Alcohol use inventory: screening and assessment of first-time driving-while-impaired offenders. II. Typology and predictive validity

Typology of driving-under-the-influence (DUI) offenders revisited: Inclusion of DUI-specific attitudes

OBJECTIVES

Much work remains to improve rehabilitative interventions for driving-under-the-influence (DUI) offenders. There is heterogeneity of patterns of alcohol use, personality, and driving behaviour within DUI offenders, but little is known about how their appraisal of DUI differs. This study investigated within-offender variability in DUI-specific attitudinal variables.

METHOD

Convicted male DUI offenders (N=219) living in greater Tokyo were interviewed. Cluster analysis was undertaken using age, psychological distress, personality trait, alcohol use, and attitudes towards DUI. Discriminant function analysis explored the relative explanatory power of the grouping variables.

RESULT

Many offenders reported current excessive alcohol consumption. About 26-36% of the participants were potentially alcohol-dependent based on screening instruments/biomarkers. Cluster analysis identified five subgroups. The biggest subgroup considered their DUI a singular mistake and reported strong self-efficacy for avoiding further DUI (clusters 1 and 2). A small subgroup manifested alcohol dependence, psychological distress, higher impulsivity, and lower self-efficacy for avoiding DUI (cluster 3). Another subgroup exhibited a tendency to rationalise DUI, higher likelihood of future DUI, and lower self-efficacy for avoiding DUI (cluster 4). Most participants in another small subgroup abstained from alcohol use temporarily after their convictions (cluster 5).

CONCLUSION

The majority of DUI offenders regarded their DUI conviction as an exceptional mishap, while they continued consuming hazardous amounts of alcohol. DUI-specific attitudinal variables, including DUI rationalisation and self-efficacy for avoiding future DUI incidents, constituted a distinct aspect of the problem, suggesting the need to address this issue in addition to the underlying alcohol use problem.

Does screening classification predict long-term outcomes of DWI offenders?

OBJECTIVES

To determine the usefulness of a screening classification system in predicting treatment use, current substance abuse disorders (SUD), and driving over the alcohol limit (DOL) at 15-year follow-up.

METHODS

Interviewed 583 driving while imparied (DWI) first offenders with SUDs. Univariate and multivariate statistics were used to determine predictors of long-term outcomes.

RESULTS

Screening classification defined groups with different treatment histories and 15-year outcomes. Current SUDs were reported by 21% and DOL by 10%, of subjects.

CONCLUSIONS

Group differences suggest that screening data could be used more effectively to triage and treat DWI offenders.

Examining factors in the Research Institute on Addictions Self-Inventory (RIASI): Associations with alcohol use and problems at assessment and follow-up

Impaired driving is a leading cause of alcohol-related deaths and injuries. Rehabilitation or remedial programs, involving assessment and screening of convicted impaired drivers to determine problem severity and appropriate programs, are an important component of society's response to this problem. Ontario's remedial program, Back on Track (BOT), involves an assessment process that includes administration of the Research Institute on Addictions Self-Inventory (RIASI) to determine assignment to an education or treatment program. The purpose of this study is to identify factors within the RIASI and examine how factor scores are associated with alcohol use and problem indicators at assessment and six-month follow-up. The sample included 22,298 individuals who completed BOT from 2000 to 2005. Principal component factor analysis with varimax rotation was conducted on RIASI data and an eight factor solution was retained: (1) Negative Affect, (2) Sensation Seeking, (3) Alcohol-Quantity, (4) Social Conformity, (5) High Risk Lifestyle, (6) Alcohol Problems, (7) Interpersonal Competence, and (8) Family History. Regression analyses were conducted to examine associations between factors and alcohol and problem measures obtained at assessment and at follow-up. Most factors, except for Interpersonal Competence, were associated with more alcohol use and problems at assessment. A similar pattern was observed at 6-month follow-up, but interestingly some factors (Negative Affect, Sensation Seeking, Alcohol-Quantity and Family History) predicted fewer days of alcohol use. The Interpersonal Competence factor was associated with significantly lower levels of alcohol use and problems at both assessment and follow-up. This work suggests that the RIASI provides information on several domains that have important relationships with alcohol problem severity and outcomes.

A multiple risk factor approach for predicting DWI recidivism

A sample of DWI (driving while impaired) offenders was studied to compare various approaches for predicting reoffenses over a 4-year period. Logistic regression yielded multivariate predictor equations that were significant statistically, but were not helpful to clinicians in assessing risk for reoffending. As a different approach, five predictor variables that were consistently correlated with reoffense status were examined to determine the cut score at which the repeat offense rate exceeded the base rate. These were combined to yield the number of risk factors (from 0 to 5) for each offender. This method, used for the original and a hold-out sample, yields results as accurate as those derived from a logistic regression model that includes all the risk variables, and allows clinicians to classify offenders into low and high risk categories in a straightforward manner. Nearly half of offenders with four or five risk factors (age, years of education, arrest blood alcohol concentration (BAC), score on the receptive area scale of AUI and raw score on the MacAndrews scale of MMPI-2) were rearrested compared to the base rate (25%). However, this method is not sufficiently precise to accurately predict which individuals will and will not be rearrested. Although generalizability of specific algorithms across populations needs to be examined, this method appears promising as a clinically accessible way to classify, in a given offender population, those who are most likely to repeat the offense.