The Brief Addiction Science Information Source (BASIS)

The DRAM, Vol 4(3) - Famous Last Words: It’s Only a Few Miles

One drinking myth is that people who drink more can handle their alcohol better than those who drink less. This fallacy might provide heavier drinkers with the opportunity to rationalize driving while under the influence of intoxicants.  This week’s DRAM reviews a study assessing actual and perceived impairments in psycho-motor functioning among a group of heavy and light drinkers.

The Chicago Social Drinking project, a longitudinal study, recruited participants through newspaper advertisements, fliers, and word of mouth.  Brumback, Cao, and King (2007) derived their sample of 21-35 year olds from the Chicago project sample. The sample of 132 (77 HD and 55 LD) research participants was 66% white and 53% female. Investigators identified participants as either light or heavy drinkers using two measures (The Quantity-Frequency Index; Callhalan, Cisin, & Crossley, 1969; Timeline Follow-Back Interview; Sobell & Sobell, 1995). The authors used previous studies (King & Byars, 2004; King & Epstein, 2005) to operationally define heavy social drinkers (HD) as those, who for at least the last two years before the study, consumed >10 alcoholic drinks weekly and had occurrences of binge drinking 1-5 times per week .  They defined light social drinkers (LD) as those, who for the last two years before the study, consumed <6 drinks per week with rare or no binge drinking.  The authors defined binge drinking as >5 drinks in a single occasion for males, and >4 for females.

To assess alcohol-related impairment, the authors first measured perceptual motor speed with the Digit Symbol Substitution Test (DSST, from WAIS-R; Wechsler, 1981), and motor speed and coordination with the Grooved Pegboard (Lafayette Instruments, Lafayette, IN).  Next, the participants consumed one 08g/kg, 190 proof ethanol beverage. After 15 minutes, the researchers re-administered the same two impairment measures, tested participants for blood alcohol concentration (BAC), and asked participants to report subjective measures of impairment: the degree of their overall impairment, how unsafe it would be to drive at the present, and whether others could detect impairment.

Figure 1. Mean scores of self rated impairment items 15 minutes after drinking alcoholic beverage.

Items rated on a 10 point scale: 0=not at all; 10=extremely.
Note: **p<0.001; *p<0.01. Adapted from Brumback et al. (2007)

BAC levels 15 minutes after drinking were 0.074g/dl for LD and 0.084g/dl for HD, a statistically but not clinically significant difference (0.08 is the legal limit in the US; Highway Loss Data Institute, 2008). After drinking, both HD and LD performed significantly worse on impairment measures, and both HD and LD were impaired equally (i.e., main effect and interaction terms were not significantly different for either Pegboard or DSST tasks). Figure 1 shows that HD were more likely than LD to self-report less subjective impairment, feeling safer driving, and that others would be less likely to detect their impairment.

The social implications of the data are limited because scores on neither the objective nor subjective tests measure actual functional impairment (i.e., driving) or the decision to drive. However, these findings suggest that after having adapted to the subjective effects of alcohol, heavy drinkers might require higher levels of consumption to achieve the same subjective effect once achieved at a lower dose. In this study both LD and HD had equal doses, but, according to a neuroadaptation model, the HD would report less impairment; the findings did support this notion. HD group members perceived themselves to be less intoxicated than the LD group. Because psychomotor impairments were similar between the two groups, the belief that those who drink more can handle their alcohol better than lighter drinkers is indeed a myth: risks for driving drunk are the same for all types of drinkers. What seems to be different is the ability of heavy drinkers to recognize their level of intoxication. This means that those who are at greater risk for deciding to drive while intoxicated are also those who tend to drink more heavily.

What do you think?  Comments can be addressed to Leslie Bosworth.

References

Brumback, T., Cao, D., & King, A. (2007). Effects of alcohol on psychomotor performance and perceived impairment in heavy binge social drinkers. Drug and Alcohol Dependence, 91, 10-17.

