The Brief Addiction Science Information Source (BASIS)

The DRAM, Vol. 8(1) – Do U WAN2 DRNK < ?

Research shows that the emergency room (ER) might be an important location for mental health screening and intervention, particularly drug and alcohol screening (Fleming et al., 2007). However, most ERs have limited time and resources to conduct such interventions. This week, the DRAM reviews a study examining the implementation and efficacy of an ER-based screening and brief intervention for young adults delivered through text messaging (Suffoletto, Callaway, Kristan, Kraemer, & Clark, 2011).

Method

• Participants were 45 individuals (41% of those initially recruited; 87% of those who screened in) recruited during emergency room visits. Participants were 18-24 years old and screened positive for hazardous drinking according to a past-three month version of the Alcohol Use Disorder Identification Test – Consumption (AUDIT-C: Dawson, Grant, & Stinson, 2005).
• At baseline and 12-week follow-up, all participants completed a computerized timeline follow-back (TLFB) assessment of their past-three month drinking, as well as the Rutgers Alcohol Problems Index to assess past-three month alcohol related problems.
• During the study, all participants received weekly texts for 12 weeks. Researchers randomized participants into one of three groups:
  • Intervention (n = 15):  Participants received weekly texts inquiring about their alcohol consumption. If they drank at hazardous levels during that week, they received additional texts asking them to set a goal to reduce their alcohol consumption during the next week and providing tips for reducing or assessing their drinking levels.
  • Assessment (n = 15): Participants received the same text inquiry about their consumption as the intervention group, but no follow-up texts about reducing or assessing their drinking.
  • Control (n = 15): Participants received weekly text messages reminding them that the researchers would ask them to complete a final survey at the end of the study.

Results

• Almost all (97%) of the young adult ER patients approached for the study willingly completed the AUDIT-C screen, and approximately half screened positive for hazardous drinking. All but one potential participant had access to a phone with text messaging capabilities. Retention was high: 70-80% of the participants in the assessment and intervention conditions responded to all 12 weekly texts, and more than 90% responded to at least one.
• According to weekly responses, participants who received the text-based intervention were less likely to have heavy drinking days (i.e., drinking more than 3 [women] of 4 [men] drinks in a single day) over the 12 weeks compared to participants in the assessment condition. Specifically, participants in the intervention condition had fewer weeks with a heavy drinking day (Median=2, IQR=1-7, compared to Median=7, IQR=3-9), and were less likely to have back-to-back weeks with heavy drinking days (58%, 95% CI=47-68, compared to 69%, 95% CI=58-79) than participants in the assessment condition.
• As Figure 1 shows, according to comparisons between the baseline and follow-up TLFB assessments of prior month drinking, participants in the intervention condition decreased their heavy drinking days and drinks per day, whereas participants in the assessment condition increased their heavy drinking days and drinks per day. 

Figure 1. Change in drinks per day and heavy drinking days from baseline to 12-week follow-up (adapted from Suffoletto et al., 2011).

*p<.05 between conditions for change in number of heavy drinking days; **p<.001 between conditions for change in number of drinks per drinking day
Note. Negative numbers represent decreases.

Limitations

• The small sample size and short follow-up period limit the strength of the study.
• Control participants’ 12-week follow-up data might have been less accurate than other participants’ follow-up data, because researchers did not prompt them to remember their alcohol consumption on a weekly basis.

Conclusions

Suffoletto et. al’s study suggests that text messaging might be a promising brief intervention strategy, particularly for young adults. As communication channels become more advanced and innovative, public health approaches should explore the value of utilizing contemporary channels for promoting positive health behavior.

-Sarah Nelson

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References

Dawson, D. A., Grant, B. F., & Stinson, F. S. (2005). The AUDIT-C: screening for alcohol use disorders and risk drinking in the presence of other psychiatric disorders. Compr Psychiatry, 46(6), 405-416.

Fleming, E. A., Gmel, G., Bady, P., Yersin, B., Givel, J. C., Brown, D., et al. (2007). At-risk drinking and drug use among patients seeking care in an emergency department. J Stud Alcohol Drugs, 68(1), 28-35.

Suffoletto, B., Callaway, C., Kristan, J., Kraemer, K., & Clark, D. B. (2011). Text-Message-Based Drinking Assessments and Brief Interventions for Young Adults Discharged from the Emergency Department. Alcohol Clin Exp Res.

The DRAM, Vol. 7(10) – Drinking and Dropping Out - Common Genetic Influences?

Many studies have described an association between academic attainment and alcohol use. For example, Crum et al. (2006) found that drinking among adolescents is associated with more educational problems and Kessler et al. (2005) found that alcohol and substance use disorders are more prevalent among those with lower education levels. It is unclear, however, what role genetic or environmental influences play in this association. This week’s DRAM reviews a study exploring influence of genetic and environmental factors on alcohol use and academic attainment (Latvala, Dick, Tuulio-Henriksson, Suvisaari, Viken, Rose & Kaprio, 2011).

Methods

• 4,858 Finnish twins (1,546 monozygotic [MZ] twins and 3,312 dizygotic [DZ] twins) comprised the sample.
• Participants completed the Rutgers Alcohol Problem Index (RAPI) and reported the maximum number of drinks ever consumed within a 24-hour period, as well as their highest level of education.
• Researchers tested ACE models (GLOSSARY LINK) predicting education, alcohol use problems, and maximum drinks, as well as their correlation, from a combination of three variables derived from twin type (i.e., MZ vs. DZ): genetic influences (A), common environmental influences (C; e.g., family environment), and unique environmental influences (E; e.g., friends).

Figure 1. Schematic of models predicting alcohol use and education attainment (Lavata et al. 2011)

 

Results

• As Figure 1 shows, genetic factors (A) and common environmental factors (C), as well as unique environmental factors (E), predicted educational attainment; however, only genetic factors and unique environmental influences predicted the alcohol use outcomes.
• Genetic factors (A) were significantly related to the negative association between educational attainment and alcohol use problems, which was small but significant in the full sample (r = -.06 to -.09). Put simply, MZ twins had higher correlations between educational attainment and alcohol use outcomes than DZ twins. This pattern was not found for common (C) or unique environmental (E) influences.

