Published online: 06 Oct 2014.

This article was downloaded by: [99.16.120.110] On: 15 October 2014, At: 10:10 Publisher: Routledge Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK Alcoholism Treatment Quarterly Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/watq20 Recidivism Risk of Repeat Intoxicated Drivers Monitored with Alcohol Biomarkers Pamela Bean a, Brian Kay a, Jero Bean a, Claudia Roska b, James Pearson b, Carol Garuz b & Patricia Hallinan c a Millennium Strategies, Madison, Wisconsin USA b Addiction Resource Council, Waukesha, Wisconsin USA c School of Business, Edgewood College, Madison, Wisconsin USA Published online: 06 Oct 2014. To cite this article: Pamela Bean, Brian Kay, Jero Bean, Claudia Roska, James Pearson, Carol Garuz & Patricia Hallinan (2014) Recidivism Risk of Repeat Intoxicated Drivers Monitored with Alcohol Biomarkers, Alcoholism Treatment Quarterly, 32:4, 433-444, DOI: 10.1080/07347324.2014.950913 To link to this article: http://dx.doi.org/10.1080/07347324.2014.950913 PLEASE SCROLL DOWN FOR ARTICLE Taylor & Francis makes every effort to ensure the accuracy of all the information (the Content ) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content. This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. Terms & Conditions of access and use can be found at http://www.tandfonline.com/page/termsand-conditions

Alcoholism Treatment Quarterly, 32:433 444, 2014 Copyright Taylor & Francis Group, LLC ISSN: 0734-7324 print/1544-4538 online DOI: 10.1080/07347324.2014.950913 Recidivism Risk of Repeat Intoxicated Drivers Monitored with Alcohol Biomarkers PAMELA BEAN, PhD, MBA, BRIAN KAY, MS, and JERO BEAN, BS Millennium Strategies, Madison, Wisconsin USA CLAUDIA ROSKA, PhD, JAMES PEARSON, MSW, and CAROL GARUZ Addiction Resource Council, Waukesha, Wisconsin USA PATRICIA HALLINAN, PhD School of Business, Edgewood College, Madison, Wisconsin USA This feasibility study analyzed the effects of alcohol biomarker testing in reducing the rearrest rates and/or lengthening the time between rearrests in repeat intoxicated drivers in Waukesha County. Participants were 250 repeat offenders who underwent monitoring with biomarker testing after being arrested for driving under the influence (DUI) of alcohol between 2006 and 2009. In 2012, their traffic records were reviewed to determine any subsequent drunk driving arrests since their assessments in 2006 to 2009. Regression analysis was used to determine the relationship between the length of biomarker monitoring (LOM) and the time to recidivism (TTR). LOM was defined as the time between the offender s first test at enrollment in this study (baseline Early Detection of Alcohol Consumption [EDAC]) and the last test at the end of monitoring. TTR was defined as the length of time from baseline EDAC until the driver s next subsequent DUI offense. The results showed that 32 of the 250 drivers enrolled in this pilot were rearrested during the study period for an overall recidivism rate The authors wish to thank Dr. James Harasymiw and Mr. Elwood Kleaver from Alcohol Detection Services for providing the EDAC tests free of charge during the duration of this study. The authors are also grateful to Mr. Kurt Holmquist and Ms. Darlene Schwartz from the Wisconsin Department of Transportation for their assistance with reviewing the public records for the drivers enrolled in this study. Address correspondence to Pamela Bean, PhD, Millennium Strategies, West Town Office Center, 6701 Seybold Rd, Suite 129, Madison, WI 53719. E-mail: PamBean@charter.net; website: thebiomarkerproject.com 433