Callhalan, V., Cisin, I., & Crossley, H. M. (1969). American drinking practices: A national study of drinking behavior and attitudes. New Brunswick, NJ: Rutgers Center for Alcohol Studies.

Highway Loss Data Institute. (2008). DUI/DWI laws.   Retrieved January 15, 2008, from http://www.iihs.org/laws/dui.aspx

King, A. C., & Byars, J. (2004). Alcohol induced performance impairment in heavy episodic and light social drinkers. Journal of Studies of Alcohol, 65, 27-36.

King, A. C., & Epstein, A. M. (2005). Alcohol dose-dependent increase in smoking urge in light smokers. Alcoholism: Clinical and Experimental Research, 29, 547-552.

Sobell, L. C., & Sobell, M. B. (1995). Alcohol timeline follow-back users' manual. Toronto, Canada: Addiction Research Foundation.

Wechsler, D. (1981). WAIS-R Manual: Wechsler Adult Intelligence Scale-Revised. New York: Harcourt, Brace, & Jovanovich.

The DRAM Vol. 4(2) - Gender and Drinking – Maybe We Aren’t so Different After All

Researchers have found men to be at greater risk for developing alcohol use disorders than women (Grant, 1997; Warner, Kessler, Hughes, Anthony, & Nelson, 1995); however, with the shift away from traditional gender roles in the United States (e.g., an increased proportion of women working outside the home, a decreased proportion of women bearing children; Thronton & Freedman, 1983), some have observed a trend toward convergence of male and female drinking patterns (Greenfield & Room, 1997). This week’s DRAM reviews a national study about the changes in drinking customs and the prevalence of alcohol disorders among men and women in the United States.

Keyes, Grant, and Hasin (2008) analyzed data from the 2001-2002 National Epidemiologic Survey on Alcohol and Related Conditions (NESARC), in which professional interviewers conducted face-to-face interviews with 42,693 civilian non-institutionalized participants aged 18 years and older. Participants responded to the Alcohol Use Disorder and Associated Disabilities Interview Schedule (AUDAIS-IV; a survey that contains over 40 questions used to evaluate alcohol use disorders). The participants reported the largest number of drinks they consumed during their period of heaviest drinking, and reported their frequency of binge drinking (i.e., 5+ drinks once per week or more often during period of heaviest drinking).  To study gender differences in drinking behavior over time, the researchers divided participants into four birth cohorts: People in Cohort 1 were born in the years from 1913 through 1932; Cohort 2 from 1933 -1949; Cohort 3 from 1950-1967; and Cohort 4 from 1968-1984.  The researchers divided Cohorts 2 and 3 at 1950 because previous findings indicated a greater risk of developing alcohol disorders among those born after 1950 (Johnson & Gerstein, 1998).  They then divided Cohorts 3 and 4 at 1968 to specifically examine those who entered the period of risk for alcohol disorders after the 1980s (i.e., the peak of per capita alcohol consumption in the United States; Lakins, Williams, Hsiao-Ye, & Hilton, 2005).

Overall, men consumed a larger average number of drinks in one sitting (6.94) than women (2.98) during their period of heaviest drinking (a male-to-female ratio of 2.3:1).  Cohort analyses suggest that consumption differences between men and women narrowed over time: from Cohort 1 to Cohort 4 the male-to-female ratio dropped from 2.9:1 to 2.1:1.  Following the peak in per capita alcohol consumption in the United States, the prevalence of alcohol dependence increased from 14.4% to 17.2%, while the prevalence of alcohol abuse decreased from 22.1% to 14.3%.  However, Figure 1 shows that the decline in alcohol abuse among females was significantly smaller than that among males (differences of 5.3% and 10.4%, respectively). This trend narrowed the difference in prevalence of alcohol disorders between genders. 