Limitations

• The education system in Finland is very different from that in the United States: schooling is free and mandatory through 9^th grade (middle school). Schools are of relatively uniform quality (Latvala et al., 2011). This may limit generalizability to countries such as the US, where income and social status can greatly influence education accessibility and quality Twin studies are limited in their generalizability, given the unique context within which twins develop.
•  This study cannot determine causality; we cannot know whether genetic or environmental factors directly influence educational attainment or alcohol use, only their level of association.

Conclusions

The current study suggests that genetic factors (A) influence both educational attainment and alcohol use, as well as their relationship, suggesting that there may be a common genetic underpinning for both these phenotypes. Common environmental influences (C), however, only influenced educational achievement. These results suggest a genetic influence on both educational attainment and alcohol use. Environmental influences play a greater role in educational attainment than alcohol use. 

-Daniel Tao

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References

Crum, R.M., Juon, H.S., Green, K.M., Robertson, J., Fothergill, K., Ensminger, M. (2006). Educational achievement and early school behavior as predictors of alcohol-use disorders. Journal of Studies on Alcohol, 67, 75-85.

Kessler, R.C., Berglund, P., Demler, O., Jin, R., Merikangas, K.R., Waters, E.E. (2005). Lifetime prevalence and age-of-onset distributions of DSM-IV disoders in the National Comorbidity Survey Replication. Archives of General Psychiatry, 62, 593-602.

Lavata, A., Dick, D.M., Tuulio-Henriksson, A., Suvisaari, J., Viken, R.J., Rose, R.J., Kaprio, J. (2011). Genetic correlation and gene-environment interaction between alcohol problems and educational level in young adulthood. Journal of Studies on Alcohol and Drugs, 72, 210-220. 

The DRAM, Vol. 7(9) - Slipping through the cracks: Underdiagnosis of comorbid mental illness among repeat DUI offenders

This week, the DRAM continues its series focusing on driving under the influence (DUI). This is the last of five issues concentrating on the body of recent DUI research. In the first four issues, DRAM Vol. 7(5), DRAM Vol. 7(6), DRAM Vol. 7(7) , and DRAM Vol. 7(8), we addressed DUI-related trends, methods to detect heavy alcohol consumption and recidivism, the use of ignition interlock devices to reduce DUI behavior, and different psychiatric profiles among DUI offenders.

Repeat DUI offenders represent a population of individuals who are at high risk for co-occurring psychiatric disorders (Lapham, C'De Baca, McMillan, & Lapidus, 2006). However, in a variety of clinical settings, such as addiction treatment facilities and DUI offender programs, offenders often do not undergo comprehensive screening for psychiatric disorders (Nelson et al., 2007; Shaffer et al., 2007). This week, the DRAM reviews a study that evaluated the extent to which repeat DUI offenders are diagnosed with comorbid psychiatric disorders during mandatory treatment (McMillan et al., 2008).

Method

• The sample consisted of 233 repeat DUI offenders (86% male, Mage= 38.5, 72% White) scheduled to undergo mandatory alcohol treatment at seven licensed facilities.
• The investigators assessed their sample with a battery of surveys, using the following measures:
  • The Composite International Diagnostic Interview (CIDI; Robins, Helzer, Ratcliff, & Seyfried, 1982)—assesses whether respondents meet diagnostic criteria for a range of mental health disorders. The investigators used the CIDI to assess disorders occurring within the past 12 months.
  • The Treatment Abstraction Form (Timken, 2001)—gathers data from treatment charts of persons convicted of DUI and sentenced to mandatory treatment, including mentions of alcohol or drug use disorders in an offenders’ medical records as well as any psychiatric comorbidity identified during the treatment process.

Results

• 92.9% of participants with bipolar disorder, 68.4% of those with depression, and 100.0% of those with OCD went undiagnosed for these disorders during treatment.
• The CIDI identified just over 10% of the sample as qualifying for a drug use disorder. Participants were overdiagnosed as having drug use disorders during treatment. Treatment records identified more than 25% as having drug use disorders; approximately 24.6% (adjusted) of defendants who were not identified as having a   drug use disorder on the CIDI were diagnosed with such during treatment.
  • The CIDI is a valid and reliable instrument for assessing mental health disorders in general populations; however, its validity has not been assessed in repeat DUI populations. There is no gold standard for diagnosis, and the CIDI “diagnosis” is a proxy just as are real-time clinician impressions for a patient’s “actual” mental health status. Absent a gold standard, we cannot determine whether the clinician or the CIDI are correct.
  • The CIDI was administered prior to treatment admission, and the records analyzed to determine disorders recognized during treatment included observations made later (i.e., during treatment), as well as records that potentially pre-dated CIDI administration. Therefore, mental health status might have been different at the time of each assessment. 
  • It is possible that the treatment programs’ patient records were not consistently maintained or accurately reflected clinicians’ diagnoses. Clinicians might have diagnosed patients consistently with the CIDI, but the diagnosis not properly recorded. Programs might not have had an alternative diagnosis record keeping system, but nevertheless made some diagnoses that were in line with the CIDI

Figure 1: A Comparison of Comorbid Psychiatric Disorders Identified by the CIDI and Treatment Records among Repeat DUI Offenders

Note. Cases identified through treatment as qualifying for a given disorder are not necessarily the same cases as those identified by the CIDI.

Limitations

• The CIDI is a valid and reliable instrument for assessing mental health disorders in general populations; however, its validity has not been assessed in repeat DUI populations. There is no gold standard for diagnosis, and the CIDI diagnosis is just as much of a proxy as real-time clinician impressions for a patient’s “actual” mental health status.
• The CIDI was administered prior to treatment admission, and the records analyzed to determine disorders recognized during treatment included observations made later (i.e., during treatment), as well as records that potentially pre-dated CIDI administration. Therefore, mental health status might have been different at the time of each assessment. 
• It is possible that the treatment programs’ patient records were not consistently maintained or accurately in line with clinicians’ diagnoses. Patients may have been diagnosed correctly by program therapists, but the diagnosis not properly recorded. Programs may not have had an alternative diagnosis record keeping system, but may have nevertheless made diagnoses that aligned with the CIDI.