434 P. Bean et al. of 12.8%. The rearrested drivers were mostly men (88%), young adults (34 years), employed (84%), and predominantly single or divorced (86%). Their drinking profile at baseline showed that 93% claimed at least one month of abstinence before their first sample was collected yet almost one half (48%) of them tested positive for biomarkers. Regression analysis showed a positive and significant correlation between LOM and TTR (r D 0.41; p < 0.05; coefficient for LOM D 1.28; N D 23) indicating that for every additional day of biomarker monitoring the time to re-arrest increased by more than one day. This feasibility study provides preliminary evidence suggesting that repeat DUI offenders monitored with alcohol biomarkers take longer to get rearrested with a subsequent DUI than those not monitored with biomarkers. KEYWORDS Alcohol biomarkers, recidivism, intoxicated drivers INTRODUCTION A recent survey released by the U.S. Department of Health and Human Services reported that Wisconsin had the highest prevalence of drunk driving out of the 50 states in the country with 26% of Wisconsinites stating they had driven under the influence in the last year (Substance Abuse and Mental Health Services Administration [SAMHSA], 2008). Based on these figures, several counties in Wisconsin have started to implement new approaches to reduce drunk driving; one of these approaches consists in monitoring drivers with alcohol biomarkers. Alcohol biomarkers are biological indicators that form in every person s body as a consequence of heavy drinking (Javors, Bean, King, & Anton, 2003). They differ from blood or urine alcohol in that they stay in the body for periods of weeks to months after the drinking has stopped. There are two main types of alcohol biomarkers, direct and indirect (SAMHSA, 2012). Direct biomarkers are those that form in the body after the body metabolizes alcohol; typical examples of direct biomarkers are ethyl glucuronide (EtG) and phosphatidyl ethanol (PEth) (Bakhireva et al., 2013; Jones et al., 2012). Indirect biomarkers are biological indicators that reflect the damage that heavy drinking is causing to a person s body; heavy drinking defined as four to five drinks daily on average for men and three to four for women. Typical examples of indirect biomarkers are the liver enzyme gamma-glutamultransferase (GGT), the blood component mean corpuscular volume (MCV), and the newer indicators carbohydrate-deficient transferrin (CDT) (Javors et al., 2003; SAMHSA, 2012) and the Early Detection of Alcohol Consumption (EDAC) test (Harasymiw & Bean, 2001a, 2001b). The measurement of indirect biomarkers requires a sample of blood whereas direct

Alcohol Biomarker Monitoring and Recidivism 435 biomarkers can be measured from several body matrixes including blood, urine, hair, and even nails. Indirect biomarkers provide health information about the participant being tested and therefore can be reimbursed by medical insurance, whereas direct biomarkers constitute strictly forensic testing with neither health information nor insurance reimbursement available up to now. The use of alcohol biomarkers to monitor participants who drive under the influence (DUI) of alcohol has been popular in Europe since the mid 1990s (Appenzeller, Schneider, Maul, & Wennig, 2005; Bortolotti et al., 2007; Morgan & Major, 1996). In at least eight European countries intoxicated drivers undergo a medical examination after their arrest, and this examination includes testing for alcohol biomarkers. European countries use mainly indirect biomarkers (GGT and CDT), and testing is done every quarter for at least a year to monitor alcohol abstinence. Drivers with positive biomarkers results cannot get back their drivers licenses until their biomarker results turn negative. More recently, a similar study conducted by Marques et al. (2010) in Alberta, Canada, revealed that higher biomarker levels predicted higher levels of blood alcohol test failures in those mandated to use interlock devices. Marques study used direct and indirect biomarkers in more than 500 drivers convicted of driving intoxicated; the authors concluded that these tools play an important role in the prediction and control of drivers risk for heavy drinking when re-granting licenses. In the United States, the use of alcohol biomarker testing was first adopted by Waukesha County in the year 2006 (Bean et al., 2009) when testing became a part of repeat offenders Driver s Safety Plan (DSP) in that county. The DSP is a statewide set of mandatory and individualized recommendations for the safety, education and treatment of intoxicated drivers. It is required from all intoxicated drivers in the State of Wisconsin after every arrest for DUI; all the requirements of the DSP must be completed for intoxicated drivers to regain their driver s licenses. The biomarker used in Waukesha County was the EDAC test that belongs to the group of indirect biomarkers and consists of using a panel of routine laboratory tests to derive a probability of heavy drinking (P1) for the individual being tested. The panel of routine tests used in the EDAC was originally chosen using a mathematical model called linear discriminant function (LDF) analysis, and it was selected to reflect the damage that heavy drinking causes to several organ systems in the body (Harasymiw & Bean, 2001b; Harasymiw, Seaberg, & Bean, 2004). When compared to other indirect biomarkers, the EDAC has proven to detect heavy drinkers in larger numbers and with better accuracy than GGT, MCV, Aspartate aminotransferase (AST), Alanine aminotransferase (ALT) and CDT (Harasymiw & Bean, 2001b; Harasymiw & Bean, 2007; Harasymiw, Seaberg, & Bean, 2006). A total of 250 repeat offenders were enrolled from 2006 to 2009, and the short-term results showed that two out of every three repeat offenders (ROs)

