TJSU-2015-0007.R2

Manuscript Type: Original article

Keywords: Treatment, Motivation, Rehabilitation, Drinking (Drinkers)

URL: http:/mc.manuscriptcentral.com/tjsu Email: [email protected]

Journal Of Substance Use

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ABSTRACT

Although a plethora of research has been conducted on the profiles of substance abusers

and the efficacy of various drug treatment programs in lowering post-treatment recidivism, there

has been a dearth of studies on the treatment progress itself, the “black-box” in drug/DWI

treatment research. This study examines the indicators of treatment noncompliance among a

sample of DWI Court Program participants of one county in a Southern state. Results of

regression indicate that clients with delinquent peers were less likely to comply with treatment

conditions. Results indicate that the odds of a client with criminal acquaintances to be non-

compliant were 4.8 times greater than for a client with no criminal acquaintances (p<.05). A

greater count of sanctions and incentives received also increased the odds of being non-

compliant. The odds of a client being noncompliant were 1.5 times greater when the count of

incentives increased by one unit. Similarly, the odds of a client being noncompliant were 2.2

times greater when the count of sanctions increased by one unit. Results indicate that the quality

of incentives and sanctions rather than the number or rate granted to clients may be more

predictive of treatment compliance.

KEYWORDS: drug and alcohol use; DWI courts; treatment compliance; program evaluation

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Unlocking the Black Box: Indicators of Treatment Noncompliance

in a Sample of Repeat DWI Offenders

INTRODUCTION

Current research on drinking-while-intoxicated (DWI) and drug courts focus primarily on

post-treatment recidivism (Bouffard, Richardson, & Franklin, 2010; Fielding, Tye, Ogawa,

Imam, & Long, 2002; Whitlock & Lubin, 1998). The over-emphasis on outcome evaluation

research for substance abuse treatment has led some scholars to stress the need for more

treatment process studies (McLellan, Woody, Metzger, McKay, Durell, Alterman, & O’Brien,

1997).

Treatment Engagement and Noncompliance

Treatment process studies focus on factors affecting retention (Dakof, Tejeda, & Liddle,

2001; Rempel & DeStefano, 2001). Recent research findings suggest, however, that treatment

engagement is necessary for successful program completion (Joe, Broome, Rowan-Szal, &

Simpson, 2002; Marrero, Robles, Colon, Reyes, Matos, Sahai, Calderon, & Shepard, 2005; Sung,

Belenko, & Feng, 2001; Sung, Belenko, Feng, & Tabachnick, 2004). Treatment engagement is a

broader term than treatment retention, encompassing several aspects of the treatment process,

including attendance, compliance with program conditions, and active participation in mandatory

meetings (Sung et al., 2001).

The Texas Christian University (TCU) Treatment Process Model

Simpson’s (2004) treatment process model describes a sequential phase for treatment,

including factors that directly impact compliance (Simpson & Knight, 2004). According to the

TCU treatment model, an effective treatment process requires the interplay of various

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components, starting with early engagement. Early engagement is influenced by social and

psychological motivation at intake (De Leon, 2000; Lang & Belenko, 2000) and social support

networks (Slaght, 1999), including family and friends (De Civita, Dobkin, & Robertson, 2000;

Miller, 2003; Shapiro, 1998).

Studies on early engagement examine factors affecting program compliance (McKellar,

Kelly, Harris, Moos, 2006; Sung et al., 2004). Peck, Arstein-Kerslake, and Helander (1994)

found that among a sample of driving under the influence offenders randomly assigned to

treatment programs between 1988 and 1981, noncompliant offenders tended to be younger, less

educated, with lower levels of income. Sung et al. (2004) found that educational attainment

increased the likelihood of treatment compliance. Sung et al. (2004), however, did not find any

significant effect of past employment on treatment engagement, contrary to prior research

(Anglin & Hser, 1990; Peters, Haas, & Murrin, 1999).