Discussion
This study demonstrated that mental health disorders other than drug use disorders likely are underdiagnosed within DUI-related treatment. Underdiagnosis of comorbid psychiatric conditions during mandatory treatment likely stands in the way of improving outcomes for this patient population. Ignorance of complicating diagnoses might result in increased rates of relapse and recidivism in an already vulnerable population.  Future research efforts should focus on improving pathways to identifying and treating DUI offenders with comorbid conditions.
The Division on Addictions is in the process of developing and testing CARS, a computerized assessment and referral system that guides DUI program staff through a standardized mental health assessment with their clients. For more information about our efforts in this area, please visit www.divisiononaddictions.org.

-Katerina Belkin

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References

Lapham, S. C., Kapitula, L. R., C’de Baca, J., & McMillan, G. P. (2006). Impaired-driving recidivism among repeat offenders following an intensive court-based intervention. Acc Analys Prevent, 38, 162–169.

Lapham, S. C., C'De Baca, J., McMillan, G. P., & Lapidus, J. (2006). Psychiatric disorders in a sample of repeat impaired-driving offenders. J Stud Alcohol, 67(5), 707-713.

McMillan, G. P., Timken, D. S., Lapidus, J., C'De Baca, J., Lapham, S. C., & McNeal, M. (2008). Underdiagnosis of comorbid mental illness in repeat DUI offenders mandated to treatment. J Subst Abuse Treat, 34(3), 320-325.

Nelson, S. E., LaPlante, D. A., Peller, A. J., LaBrie, R. A., Caro, G., & Shaffer, H. J. (2007). Implementation of a computerized psychiatric assessment tool at a DUI treatment facility: a case example. Adm Policy Ment Health, 34(5), 489-493.

Robins, L. N., Helzer, J. E., Ratcliff, K. S., & Seyfried, W. (1982). Validity of the Diagnostic Interview Schedule, Version II: DSM-III diagnoses. Psychological Medicine, 12, 855– 870.

Shaffer, H. J., Nelson, S. E., LaPlante, D. A., LaBrie, R. A., Albanese, M., & Caro, G. (2007). The epidemiology of psychiatric disorders among repeat DUI offenders accepting a treatment-sentencing option. J Consult Clin Psychol, 75(5), 795-804.

Timken, D. S. (2001). TAB (Treatment Abstraction Form). Albuquerque, NM7 Behavioral Health Research Center of the Southwest.

The DRAM, Vol. 7(8) - Severe DUI Offenders: One Size Doesn’t Fit All

This week, the DRAM continues its series focusing on driving under the influence (DUI). This is the fourth of five issues concentrating on the body of recent DUI research. In the first three issues, DRAM Vol. 7(5), DRAM Vol. 7(6), and DRAM Vol. 7(7)  we addressed DUI-related trends, methods to detect heavy alcohol consumption and recidivism, and the use of ignition interlock devices to reduce DUI behavior.

DUI research suggests that DUI offenders exhibit an array of characteristics that distinguish them from the general population – mental health issues, personality characteristics, and criminal behavior, among others. It is tempting to conclude from this research that DUI offenders are a homogenous group characterized by these traits. However, this week the DRAM reviews recent research by Hubicka and colleagues (2010) suggesting that even among severe (i.e., defined in this study as having a BAC >=0.1 at time of arrest) DUI offenders, multiple profiles exist.

Methods

• Hubicka and colleagues (2010) recruited 163 (12% female) Swedish DUI offenders whose BAC was >=0.1 at time of arrest.
• The researchers assessed their sample using the following measures, all of which have general population norms available:
  • The Alcohol Use Disorders Identification Test (AUDIT):  A screen for alcohol-related problems.
  • The Revised Neuroticism-Extraversion-Openness Personality Inventory (NEO-PI-R): Assesses five major personality traits – extraversion, agreeableness, neuroticism, conscientiousness, and openness to experience.
  • The Symptom Checklist (SCL-90): A self-report measure of psychiatric symptoms across all major domains of mental illness.
  • Researchers also collected DUI arrest records for the two years following the study.
  • Researchers used cluster analysis to identify subtypes of DUI offenders grouped by personality, mental health, and alcohol use characteristics. They compared the rate of DUI relapse across these groups.

Results

• Severe DUI offenders exhibited significantly lower conscientiousness, higher agreeableness, and lower openness to experience than the general population, and scored significantly higher (i.e., higher = more problematic) than the general population on 8 of 9 mental health domains.
• Cluster analyses identified three DUI subtypes, presented in Table 1.

Table 1. Severe DUI Offender Subtypes (adapted from Hubicka, et al., 2010)

Group	AUDIT	NEO-PI-R	SCL-90	Relapse
Group 1 (N = 104; 64%)	High level of alcohol problems	Similar to general population	Similar to general population	9.3%
Group 2 (N = 50; 31%)	Very high level of alcohol problems	High neuroticism, low conscientiousness	Elevated mental health problems	12.6%
Group 3 (N = 8; 5%)	Very high level of alcohol problems	Very high neuroticism, low conscientiousness	Severe mental health problems	0.0%

• Group 3, arguably the group with the highest levels of psychopathology, was significantly less likely to relapse than the other two groups.

Limitations

• This study was conducted with severe DUI offenders in Sweden. These offenders and the Swedish general population might differ from United States’ DUI offenders and general population.
• Study involvement was voluntary and required the offenders to contact the researchers proactively to participate, leading to a potential selection bias that might bias the results.
• All measures other than DUI relapse were self-report; consequently, these data are vulnerable to self-report biases.

Discussion

Though repeat or severe DUI offenders reliably exhibit elevated mental health, personality, and substance use problems, the current research suggests that these risks are not evenly distributed. Indeed, the largest group (i.e., Group 1) differed from the general population only on the extent of alcohol use problems. The two more severe subtypes also had elevated alcohol use problems; one of these was characterized primarily by high neuroticism and low conscientiousness, and the other also had multiple mental health issues. Level of problems itself was not a predictor of relapse – the group with the most problems had the lowest relapse rate. Future research needs to investigate these subtypes longitudinally to determine how DUI develops in relation to these other problems; for example, whether the Group 3 type is using alcohol to self-medicate pre-existing mental health issues, or whether the Group 2 type is exhibiting DUI as part of a more general impulsive pattern.