436 P. Bean et al. reported full abstinence for the 30 days prior to their assessment interviews but tested positive for biomarkers, suggesting a high rate of denial of their drinking at baseline. The results also showed that the EDAC could identify three types of high-risk drivers, those who relapsed (20%) during the DSP, those who remained sober (50%), and those who became noncompliant (30%) with biomarker testing. Of those ROs who relapsed as identified by an elevated EDAC result during follow-up most (80%) returned to abstinence or reduced their drinking after biomarker information was used by the counselor to deliver a brief phone intervention reminding them of the DSP requirement for abstinence (Bean et al., 2009). Similar short-term results have been found more recently with third-repeat offenders in Dane County (Bean et al., 2013). The long-term objective of the Waukesha pilot was to determine whether biomarker testing would have an impact in the recidivism profile of repeat intoxicated drivers in this county. To that effect, in 2012 6 years after the start of the pilot we examined the public records for the first 250 ROs tested with biomarkers. The goal was to determine the relationship between the length of biomarker monitoring and the time to a subsequent DUI arrest. Only one previous study (Marques, Tippetts, & Yegles, 2013) has shown alcohol biomarkers as top predictors of new recidivism events in intoxicated drivers. This study attempts to address the gap of longitudinal outcomes research in traffic medicine by exploring the relationship between biomarker monitoring and the risk of recidivism in a population of repeat intoxicated drivers in the United States. Participants METHOD Drivers (N D 250) were repeat offenders presenting to the Addiction Resource Council (ARC) in Waukesha County from September 2006 to 2009 for a state-mandated assessment after receiving their third DUI arrest. Only third-repeat offenders were chosen to participate in this pilot because they represented a manageable fraction (10%) of the total number of annual assessments done by ARC, in terms of resources and operational costs. All drivers enrolled in this study signed a consent form allowing the use of the data for research purposes following the confidentiality guidelines recommended by the National Institute of Health and the Federal Wide Assurance (FWA) for the Protection of Human Subjects. To be included in this pilot, third offenders had to comply with the following criteria at the assessment interview: (1) experienced their second and third DUI arrest within 5 years of each other, (2) showed a blood alcohol concentration (BAC) of greater than 0.15 at the time of the third DUI arrest,

Alcohol Biomarker Monitoring and Recidivism 437 (3) had a relative or partner concerned with their current use of alcohol, and (4) received a diagnoses of alcohol dependence or suspected dependence after using the Wisconsin Assessment of the Impaired Driver (WAID) screening instrument. If all these conditions were met then biomarker testing was requested at baseline (assessment interview) and every 3 months during the 12 months duration of the mandatory follow-up period (Bean et al., 2009). Drivers who experienced a relapse during monitoring received a brief intervention by phone, and additional biomarker testing was requested within a month of obtaining the positive result. Not all data points were available for every driver mainly due to (1) a temporary shortage of staff resources and (2) to drivers becoming noncompliant with biomarker testing. No data were excluded from the analysis. The EDAC Test The biomarker used in this pilot was the EDAC test, which has been tested and validated in several populations including impaired professionals, college students, methadone clinics, and participants undergoing outpatient alcohol treatment (Harasymiw, Forster, & Bean, 2006; Harasymiw et al., 2004; Harasymiw, Vinson, & Bean, 2000). The routine tests of the EDAC included a comprehensive metabolic panel along with complete blood counts and differentials as described previously (Harasymiw & Bean, 2001b; Harasymiw et al., 2004). The panel of routine tests was performed at Dynacare Laboratories (Milwaukee, WI) and the results were forwarded to Alcohol Detection Services (Big Ben, Wisconsin) to calculate the probability of heavy drinking (P1) and the risk of alcohol causing harm to the body (low, medium, high). Probabilities higher than 50%P1 were indicative of heavy drinking and a manifestation of the harmful effect that alcohol was having in that person s body. Probabilities below 50%P1 reflected light drinking and manifested no serious harm to the body. When the EDAC was used as a monitoring tool, the changes in P1 scores were compared within the same individual over time. A 30-point increase in the EDAC value from the previous measure was used to signal a relapse to heavy drinking. Conversely, a 30-point decrease signaled reduced drinking and abstinence. For example, a participant showing an increase from 40%P1 to 70%P1 between two consecutive tests signaled a relapse and a driver showing a decrease from 60%P1 to 30%P1 between two consecutive EDACs reflected decreased drinking or abstinence. This variation rate has been previously used as a standard when monitoring abstinence and relapses in several populations of heavy drinkers, including drunk drivers (Borg, Helander, Carlsson, & Brandt, 1995; Maenhout, Baten, De Buyzere, & Delanghe, 2012; Rosman, Basu, Galvin, & Lieber, 1995). Consistent with these standards, the variation rate of the EDAC among different laboratories averages less than 20% (Bean & Harasymiw, 2011).