Other studies also examine the influence of various incentives such as vouchers (Griffith,

Rowan-Szal, Roark, & Simpson, 2000; Higgins, Alessi, & Dantona, 2002; Prendergast, Hall,

Roll, & Warda, 2008), small gifts, bus tokens, or car fare (Rowan-Szal, Joe, Chatham, &

Simpson, 1994), and drawing prizes contingent on negative urinalysis. Voucher-based

incentives have been found effective in increasing session attendance and drug abstinence in

various types of drug treatment settings (Petry, Martin, Cooney, & Kranzler, 2000). Other types

of behavioral interventions such as social recognition and reward for good behavior were also

effective for community based programs (Rowan-Szal, et al., 1994).

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RESEARCH QUESTION

This study analyzes factors that influence treatment noncompliance in a court program

for repeat DWI offenders based on the TCU Treatment Model. This is of interest to practitioners,

especially court program administrators and judges, because of the difficulty and uncertainty of

program success in various criminal justice programs. Also, since the DWI court program is a

pilot program implemented relatively recently in this particular county, program designers and

implementers would benefit from analysis of factors that influence success in the program.

Results of the study will hopefully lead to a more informed selection of qualified individuals

who are most likely to comply, as well as better monitoring of individuals who are at greater risk

of noncompliance.

The current study tests the following hypotheses:

Supportive Social Network

Social support systems affect the black box of treatment process, including early

engagement. Program clients with supportive spouses or partners were more likely to comply

with program conditions (Wright, Cullen, & Miller, 2001). This study hypothesizes that the

lesser the quality of client relationship with spouse or significant other, the greater the treatment

noncompliance.

Delinquent Peers

The quality of one’s peers and associations also influence early engagement or

noncompliance. According to Sung et al. (2004, p. 15), not all close friendships positively affect

treatment compliance and only those friends “who are not themselves engaged in illegal or

addictive behavior can provide support.” The study hypothesizes that clients who have

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delinquent peers are less likely to comply with program conditions and more likely to engage in

treatment noncompliance.

Conventional Social Involvement

Compliant clients often come from more socially stable groups, whereas poorly educated

or unemployed/low-income clients are more likely to engage in treatment noncompliance (Sung

et al., 2001). The study hypothesizes that clients who have less conventional social involvement

are more likely to engage in treatment noncompliance.

Treatment Motivation

Patient attributes at intake, such as motivation for change or readiness for treatment, and

problem severity at intake affect early engagement (Simpson, 2004). Behavioral interventions

such as use of sanctions and incentives have also been found to affect early engagement

(Simpson, 2004). Thus, the study hypothesizes that clients who have less treatment motivation

(measured by alcohol and drug problem at intake, use of incentives and sanctions) are more

likely to engage in treatment noncompliance.

METHODS

Sample and Data

The sample consists of 87 repeat DWI offenders who participated in a 24 months

criminal justice-mandated DWI court program in a large southern urban county. At the time of

this study, 164 individuals were enrolled in the program, at various stages. For purposes of this

study, only those participants who had fully completed the Level of Supervision Inventory –

Revised (LSI-R) intake assessment were considered (N=87). The LSI-R was not implemented

immediately by program administrators upon the creation of the program. This intake instrument

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was implemented more consistently during treatment of later cohorts. All program participants

upon whom the LSI-R was administered are included in this study.

The DWI Court Program under study was implemented in December 2007 pursuant to a

legislative mandate requiring the inclusion of second offense DWI offenders in either a county’s

already established drug court or a specifically developed treatment court. The program requires

clients to undergo assessment, attend court sessions regularly, perform community service, make

fee payments, serve jail time, wear the Secure Remote Alcohol Monitoring (SCRAM) device,

meet regularly with their community service officer and undergo periodic alcohol and drug

testing. Clients must also participate in alcohol treatment programs, 12-step programs, complete

a repeat offender class and attend victim impact panels.

Dependent Variable

Official court data contained several indicators of treatment noncompliance. An index of

noncompliance was computed from thirteen infractions types that were recorded by the courts.

This index of noncompliance included absence from court sessions, SCRAM violations,

unexcused absence from Alcoholics Anonymous meetings, unexcused absence from supervision

appointment, unexcused absence from treatment, breathalyzer violations, ignition interlock

violations, driving without a license, missed urinalysis, positive urinalysis, tampered or diluted

urinalysis, absconding, and new arrest.