-Sarah Nelson

What do you think? Please use the comment link below to provide feedback on this article.

References

Hubicka, B., Kallmen, H., Hiltunen, A., & Bergman, H. (2010). Personality traits and mental health of severe drunk drivers in Sweden. Social Psychiatry and Psychiatric Epidemiology, 45(7), 723-731.

The DRAM, Vol. 7(7) – Ignition interlock devices: The silver bullet for DUI?

This week, the DRAM continues its series focusing on driving under the influence (DUI). This is the third of five issues concentrating on the body of recent DUI research. In the first two issues, DRAM Vol. 7(5) and DRAM Vol. 7(6), we addressed DUI-related trends and methods to detect heavy alcohol consumption and recidivism.

Ignition Interlock Devices (IIDs) are gaining rapid popularity among State governments as tools to reduce DUI recidivism. Figure 1 shows the rapid increase in the number of IIDs installed in recent years. An IID is a dashboard-mounted device that requires an operator to provide a breath sample periodically. If the device detects more than a predetermined amount of alcohol, the car’s engine will not start. Today’s DRAM reviews a meta-analysis of 15 studies assessing the effectiveness of IIDs (Elder, Voas, Bierness, Shults, Sleet, Nichols & Compton, 2011).

Figure 1. The rapid increase in the number of IIDs installed (from Elder et al., 2011)

Methods

• Researchers identified 15 studies examining various IID programs around the US.
• Although programs differed in their exact protocols, all targeted high-risk offenders, were offered in conjunction with a reduced sentence, and required IID installation for a minimum period of time.

Results

• The studies that examined the effects of IID installation on recidivism all found that IID installation was associated with lowered rates of recidivism.

• Across studies, recidivism was 60%-75% lower for offenders with IIDs than for those without.
• The only randomized control trial of IIDs found a 63% decrease in alcohol-related re-arrest among the IID group compared to a conventional treatment group (Beck, Rauch, Baker, & Williams, 1999). It is important to note that only a small percentage of either group were re-arrested.

• No studies found evidence of long-term behavioral change after IID removal. In all studies that measured recidivism after IID removal, recidivism rates among those who had IIDs installed rebounded to pre-IID levels post-removal.

Limitations

• No studies monitored participants to ensure compliance with IID guidelines. Research suggests people can circumvent IIDs by providing a third-party breath sample or illegally driving a car without an IID (Marques & Voas, 2010).
• Only one of the 15 studies was a true randomized control trial. Additional randomized controlled trials will help further clarify the effects of IIDs on recidivism.

Discussion

These results suggest that IIDs are an effective tool against future DUI reoffense among recidivist DUI offenders while installed. However, the fact that IIDs are effective only while installed suggests that IIDs do not influence driving habits and behavior in the long-term, but rather reduce offenders’ access to vehicles after drinking. As a result, IIDs should be viewed not as a silver bullet, but as one tool to be used in conjunction with other tools, as part of a comprehensive strategy against DUI.

-Daniel Tao

What do you think? Please use the comment link below to provide feedback on this article.

References

Beck, K. H., Rauch, W. J., Baker, E. A., & Williams, A. F. (1999). Effects of Ignition Interlock License Restrictions on drivers with multiple alcohol offenses: A randomized trial in Maryland. American Journal of Public Health, 89(1), 1696-1700.

Elder, R. W., Voas, R., Beirness, D., Shults, R. A., Sleet, D. A., Nichols, J. L., & Compton, R. (2011). Effectiveness of Ignition Interlocks for preventing alcohol-impaired driving and alcohol-related crashes: A community guide systematic review. American Journal of Preventive Medicine, 40(3), 362-376.

Marques, P. R., & Voas, R. B. (2010). Key features for ignition interlock programs (No. DOT HS 811 262). Washington, DC: NHTSA.

The DRAM, Vol. 7(6) – Biomarker testing: Detecting heavy alcohol consumption and relapse among DUI offenders

This week, the DRAM continues its series focusing on driving under the influence (DUI). This is the second of five issues concentrating on the body of recent DUI research. In the first issue, DRAM Vol. 7(5), we discussed how different DUI-related events exhibit different trends across time.

DUI recidivism is a difficult phenomenon to prevent or limit. European countries have used biomarker tests to monitor DUI offenders since the 1980s (Morgan & Major, 1996). Biomarkers provide objective evidence for heavy drinking, a known factor associated with recidivism. This week, the DRAM reviews a study (Bean, Roska, Harasymiw, Pearson, Kay, & Louks, 2009) that examined whether biomarkers can (1) identify DUI repeat offenders who continue to drink heavily after arrest and (2) detect instances of heavy drinking during a 12-month follow-up.

Methods

• Participants were 200 third and fourth time repeat DUI offenders who provided a blood sample once every three months for 12 months as part of court-mandated assessments required to re-instate their licenses.
• Researchers used these blood samples to calculate Early Detection of Alcohol Consumption (EDAC) and Carbohydrate-Deficient Transferrin (CDT) scores.
  • EDAC compares an individual’s alcohol blood level to samples provided by prototypical heavy drinkers in past research. The results indicate the probability that the individual is a heavy drinker. In this study, a probability of 40% or higher was considered indicative of heavy drinking. Gaining or losing thirty points (e.g., from 40% to 70% or from 40% to 10%) signified relapse and remission, respectively.
  • CDT measures the percent of transferrin (i.e., a type of protein in the blood stream) that is carbohydrate-deficient. In this study, a score higher than 2.2% indicated heavy drinking. 

Results

• Biomarker tests identified 40 participants (20%) as heavy drinkers at baseline (i.e., first assessment after arrest); only 13 of those 40 admitted to drinking within the past 30 days. Investigation of the self-reports of individuals who reported drinking but did not meet biomarker cut-points for heavy drinking confirmed that these individuals reported low to moderate drinking.
• Figure 1 presents the baseline EDAC and CDT scores for three categories of drivers: 
  • Abstainer/Reducers (52%) included those who either were below biomarker cutpoints for heavy drinking at all assessments or decreased their drinking consistently throughout the follow-up. 
  • The relapse group (18%) included all drivers who did not decrease their drinking or remain abstinent according to biomarkers. As Figure 1 shows, this group had elevated baseline biomarker scores compared to the other groups.
  • The noncompliant group (30%) included all drivers who did not complete biomarker testing at the required follow-up periods. 