438 P. Bean et al. Study Design During follow-up, when the EDAC result was negative (<50%P1) or had decreased by at least 30 points from the previous measure then the repeat offender was contacted again at the preestablished follow-up periods (3, 6, 9, and 12 months after baseline). However, if the biomarker result was positive (50%P1) or had increased by 30 points or more from the previous measure the repeat offender was contacted by the assessor immediately to receive a brief phone intervention reminding him or her to stay sober, encouraging additional treatment, and requesting a mandatory repeat test within a month. The enrollment of new drivers ended in late 2009; and in 2012, the Wisconsin State Department of Transportation (DOT) examined the public rearrest records of all 250 drivers to determine any additional DUI offenses since the start of the pilot in 2006. The rearrest information was used to determine the relation between the length of the monitoring period (LOM) and the time to recidivism (TTR). LOM was defined as the time between the offender s first test at enrollment in this study (baseline EDAC) and the last test at the end of the monitoring period. TTR was defined as the length of time from baseline EDAC until the driver s next subsequent DUI offense. Statistical Analysis Pearson regression analysis was used to determine whether changes in the length of biomarker monitoring (LOM) would predict the time to subsequent DUI arrest (TTR). A probability of 0.05 or smaller was used to indicate that the relationship between these two variables was statistically significant. RESULTS The rearrest records examined by the DOT showed that 32 of the 250 offenders enrolled in the Waukesha pilot had been rearrested with a new DUI since 2006. This translates into an overall recidivism rate of 12.8% and a mean recidivism rate of 2.1% annually. The rearrested drivers were mostly men (88%), young adults (age 34 years), employed (84%), and predominantly single or divorced (86%) (Table 1). Their drinking profile showed that almost all of them (93%) claimed full abstinence for the 30 days immediately before their assessment interview even though their biomarker profile shows that almost one half (48%) of them tested positive for biomarkers (EDAC, GGT, or both). A fraction (19%) tested positive for the EDAC test indicative of more recent heavy drinking and 29% tested positive for GGT indicative of prolonged liver damage. Next, ROs were classified into two groups based on their compliance with follow-up. Group 1 were drivers who received EDAC testing only at the