Since the objective of the current study was to determine the factors predicting

noncompliance among DWI offenders, the count dependent variable was recoded into a

dichotomous variable (“0” = compliant group, and “1” = noncompliant group). The

noncompliant group had committed at least one infraction during their time in the program.

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Independent Variables

The independent and control variables were measured as follows:

Supportive social network. The family/marital scale of the LSI-R provides constructs

that can be used to measure the supportive social networks. This study used the scale measuring

satisfaction with intimate partner (q.23).

Conventional social involvement. The study examined two variables: current

employment status (full-time or part-time) and educational attainment (less than high school

degree, high school degree or GED, and college degree).

Delinquent peers. The delinquent peers’ hypothesis was measured using a dichotomous

variable from the LSI-R companions’ scale. This question (LSI-R q.33) asked the respondent

whether they had had criminal acquaintances during the 12 months preceding the assessment.

Treatment motivation. Treatment motivation was examined using three measures:

client level of satisfaction with current alcohol problem (LSI-R, q.39), recoded into a

dichotomous variable (yes/no); incentives; and, sanctions. An index of incentives1 and of

sanctions2 was computed for each client.

Control variables. Control variables include gender, age, ethnicity, and length of

program enrollment.

1 Incentives include number of commendations by court officer; lower level of supervision; Judge’s praise; report

closer to home; adjustments to or removal of curfew; gift card; return of driver’s license; field visit instead of office

visit; mail in reporting, sobriety chips; and removal of SCRAM device. 2 Sanctions include verbal warning, increase in CSR, community service, cognitive exercise, thinking report, higher

level of supervision, inpatient treatment, weekend in jail, and removal from court.

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RESULTS

Descriptive Statistics

The sample of 87 participants who had completed an LSI-R intake assessment was

predominantly male (82.8%), White (50.6%), employed full-time or part-time (83.9%), and at

least 81.6% had a high school degree or GED. The average age was 36 years-old. Clients were

enrolled in the program for an average of 513 days. Approximately 75% of all clients were

noncompliant.

Bivariate correlations

Bivariate correlations between predictor variables are presented in Table 1 below. Chi-

square tests of group distribution were used to assess the association between categorical

predictor variables and the dependent variable. The relationship between continuous predictors

and the treatment compliance was measured using t-test of group means. Bivariate analysis

results indicate that only two of the predictors, incentive and sanction counts, were significantly

correlated with our dependent variable.

___________________________________________________________

Insert Table 1 here

___________________________________________________________

Multivariate Analyses

Logistic regression was conducted to test our various hypotheses and determine

predictors of treatment noncompliance in a DWI court program setting. Prior to running the

logistic regression models, a Pearson’s product moment correlational analysis was conducted for

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all variables and did not reveal any correlation greater than .60 (see Table 2). The data was also

checked for multivariate outliers and multicollinearity. The data did not contain any multivariate

outliers at p = .001 (χ2 = 32.909; d.f. = 12). Also, no Variance Inflation Factor (VIF) value

exceeded 1.7 for any variable (multicollinearity is a problem only if VIF values are greater than

10).

___________________________________________________________

Insert Table 2 here

___________________________________________________________

Regression results indicate an overall model of three predictors (criminal acquaintances,

incentives count, and sanctions count) was statistically reliable in distinguishing between

compliant and noncompliant DWI clients (-2 Log Likelihood=72.724; χ2(2)=25.668, p<.01),

after controlling for length of enrollment. The model correctly classified 80.5% of the cases, and

accounts for 26.1% of the variance in the dependent variable (noncompliance). Regression

coefficients are presented in Table 3 below.

___________________________________________________________

Insert Table 3 here

___________________________________________________________

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Clients with delinquent peers were more likely to be noncompliant. The odds of a client

with criminal acquaintances to be noncompliant were 4.8 times greater than for a client with no

criminal acquaintances (p<.05). A greater count of sanctions and incentives also increased the

odds of noncompliance. The odds of a client being noncompliant were 1.5 times greater when

the count of incentives increased by one unit. Similarly, the odds of a client being noncompliant

were 2.2 times greater when the count of sanctions increased by one unit.