Figure 1. EDAC and CDT Scores at Baseline

Limitations

• These biomarker tests specifically target heavy drinkers, and therefore cannot identify instances of below-threshold alcohol consumption.
• Because biomarkers only detect heavy drinking within the past 2-3 weeks and researchers conducted follow-ups every three months, these tests might have had windows too small for adequate detection of heavy drinking between follow-ups in this study.
• The current study provides information about relapse to heavy drinking, but does not tell us whether these biomarkers are effective predictors of DUI recidivism.

Discussion 

Biomarker testing has been a reliable diagnostic tool in many European countries. This strategy also might be useful to incorporate into US DUI intervention programs. This study is the first in the US to measure relapse rates among DUI offenders using objective measures during follow-up. Figure 1 presents results that suggest baseline biomarkers might be important predictors of risk for continued heavy drinking, which in turn, might be associated with DUI recidivism. Expanded follow-up studies, such as a comparative exploration of various cutoff points for heavy drinking, might strengthen the efficacy of these measures within heterogeneous DUI populations. Such improvements might support the use of biomarker tests in the development of individualized driver safety plans.

-Aaron Lim & Sarah Nelson

What do you think? Please use the comment link below to provide feedback on this article.

References

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(6), 519-527.

Brown, T. G., Gianoulakis, C., Tremblay, J., Nadeau, L., Dongier, M., Kin, N., & ... Ouimet, M. (2005). Salivary cortisol: A predictor of convictions for driving under the influence of alcohol?. Alcohol and Alcoholism, 40(5), 474-481.

Marques, P., Tippetts, S., Allen, J., Javors, M., Alling, C., Yegles, M., & Wurst, F. (2010). Estimating driver risk using alcohol biomarkers, interlock blood alcohol concentration tests and psychometric assessments: Initial descriptives. Addiction, 105(2), 226-239.

Morgan, M. &  Major, M. (1996). The use of serum carbohydrate-deficient transferrin in the assessment of ‘high risk offenders’ in Great Britain. Alcohol Alcohol, 31(6), 625–628.

The DRAM, Vol. 7(5) – DUI on the Rise? It Depends Whom You Ask

This week, we begin a DRAM series focusing on driving under the influence (DUI). DUI is one of the leading public health threats related to addiction. The Division on Addictions receives funding from the Century Council, bwin Interactive AG, and the National Institutes of Health, among others. The Century Council funding supports our work to develop an assessment tool for use in DUI programs. As part of our work in this project, we have conducted a thorough review of recent research on the development of DUI behavior, its persistence, and effective prevention and intervention tools. The next five issues of the DRAM will focus on this body of recent DUI research and the new insights that have emerged from it. 

Introduction

Much of what we know about driving under the influence (DUI) behavior in the United States derives from statistics collected and analyzed by the National Highway Traffic Safety Administration (2010). However, most of the data presented annually by NHTSA involves alcohol-related motor vehicle (MV) fatalities. This week, the DRAM provides a review of DUI trends based on five data sources: motor vehicle fatality data, arrest records, national roadside surveys, self-report data, and passenger reports.  

Methods

• Alcohol-Related Motor Vehicle Fatalities
  • Data are derived from the NHTSA Traffic Safety Facts reports for 1993 through 2010 (NHTSA 1993-2010).
  • Figure 1 presents the percent of all motor vehicle fatalities in a year that involved an alcohol-impaired driver.
• DUI Arrests
  • Data are derived from the Federal Bureau of Investigation report on Crime in the United States: 2009 (FBI, 2010).
  • Figure 1 presents the percent of all arrests in a year that are for DUI.
• Roadside Surveys
  • Data are derived from the 2007 National Roadside Survey of Alcohol and Drug Use by Drivers (NHTSA, 2009b).
  • Figure 1 presents estimates of the percent of all Friday and Saturday nighttime drivers (10pm-3am) who have BACs over the legal limit.¹
• Self Report DUI Behavior
  • Data are derived from the Injury Control and Risk Survey (ICARIS) and the Behavioral Risk Factor Surveillance System (BRFSS), both nationally representative telephone surveys of US adults (Dellinger, Bolen, & Sacks, 1999;Quinlan et al., 2005; Shults, Kresnow, & Lee, 2009).
  • Figure 1 presents percent of adult drivers who report driving after having had too much to drink during the past month.¹
• Passenger Report DUI Behavior
  • Data are derived from the ICARIS survey (Dellinger, et al., 1999; Shults, et al., 2009).
  • Figure 1 presents the percent of adults who report riding with a driver who had too much to drink in the past month.¹

Results

• Figure 1 shows that different DUI events exhibit a diverse set of patterns across time. 

Figure 1: Rates of different DUI events over time.

Note. In 2001, the definition of an alcohol-impaired driver in an MV fatality changed from a BAC of .10 to .08, accounting for the steep increase in the alcohol-related MV fatality data between 2000 and 2001. 

• The most complete set of data available, for motor vehicle fatalities, shows that the percent of these fatalities that are alcohol-related has decreased steadily over time, but has remained stable for the past four years.  
• Roadside surveys suggest a steady decline in the percent of drivers over the limit across time.  
• DUI arrests have remained relatively stable across time, as have passenger estimates of DUI events. 
• Self-report estimates vary by sample but suggest that DUI behavior might again be on the rise. 

Limitations

• Motor vehicle fatality estimates and arrest data onlytell us about those who are involved in fatal accidents and those who are apprehended for DUI. This information demonstrates trends in DUI behavior, but it tells us little about the extent of the behavior; fewer than 1% of DUI drivers are arrested for their behavior (Centers for Disease Control, 2010), and far fewer DUI occasions result in fatal accidents.
• Roadside sobriety checkpoints reveal the percent of drivers over the legal BAC limit, but are constrained by time and location.
• Self reports likely underestimate DUI behavior and ambiguous phrasing (i.e., “too much to drink”) may elicit differentinterpretations of questions.
• Passenger reports avoid these self-report biases, but fail to account for solo DUI events.