Alcohol Biomarker Monitoring and Recidivism 439 TABLE 1 Profile of Repeat Offenders Rearrested During Study Period Demographics All (N D 31) All rearrests Control (n D 8) No intervention Experimental (n D 23) Brief intervention(s) Percent of drivers 100 25 75 % Male 88 100 83 Age (Range in years) 34 (20 51) 35 (25 51) 34 (20 50) % Employed (full- or part-time) 84 75 83 % Single or divorced 86 75 86 Drinking profile % reports abstinence at baseline 93 88 95 # abstinent days before baseline 28 30 27 Biomarker profile % EDACC baseline 19 0 26 % GGTC 29 50 22 % EDACC and/or GGTC 48 50 43 Recidivism profile # EDACs during monitoring 2.7 1 3.3 LOM (days) 214 0 290 TTR (days) 690 501 775 EDAC D Early Detection of Alcohol Consumption; GGT D gamma-glutamultransferase; LOM D length of monitoring; TTR D time to recidivism. assessment interview (baseline) but never came back for follow-up; these ROs represented the control no-intervention group in this analysis. Group 2, were drivers who received EDAC testing at baseline and at least one followup; these ROs represented the experimental intervention group. In addition to biomarker testing this group also received brief phone interventions every time the biomarker results were positive during follow-up. The demographic profile of these two groups at the time of the assessment interview showed that they were similar in terms of age (range D 33 35 years), gender (range D 83% 100% males), percent employed (range D 75% 83%), and percent single or divorced (range D 75% 86%). The range of variation for all these variables was less than 17% between the two groups. The two groups were also similar in terms of their reports of alcohol use with the vast majority of drivers in both groups reporting drinking no alcohol at all (range D 88% 95%, respectively) and abstaining for almost a month (range D 27 30 days, respectively) prior to their baseline EDAC. Overall, 50% of the ROs in the control group and 43% of those in the experimental group showed biomarker data which suggests heavy drinking in contrast to their self-reports. The recidivism profile showed that drivers in Group 1 were rearrested an average of 501 days (17 months), whereas drivers in Group 2 were rearrested an average of 775 days (26 months) after the baseline EDAC. This translates

440 P. Bean et al. FIGURE 1 Regression analysis between length of monitoring (LOM) and time to recidivism (TTR). into an average delay of 274 days for the rearrests of ROs in the experimental group compared to the control nonintervention group. The regression analysis between the LOM and the time to recidivism was done taking into consideration only the ROs in Group 2, which corresponds to the intervention group. The results showed a significant correlation (r D.41, n D 23, p <.05) between these two variables suggesting that the time to recidivism is positively related to the LOM period (Figure 1). The coefficient associated with LOM was 1.28 which means that for every 10 additional days of monitoring the TTR increased by 12.8 days during the time of this study. DISCUSSION Several reports using alcohol biomarkers in high-risk DUI offenders have shown that these indicators can provide useful information to assist decisions regarding the reinstatement of drivers licenses in Europe and Canada (Appenzeller et al., 2005; Bianchi, Ivaldi, Raspagni, Arfini, & Vidali, 2010; Bortolotti et al., 2007; Morgan & Major, 1996). Most of these studies have used mainly cross-sectional analyses conducted at just one time period that has experts in the field agreeing with the need for longitudinal outcomes research to more comprehensively explore the relationship between biomarker monitoring and the drivers risk of recidivism. More recently, Maenhout et al. (2012) reported that 1-year follow-up biomarker data revealed a favorable outcome for programs of licenses renewal conducted in Belgium and Mar-

Alcohol Biomarker Monitoring and Recidivism 441 ques et al. (2013) reported that EtG in hair was a top predictor of recidivism in DUI drivers in Canada. The preliminary results reported here seem to agree with the beneficial impact of these previous two longitudinal studies. In Waukesha, biomarker testing and brief interventions during relapse delayed the rearrests dates of drivers by an average of 274 days when comparing the control and the experimental groups. The demographic profile of the high-risk offenders identified in this study shows that those most likely to reoffend were young adult males, employed, and living alone who denied heavy drinking prior to the assessment interview despite a frequently positive biomarker test. Similar results have now been reported in third repeat offenders monitored with the EDAC in Dane County (Bean et al., 2013). The main findings of this analysis also showed an overall rearrest rate of 12.8% from 2006 to 2012 that translates into a 2.1% recidivism rate per year on average. For comparison, the Addiction Resource Council reported that the rearrest rates for third offenders to become fourth offenders in Waukesha County for the 4 years prior to implementing this pilot were 2.9%, 3.1%, 2.6%, and 3% for the years 2002 to 2005, respectively. Based on these figures, biomarker monitoring appears to have little effect on reducing the rearrests rates when comparing the periods before and after introducing the EDAC. Additional longitudinal data is needed to compare these rates with a larger number of repeat offenders which will be gathered over the coming years. The main limitation of this study is the small number (N D 23) of rearrested drivers considered in the regression analysis. This can be explained by the rigors of the inclusion criteria (only third repeat offenders were included) and the relatively long time (2.2 years) it takes for third-repeat offenders in Waukesha to recidivate. With this in mind, this pilot was designed primarily as a feasibility study that, if proven successful could be expanded to a larger fraction of repeat offenders and additional Wisconsin counties. Since 2006, new biomarker programs have been implemented in Dane, Forest, Kenosha, Oneida, and Vilas counties with more than 1,500 repeat offenders tested so far using either indirect or direct biomarkers. Over the next few years, the use of aggregated rearrest data from all counties combined will help determine if biomarker testing has indeed an effect on reducing subsequent rearrest rates in these counties. Biomarker testing is permissible in Wisconsin under intoxicated driver program rules (HFS 62.03) and in treatment-oriented driver safety plan programs. These tests are becoming helpful in identifying those high-risk ROs who continue to drink heavily after their DUI arrest and those who relapse during monitoring. This information allows assessors and counselors to more objectively address issues of denial when the ROs claim full abstinence and to provide better recommendations for the treatment of those drivers flagged by positive biomarkers.