Consistent with prior literature, delinquent peers was found to be a significant predictor

of treatment noncompliance (Sung et al., 2004). The results failed to support findings in

previous literature that employment status and level of education completed have significant

effects on program noncompliance. Incentives increased treatment noncompliance, contrary to

findings in previous studies that incentives improve program participation .

DISCUSSION

Results indicate that delinquent peers, incentives, and sanctions significantly predict

treatment noncompliance. Associating with delinquent peers increases the odds of

noncompliance by 4.8 times (p<.05). A practical significance of this study indicates that clients

must be encouraged to cultivate pro-social peers through involvement in various socio-civic

groups and organizations with pro-social goals. Community involvement in neighborhood

associations and socio-civic organizations may be an aspect of the treatment process mandated

by the DWI court. The comparative small size of this sample, however, renders the results

tentative. Results of this study may not be generalized to other DWI court programs.

The study showed that increasing the number of incentives increased noncompliance.

This study, however, did not examine the timing of incentives (i.e., the phase during which it

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was given). Examination of the types of incentives may explain the positive association with

noncompliance. Examples of incentives are lower levels of supervision, adjustments to or

removal of curfew, and removal of SCRAM device. Granting these incentives at the early stages

of the program where the client may not have undergone behavioral changes conducive to

treatment compliance possibly does not lead to greater compliance. Lesser supervision, for

example, may lead to more infractions by the client if granted too early in the treatment program.

At least one study also indicates that vouchers may not be useful in improving treatment

compliance (Prendergast, et al., 2008). Prendergast et al. (2008) found that program participants

who received twice-weekly vouchers for good behavior were more likely to have poorer

performance. They concluded that the judge’s influence within the courtroom had a “stronger

impact” on engagement rather than the “relatively low-value vouchers” granted as incentives

(Prendergast et al., 2008, p. 125). Program administrators of the current DWI program might

consider revising the incentive grid presently enforced to encourage participation and

compliance with treatment conditions. The quality of incentives may be more useful in

determining treatment compliance than the number or rate of incentives granted to clients.

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ACKNOWLEDGMENTS

Data used for this article was prepared with the support of a grant from a Southern State

County’s Office of Court Management. The authors would like to thank the DWI Court judges

and the members of the County Community Supervision and Corrections Department for their

cooperation and insight throughout the evaluation process.

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Bouffard, J., Richardson, K., & Franklin, T. (2010). Drug courts for DWI offenders? The

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Dakof, G., Tejeda, M., & Liddle, H. (2001). Predictors of engagement in adolescent drug abuse

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De Civita, M., Dobkin, P., & Robertson, E. (2000). A study of barriers to the engagement of

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De Leon, G. (2000). The therapeutic community: Theory, model and method. New York:

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Fielding, J., Tye, G., Ogawa, P., Imam, I., & Long, A. (2002). Los angeles county drug court

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Joe, G., Broome, K., Rowan-Szal, G., & Simpson, D. (2002). Measuring patient attributes and

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Lang, M., & Belenko, C. (2000). Predicting retention in a residential drug treatment alternative

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McKellar, J., Kelly, J., Harris, A., Moos, R. (2006). Pretreatment and during treatment risk

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Prendergast, M., Hall, E., Roll, J., & Warda, U. (2008). Use of vouchers to reinforce abstinence

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Rempel, M, & DeStefano, C. (2001). Predictors of engagement in court-mandated treatment:

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Rowan-Szal, G., Joe, G., Chatham, L., & Simpson, D. (1994). A simple reinforcement system for

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of Substance Abuse Treatment, 27, 99-121.

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Slaght, E. (1999). Focusing on the family in the treatment of substance abusing criminal

offenders. Journal of Drug Education, 29, 53-62.

Sung, H., Belenko, S., & Feng, L. (2001). Treatment compliance in the trajectory of treatment

progress among offenders. Journal of Substance Abuse Treatment, 20, 153-162.

Sung, H., Belenko, S., Feng, L., & Tabachnick, C. (2004). Predicting treatment noncompliance

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Whitlock, R., & Lubin, B. (1998). Predicting outcome of court-ordered treatment of DWI

offenders via the MAACL-R. Journal of Offender Rehabilitation, 28, 29-40.