Conclusions

DUI is a complex moving target. Each of the variety of methods used to measure this behavior captures a separate facet of DUI behavior and potentially a separate population segment of DUI offenders. For example, it appears that DUI arrests and DUI-related MV fatalities are no longer decreasing; it is possible that current efforts to decrease DUI behavior are not effective within this severe population. The continued decrease in alcohol-impaired drivers at sobriety checkpoints, however, is encouraging and suggests that prevention efforts continue to be effective among those who exhibit less severe DUI behaviors. More research is needed to clarify the varied self-report DUI behavior trends and their interpretation. 

-Sarah Nelson & Aaron Lim

What do you think? Please use the comment link below to provide feedback on this article.

Notes

¹ The percents derived from the National Roadside Survey and the ICARIS and BRFSS surveys are weighted to make them nationally representative. 

References 

Centers for Disease Control. (2010). Impaired driving: Get the facts.   Retrieved April 19th, 2006, 2001, from http://www.cdc.gov/Motorvehiclesafety/Impaired_Driving/impaired-drv_factsheet.html

Dellinger, A. M., Bolen, J., & Sacks, J. J. (1999). A comparison of driver- and passenger-based estimates of alcohol-impaired driving. Am J Prev Med, 16(4), 283-288.

Federal Bureau of Investigation. (2010). Crime in the United States: 2009. Crime in the United States  Retrieved Jan 18, 2011, from http://www2.fbi.gov/ucr/cius2009/data/table_33.html

National Highway Traffic Safety Administration. (1993-2006). Traffic safety facts: Alcohol. Retrieved May 12, 2011, from http://www-nrd.nhtsa.dot.gov/CATS/listpublications.aspx?Id=A&ShowBy=DocType

National Highway Traffic Safety Administration. (2007-2010). Traffic safety facts: Alcohol impaired driving.   Retrieved May 12, 2011, from http://www-nrd.nhtsa.dot.gov/CATS/listpublications.aspx?Id=A&ShowBy=DocType

National Highway Traffic Safety Administration. (2009b). Traffic safety facts research notes: Results of the 2007 National Roadside Survey of Alcohol and Drug Use by Drivers.   Retrieved May 24, 2010, from http://www.nhtsa.gov/Driving%2BSafety/Research%2B&%2BEvaluation/2007%2BNational%2BRoadside%2BSurvey%2Bof%2BAlcohol%2Band%2BDrug%2BUse%2Bby%2BDrivers

Quinlan, K. P., Brewer, R. D., Siegel, P., Sleet, D. A., Mokdad, A. H., Shults, R. A., et al. (2005). Alcohol-impaired driving among U.S. adults, 1993-2002. American Journal of Preventive Medicine, 28(4), 345-350.

Shults, R. A., Kresnow, M.-J., & Lee, K. C. (2009). Driver- and passenger-based estimates of alcohol-impaired driving in the U.S., 2001-2003. American Journal of Preventive Medicine, 36(6), 515-522.

The DRAM, Vol. 7(4) – The follies of youth? The relationship between age at alcohol initiation and future alcohol problems.

Adolescent drinking is an important problem in the US. The CDC (2010) estimates that 65% of children under 18 drank alcohol in 2009, with 52% consuming more than 12 drinks per year. But, how dangerous is alcohol to adolescents? Current research suggests that 11-14 year olds are especially vulnerable to physiological and environmental ‘shocks’, including stress and alcohol consumption (Grant & Dawson, 1998). Today’s DRAM reviews a study examining the link between age at alcohol initiation and future drinking problems (Guttmannova et al., 2011).

Methods

• This study utilizes data collected as part of the Seattle Social Development Project (SSDP).
  • The SSDP consists of 808 people, recruited as fifth graders in Seattle public schools serving high crime areas in 1985.
  • Participants reported their past-month drinking annually until the 12th grade, and every three years beginning at age 18. The SSDP has data through age 33.
  • Researchers recorded the age when participants first consumed any alcohol and when they initiated regular drinking.
    • ‘Any alcohol consumption’ was defined as self-reported drinking “more than as sip or two” of alcohol on any occasion.
    • ‘Regular drinking’ was defined as self-reported drinking on three or more occasions per month.
• Of this sample, 692 (86%) initiated any alcohol consumption before age 21 and had complete alcohol consumption data through age 33.
• Researchers used the Diagnostic Interview Schedule (DIS) to assess the consequences of drinking based on the DSM-IV diagnostic criteria.
  • Participants endorsing one or two items on an assessment were considered alcohol misusers. 
  • Participants endorsing three or more items on any one assessment were considered alcohol dependent. 
  • Researchers created models to predict risk of adult alcohol misuse and alcohol dependence based on age of any drinking and age of regular drinking.
    • Researchers divided participants into groups that initiated any drinking before age 11, from age 11 to 14, from age 15 to 17, from age 18 to 20, or at age 21 or older.
      • Only five participants (0.7%) initiated regular drinking prior to age 11; for analysis, researches collapsed to two youngest groups into a single 14 or younger group.
• Participants completed the DIS five times starting from age 21 through age 33.
• Models controlled for socio-economic status, demographics and other drug use.

Results

• Participants who began regular alcohol consumption from ages 15 through 17 (Odds ratio [OR] = 1.71, p < .05) and 18 through 20 (OR = 1.85, p < .05) were at significantly greater risk of developing lifetime alcohol dependence than those initiating regular drinking at 21 or later.
• Neither initiation of any alcohol use at any age nor initiation of regular alcohol use before age 14 related significantly to future alcohol misuse or dependence.

Figure 1: Odds of adult alcohol dependence versus age at initiation of regular drinking.

* indicates p < .05

Limitations

• The SSDP measured self-reported past-month alcohol consumption. Depending on the month administered, this method might have led to an over- or underestimation of alcohol consumption. The self-report nature of the data might also have introduced bias.
• Guttmannova et al. did not track the number of drinks consumed beyond ‘any’ and ‘regular.’ Thus, the relationship between heavy or binge drinking and future problems is undetectable.  Furthermore, the survey did not distinguish among the types of beverages. 