442 P. Bean et al. Each repeat offender costs taxpayers in Wisconsin $90 per day in jail expenses alone (Brown, 2011) that translates into an annual cost of almost $33,000 per year per driver. For comparison, the cost of one biomarker test in the United States direct or indirect ranges from $50 to $100. Therefore, monitoring a single RO for 12 months with five biomarker tests at $75 per test costs these counties $375 per year, almost 10 times less than one year of jail for a single inmate. In fact, one year of jail for a single RO can fund one year of biomarker testing for almost 100 drivers. In Wisconsin, biomarker testing is developing as one more tool for assessors and treatment providers to flag high-risk drivers in urban and rural counties. Armed with this information, assessors and counselors are better able to recommend a more adequate level of care, provide timely interventions during relapses, and work with treatment providers more closely and objectively to keep ROs engaged in their recovery for longer. The use of alcohol biomarkers is helping these counties allocate their limited resources more effectively by providing more targeted care which is also less expensive than current practices such as interlock and jail. REFERENCES Appenzeller, B. M., Schneider, S., Maul, A., & Wennig, R. (2005). Relationship between blood alcohol concentration and carbohydrate-deficient transferrin amound drivers. Drug and Alcohol Dependence, 79(2), 261 265. Bakhireva, L. N., Savich, R. D., Raisch, D. W., Cano, S., Annett, R., Leeman, L., : : : Savage, D. (2013). The feasibility and cost of neonatal screening for prenatal alcohol exposure by measuring phosphatidylethanol in dried blood spots. Alchoholism: Clinical and Experimental Research, 37, 1008 1015. Bean, P., Bean. J., Jacobson, A., Smith, K., Harasymiw, J., & Campbell, T. (2013). Alcohol biomarkers as tools to establish risk patterns and guide treatment decisions in repeat intoxicated drivers in Dane County. Alcoholism Treatment Quarterly, 3, 67 77. Bean, P., & Harasymiw, J. (2011). The reproducibility of the Early Detection of Alcohol Consumption (EDAC) test using split samples analyzed in different laboratories. Alcohol and Alcoholism, 46, 694 701. Bean, P., Roska, C., Harasymiw, J., Pearson, J., Kay, B., & Louks, H. (2009). Alcohol biomarkers as tools to guide and support decisions about intoxicated driver risk. Traffic Injury Prevention, 10, 519 527. Bianchi, V., Ivaldi, A., Raspagni, A., Arfini, C., & Vidali, M. (2010). Use of carbohydrate-deficient transferrin (CDT) and a combination of GGT and CDT (GGT- CDT) to assess heavy alcohol consumption in traffic medicine. Alcohol and Alcoholism, 45(3), 247 251. Borg, S., Helander, A., Carlsson, A. V., & Brandt, A. (1995). Detection of relapses in alcohol-dependent patients using carbohydrate-deficient transferrin: Improvement with individualized reference levels during long-term monitoring. Alcohol: Clinical and Experimental Research, 19, 961 963.