Wright, J., Cullen, F., & Miller, J. (2001). Family, social capital and delinquent involvement.

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Table 1

Bivariate associations (N = 87)

Variables Compliant clients

(N = 22)

Non-compliant

clients (N =

65)

Supportive Social Network Hypothesis

Satisfaction with spouse: Yes 16 (72.7%) 51 (78.5%)

No 6 (27.3%) 14 (21.5%)

Delinquent Peers

Criminal acquaintances: Yes 7 (31.8%) 33 (50.8%)

No 15 (68.2%) 32 (49.2%)


Employed full-time or part-time: Yes 17 (77.3%) 56 (86.2%)

No 5 (22.7%) 9 (13.8%)

Educational achievement: Less than HS 3 (13.6%) 13 (20.0%)

HS or GED 14 (63.6%) 41 (63.1%)

College 5 (22.7%) 11 (16.9%)

Treatment Motivation Hypothesis

Alcohol problem, currently: Yes 17 (77.3%) 50 (76.9%)

No 5 (22.7%) 15 (23.1%)

Incentives (count) a 1.41*** 3.60***

Sanctions (count) a .36*** 1.35***

Control Variables

Gender: Male 17 (77.3%) 55 (84.6%)

Female 5 (22.7%) 10 (15.4%)

Age a 38.45 35.26

Race/Ethnicity: White 12 (54.5%) 32 (49.2%)

NonWhite 10 (45.5%) 33 (50.8%)

Time in program (# days) a 485.41 523.00

a T-test comparison of means

* p < .05; ** p < .01; *** p < .001

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Table 2

Zero-order Correlation Matrix for Variables

1 2 3 4 5 6 7 8 9 10 11 12

COMPL --

SPOUSE -0.059 --

PEERS 0.165 0.209 --

EMPL -0.105 0.207 0.098 --

EDUC -0.087 -.225* -0.114 -0.052 --

ALCOH -0.004 -0.026 0.12 0.165 0.045 --

INCEN .282** -0.169 -0.122 0.013 -0.028 -0.17 --

SANCT .267* -0.035 -0.016 0.108 -0.118 0.086 0.062 --

GENDER -0.084 -0.032 0.189 .214* 0.151 0.105 0.039 0.159 --

AGE -0.128 0.074 -0.178 0.164 0.084 -0.178 .224* -0.042 -0.031 --

RACE 0.046 0.006 -.220* 0.005 -.265* -0.061 0.014 0.108 -0.086 0.133 --

ENROL 0.089 -0.093 -0.069 -0.144 0.072 -0.17 .446** 0.197 0.053 -0.002 -0.014 --

Note. COMPL = Compliance (Compliant vs. Non-Compliant); SPOUSE = Relationship with spouse; PEERS = Criminal

acquaintances; EMPL = Employed full-time or part-time; EDUC = Educational achievement; ALCOH = Current alcohol problem;

INCEN = Count of incentives; SANCT = Sanctions count; GENDER; AGE; RACE; ENROL = Number of days enrolled in the

program.

* p < .05; ** p < .01.

a. Listwise N = 87

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Table 3

Logistic regression model (N=87)

Predictors of non-compliance B S.E. Wald Df Sig. Exp (B)

Supportive Social Network Hypothesis

Relationship with spouse: Unsatisfactory .324 .747 .188 1 .665 1.382

Delinquent Peers

Criminal acquaintances: Yes 1.580* .705 5.027 1 .025 4.854


Employed full-time or part-time: No -1.214 .854 2.019 1 .155 .297

Educational achievement -.153 .546 .079 1 .779 .858

Treatment Motivation Hypothesis

Alcohol problem, currently: Yes -.080 .769 .011 1 .917 .923

Incentives (count) .415** .155 7.185 1 .007 1.515

Sanctions (count) .799* .341 5.492 1 .019 2.224

Control Variables

Time in program (# days) -.002 .002 1.145 1 .285 .998

Constant .028 1.328 .000 1 .983 1.029

* p < .05; ** p < .01; *** p < .001

-2 Log Likelihood = 72.724

Model Chi-square = 25.668 (p = .001)

McFadden R2

= .261

Percentage of correct classification = 80.5

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unlocking the black box

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