Conclusions

These results do not support the hypothesis that children who initiate drinking from ages 11 through 14 are especially vulnerable to future alcohol problems. Instead, it seems that the initiation of regular drinking at any age from 15 through 21 might predispose children to future alcohol dependence. It is unclear why regular alcohol initiation before age 15 does not significantly elevate the risk for developing alcohol dependence. It is possible that those aged 14 or younger might have fewer drinking opportunities. For example, alcohol might be difficult to acquire and social pressure to drink might be less than for older counterparts. This situation can result in less chance to experience the pleasurable aspects of drinking. Furthermore, early adverse experiences with alcohol might discourage further drinking. This discrepancy between the current study and past work might reflect the retrospective nature of past studies compared to the prospective design of this study. Future research might investigate whether this effect is due to psychological, social, biological or a combination of causes.

-Daniel Tao

What do you think? Please use the comment link below to provide feedback on this article.  

References

Grant, B. F., & Dawson, D. A. (1998). Age of onset of drug use and its association with DSM-IV drug abuse and dependence: Results from the National Longitudinal Alcohol Epidemiologic Study. Journal of Substance Abuse, 10(2), 163-173.

Centers for Disease Control and Prevention. (2010). Youth Risk Behavior Surveillance - United States, 2009 (No. SS-5). Atlanta, GA: Centers for Disease Control and Prevention,.

Guttmannova, K., Bailey, J. A., Hill, K. G., Olivia, J., Hawkins, J. D., Woods, M. L., et al. (2011). Sensitive periods for adolescent alcohol use initiation: Predicting the lifeime occurrence and chronicity of alcohol problems in adulthood. Journal of Studies on Alcohol and Drugs, 72(2), 221-231.

The DRAM, Vol. 7(3) – Adolescent drinking and risk of benign breast disease

Benign breast disease (BBD) is a risk factor for breast cancer, and evidence indicates that adult alcohol intake among postmenopausal women increases risk of BBD and breast cancer (e.g., Longnecker, Berlin, Orza, & Chalmers, 1988; Zhang, Lee, Manson, Cook, Willett, & Buring, 2007).  Further, young adult alcohol consumption (i.e., ages 18-22) is associated with BBD later in life (Bryne, et al., 2002).  This month, the DRAM reviews the first prospective examination of the relationship between adolescent alcohol consumption and BBD (Berkey et al., 2010).

Methods

• The Growing Up Today Study (GUTS) began in 1996.
  • A nationwide sample of 9,037 girls, age 9 to 15, who were daughters of Nurses’ Health Study II participants, was enrolled at baseline.
• The participants completed questionnaires annually from 1996 through 2001, as well as follow-up surveys in 2003, 2005, and 2007.
• The 2003 survey, administered when the subjects were 16 to 23 years old, provided information about their alcoholic beverage consumption in the previous year, specifically:
  • Drinking frequency – how often they consumed alcohol
  • Drinking quantity – how much alcohol they consume on drinking occasions
  • Binge drinking – how often they consume 4+ drinks within a few hours
  • 6,899 women completed both the 2005 and 2007 surveys, administered when the women were 18 to 27 years old. These surveys indicated whether a health care provider had ever diagnosed them with BBD and whether it was confirmed by biopsy."
  • Berkey et al. (2010) conducted logistic regression analyses to examine the association between BBD and adolescent alcohol consumption.  All models were adjusted for body mass index (BMI) and exact age (to the month).

Results

• Sixty-seven (1.0%) of the 6,899 women included in analysis were biopsy-confirmed BBD cases. 
• Table 1 presents alcohol consumption data for BBD and non-BBD cases. 
• The primary logistic regression analysis, controlled for age, found that risk for BBD increased with more frequent alcohol consumption; frequency appeared more important than quantity.

Table 1: Alcohol Consumption in 2003 by BBD Case Status (adapted from Berkey et al., 2010)

	No BBD (n = 6752)	BBD (Biopsy Confirmed) (n = 67)	Risk for BBD (Odds Ratio from Age adjusted Model [95% CI])
Average age at alcohol report in 2003	19.3	20.0
Drinking frequency (%)
< once/week	75.9	54.9	1.0 (Referent)
1-2 days/week	18.7	27.5	1.6 (0.8-3.1)
3-5 days/week	4.8	13.7	3.0 (1.3-7.1)*
6-7 days/week	0.7	3.9	5.5 (1.2-24.5)*
Drinks consumed / drinking occasion	2.4	2.8	1.1 (0.9-1.2)
Drinks/day (frequency*quantity)	0.4	0.8	1.5 (1.2-1.9)*
# of bingeing occasions, past year	4.7	6.4	1.0 (1.0-1.1)
Binge = 12 times during previous year, (%)	20.9	42.3	1.5 (0.8-2.9)

Model adjusted for age and BMI.
*p < .05

Limitations

• Investigators relied on participants' self-reports of their alcohol consumption.
• Because little is known about childhood/adolescent risk factors for BBD, the authors could not exclude the possibility that there are other confounding variables.
• The study participants were daughters of nurses; thus, they might not be representative of US women.

Conclusions

This study was the first to examine the relationship between alcohol intake during adolescence and BBD; the results are consistent with previous retrospective studies finding a relationship between BBD and alcohol consumption at later ages (e.g., Longnecker et al., 1988; Zhang et al., 2007). Specifically, this study found a positive association between alcohol consumption (frequency, and to a lesser extent, quantity) and subsequent BBD.  However, it is important to note that among the women with confirmed cases of BBD, over 50% reported that they abstained from alcohol or drank infrequently. Future research should continue to explore risk factors for BBD, including alcohol intake.

-Matthew Hojatzadeh (Matthew is a special contributor to the BASIS and a graduate student at East Carolina University)

What do you think? Please use the comment link below to provide feedback on this article.

References

Berkey, C., Willett, W., Frazier, L., Rosner, B., Tamimi, R., Rockett, H., & Colditz, G. (2010). Prospective Study of Adolescent Alcohol Consumption and Risk of Benign Breast Disease in Young Women. Pediatrics, 125(5). 1081-1087.

Bryne, C., Webb, P., Jacobs, T., Peiro, G., Schnitt, S., Connolly, J., Willett, W., & Colditz, G. (2002). Alcohol Consumption and Incidence of Benign Breast Disease. Cancer Epidemiology Biomarkers Preview, 11(11). 1369-1374.