Alcohol Biomarker Monitoring and Recidivism 443 Bortolotti, F., Trettene, M., Gottardo R., Bernini, M., Ricossa, M. C., & Taqliaro, F. (2007). Carbohydrate-deficient transferrin (CDT): A reliable indicator of the risk of driving under the influence of alcohol when determined by capillary electrophoresis. Forensic Science Internation, 170(2/3), 175 178. Brown, R. (2011). Drug court effectiveness: A matched cohort study in the Dane County Drug Treatment Court. Journal of Offender Rehabilitation, 50, 191 201. Harasymiw, J., & Bean, P. (2001a). The combined use of the Early Detection of Alcohol Consumption Score and carbohydrate-deficient transferrin to identify heavy drinking behavior in males. Alcohol and Alcoholism, 36, 349 353. Harasymiw, J., & Bean, P. (2001b). Identification of heavy drinkers by using the Early Detection of Alcohol Consumption (EDAC) test. Alcohol: Clinical and Experimental Research, 25, 228 235. Harasymiw, J., & Bean, P. (2007). The EDAC shows better performance than gammaglutamyltransferase (GGT) to detect heavy drinking in a large population of males and females. Medical Science Monitor, 13, 19 24. Harasymiw, J., Forster, I., & Bean, P. (2006). Using the EDAC test to monitor abstinence and relapses during outpatient treatment. Journal of Addictive Diseases, 36, 59 63. Harasymiw, J., Seaberg, J., & Bean, P. (2004). Detection of alcohol abuse using a routine test panel: The EDAC test. Alcohol and Alcoholism, 39, 329 335. Harasymiw, J., Seaberg, J., & Bean, P. (2006). Using routine laboratory tests to detect heavy drinking in the general population. Journal of Addictive Diseases, 59 63. Harasywic, J., Vinson, D., & Bean, P. (2000). The Early Detection of Alcohol Consumption (EDAC) score in the identification of heavy and at-risk drinkers from routine blood tests. Journal of Addictive Diseases, 25, 43 59. Javors, M., Bean, P., King, T., & Anton, R. (2003). Biochemical markers of alcohol consumption. In B. Johnson, P. Ruiz, & M. Galanter (Eds.), Handbook of clinical alcoholism (pp. 62 79). Baltimore, MD: Lippincott Williams & Wilkins. Jones, J., Jones, M., Plate, C., Lewis, D., Fendrich, M., Berger, L., & Fuhrmann, D. (2012). Liquid chromatography-tandem mass spectrometry assay to detect ethyl glucuronide in human fingernail: Comparison to hair and gender differences. American Journal of Analytical Chemistry, 3, 83 91. Maenhout, T., Baten, G., De Buyzere, M., & Delanghe, J. (2012). Screening and identification Carbohydrate-deficient transferrin in a driver s regranting program. Alcohol and Alcoholism, 47(3), 253 260. Marques, P., Tippetts, S., & Yegles, M. (2014). Ethylglucuronide in hair is a top predictor of impaired driving recidivism, alcohol dependence, and a key marker of the highest BAC interlock tests. Traffic Injury Prevention, 15, 361 369. doi: 10.1080/15389588.2013.824569 Marques, P., Tippetts, S., Allen, J., Javors, M., Alling, C., Yegles, M., Pragst, F. & Wurst, F. (2010). Estimating driver risk using alcohol biomarkers, interlock BAC test and psychometric assessments: Initial descriptives. Addiction, 105(2), 226 239. Morgan, M. Y., & Major, M. G. (1996). The use of serum carbohydrate-deficient transferrin in the assessment of high risk offenders in Great Britain. Alcohol and Alcoholism, 31(6), 625 628.

444 P. Bean et al. Rosman, A. S., Basu, P., Galvin, K., & Lieber, C. S. (1995). Utility of carbohydratedeficient transferrin as a marker of relapse in alcoholic patients. Alcohol: Clinical and Experimental Research, 19, 611 616. Substance Abuse and Mental Health Services Administration. (2008). State estimates of persons 18 and older driving under the influence of alcohol or illicit drugs. Retrieved from http://www.samhsa.gov/data/2k8/statedui/statedui.htm Substance Abuse and Mental Health Services Administration. (2012). The role of biomarkers in the treatment of alcohol use disorders. 2012 Revision Substance Abuse Treatment Advisory News for the Treatment Field, 11(2), 1 7.