Longnecker, M., Berlin, J., Orza, M., & Chalmers, T. (1988). A Meta-Analysis of Alcohol Consumption in Relation to Risk of Breast Cancer. JAMA, 260(5). 652-656.

Vachon, C., Kushi, L., Cerhan, J., Kuni, C., & Sellers, T. (2000). Association of Diet and Mammographic Breast Density in the Minnesota Breast Cancer Family Cohort. Cancer Epidemiology, Biomarkers and Prevention, 9(2). 151-160.

Zhang, S., Lee, I., Manson, J., Cook, N., Willett, C., & Buring, J. (2007). Alcohol Consumption and Breast Cancer Risk in the Women's Health Study. American Journal of Epidemiology, 165(6). 667-676.

The DRAM, Vol. 7(2) - A Binge Influence: Findings from the College Alcohol Study (CAS) highlight the role of college environment in students' drinking habits

Half of college binge drinkers adopt these bingeing behaviors before entering college, the other half develop this pattern during college (Weitzman et al. 2003b) and, despite the large variation of bingeing rates across colleges (ranging from 1% to 76%), within-college rates have remained constant for more than a decade (Wechsler et al., 2002b). College environmental factors likely play an important role in the problem drinking of this high-risk group. This week’s DRAM summarizes the key findings about the influence of environmental factors and students’ bingeing patterns from a review of the landmark Harvard School of Public Health’s College Alcohol Study (CAS) and also discusses the implications for drinking-related preventions and interventions among college students (Wechsler & Nelson, 2008).

Methods

• Participants (n ~ 50,000) were college students recruited as part of a nationally representative sample of 120 4-year colleges across the United States.
• Researchers distributed four national surveys to college student participants, first in 1993, and again in 1997, 1999, and 2001.
• These surveys assessed college students’ alcohol consumption and related drinking behaviors. The surveys contained the following measures of alcohol consumption:
  • Binge drinking- defined as five or more consecutive drinks for men and four or more drinks in a row for women (this is known as the “Five/Four Rule”) on one or more occasions during the two weeks prior to completion of the survey
  • Frequent binge drinking- bingeing on three or more occasions during the two weeks prior to the survey
  • Number of drinking occasions during the past 30 days
  • Number of drunken occasions during the past 30 days
  • Usual number of drinks consumed during each occasion
• Researchers also obtained information on the contextual factors of each college environment.

Results

• Most alcohol-related injuries (53%) and adverse consequences occurred among students who satisfied the Five/Four Rule.  Among respondents who reported alcohol-related injuries 21% consumed eight or more drinks in a row.
• The following college-level contextual factors influenced the prevalence of students’ problem drinking:
• College residence: As Table 1 demonstrates, underage students living at home had the lowest rates of alcohol consumption. The highest rate was among students living in fraternities and sororities.

Table 1. Underage Students' Alcohol consumption by college residence (n=4,231) (adapted from Wechsler et al., 2002b)

   

• Demographics of the student body: Specifically, higher percentages of racial and ethnic diversity, females, and older students were correlated with lower rates of binge drinking among students, even after controlling for survey year, gender, underage status, and race as Table 2 shows (Wechsler & Kuo, 2003).

Table 2. Correlation between college students' binge drinking rates and demographic distribution across all four survey years (adapted from Wechsler & Kuo, 2003)

 

• Volunteerism: In multilevel multivariate analyses that controlled for individuals’ levels of volunteering, the level of student volunteerism, or social capital, was associated inversely with alcohol use and drinking-related consequences (Weitzman and Chen, 2005). As shown in Table 3, the odds for students with social capital favor the absence of the risky behavior (all ORs less than 1.0).

Table 3. Reduced risks for students enrolled in colleges with higher levels of social capital (adapted from Weitzman & Chen, 2005)

  
   Notes: OR = Odds ratio

• Campus drinking policy: College students attending “dry” campuses that ban alcohol had a higher frequency of abstainers, but the students who drank at these schools drank at high levels similar to those in colleges without the alcohol ban.

Limitations

• Students’ response rates declined across the four national surveys and also varied among colleges within each survey year, so the sample was not consistent.
• Additionally, a small number of students were sampled within each college, raising the possibility that these estimates do not accurately represent each of the schools.
• Lastly, the survey included only self-report measures of alcohol consumption.  Future research might benefit from the inclusion of other independent and perhaps more objective measures of alcohol use among this high-risk group.

Discussion

The findings that environmental factors play a key role in predicting college students’ drinking and consequences suggest the need for a broader, community approach to addressing college drinking. For example, prevention efforts that target the unique college environment (e.g., evaluating traditions and college drinking policies) as well as other factors relating to students’ exposure to alcohol (e.g., aggressive marketing, number of liquor stores, availability of cheap alcohol) might be more effective than targeting individual behaviors of the heaviest drinkers.

-Brittany Bannon 

What do you think?  Please use the comment link below to provide feedback on this article.

 References

Wechsler, H., & Nelson, T. F. (2008). What we have learned from the Harvard School of Public Health College Alcohol Study: Focusing attention on college student alcohol consumption and the environmental conditions that promote it. Journal of Studies on Alcohol and Drugs, 69(4), 481-490.

Wechsler, H., Lee, J. E., Kuo, M., Seibring, M., Nelson, T. F., & Lee, H. (2002b). Trends incollege binge drinking during a period of increased prevention efforts: Findings from 4 Harvard School of Public Health College Alcohol Surveys: 1993-2001. Journal of American College Health, 50, 203-217.

Wechsler, H., & Kuo, M. (2003). Watering down the drinks: The moderating effect ofcollege demographics on alcohol use of high-risk groups. American Journal of      Public Health, 93, 1929-1933.

Weitzman, E. R., & Chen, Y. Y. (2000). Risk modifying effect of social capital on measure of heavy alcohol consumption, alcohol abuse, harms, and secondhand effects:      National Survey Findings. Journal of Epidemiology and Community Health, 59,            303-309.

Weitzman, E. R., Nelson, T. F., & Wechsler, H. (2003b).  Taking up binge drinking in college: The influence of person, social group, and environment. Journal of           Adolescent Health, 32, 26-35.