Figure 1 Prevalence of lifetime DSM-IV alcohol abuse and dependence by gender and birth Cohorts 3 and 4

The study is subject to the typical limitations of a cross-sectional design, such as recall bias.  Further, cross-section designs are not ideal for interpreting trends. To illustrate, participants in the older cohorts might have had difficulty remembering their drinking behaviors, resulting in an underestimation of the prevalence of alcohol dependence among the older cohorts.  Although these discrepancies make it difficult to compare drinking behavior by cohort, they should not confound the comparisons of interest between the genders.   

Keyes, Grant and Hasin’s findings, that the differences in male and female drinking patterns are diminishing, provide support for the findings of earlier researchers (e.g., Johnson & Gerstein, 1998).  The significantly smaller decrease in alcohol abuse among women in Cohorts 3 and 4 suggests that public health workers place increased emphasis on prevention efforts that target women.  It is important that we continue to study these trends to gain an improved understanding about why the gender gap in alcohol disorders is narrowing, and to assure appropriate treatment for the individuals in need of treatment.

What do you think?  Comments can be addressed to Sara Kaplan.

References

Grant, B. F. (1997). Prevalence and correlates of drug use and DSM-IV alcohol dependence in the United States:  Results of the National Longitudinal Alcohol Epidemiologic Survey. Journal of Studies on Alcohol 58, 464-473.

Greenfield, T. K., & Room, R. (1997). Situational norms for drinking and drunkenness:  Trends in the US adult population, 1979-1990. Addiction, 92, 33-47.

Johnson, R. A., & Gerstein, D. R. (1998). Initiation of use of alcohol, cigarettes, marijuana, cocaine, and other substances in US birth cohorts since 1919. American Journal of Public Health, 88(1), 27-33.

Keyes, K. M., Grant, B. F., & Hasin, D. S. (2008). Evidence for a closing gender gap in alcohol use, abuse and dependence in the United States population. Drug and Alcohol Dependence, 93, 21-29.

Lakins, N., Williams, G. D., Hsiao-Ye, Y., & Hilton, M. E. (2005). Apparent per capita alcohol consumption:  National, state, and regional trends, 1977-2003. Rockville, Maryland NIAAA, Division of Biometry and Epidemiology, Alcohol Epidemiologic Data System.

Thronton, A., & Freedman, D. (1983). The changing American family. Population Bulletin, 39, 1-44.

Warner, L. A., Kessler, R. C., Hughes, M., Anthony, J. C., & Nelson, C. B. (1995). Prevalence and correlates of drug use and dependence in the United States: results from the National Comorbidity Survey. Archives of General Psychiatry, 52(3), 219-229.

The DRAM Vol. 4(1) - Alcohol Use and HIV Risk in South Africa

Residents of sub-Saharan Africa comprise 60% of the 41 million people in the world with acquired immune deficiency syndrome (AIDS; World Health Organization and UNAIDS, 2004); about 18.8% of South African adults have human immunodeficiency virus (HIV; World Health Organization, 2006).  Changing behaviors that increase the risk of HIV exposure, such as risky consumption of alcohol, might improve the public health in this region.  This week’s DRAM reviews a study examining the relationships among alcohol-sex outcome expectancies, alcohol use in sexual contexts, sensation seeking, and HIV risk behaviors within a population of South African sexually transmitted infection (STI) clinic patients (Kalichman, Simbayi, Jooste, Cain, & Cherry, 2006)

The men (292) and women (219) receiving STI services from a clinic in Cape Town anonymously reported demographic information, drug use history, and responses to the Alcohol Use Disorder Identification Test (AUDIT; Saunders, Aasland, Barbot, de la Fuente, & Grant, 1993).  In addition, these respondents completed culturally-adapted versions of The Sensation Seeking Scale (Kalichman et al., 1994) and an alcohol-sex outcome expectancy scale (Kalichman & Simbayi, 2003).  The participants also reported HIV risk-related behaviors, such as unprotected sex, number of sex partners, and injection drug use.  The authors created a composite index of these behaviors.  The authors tested correlations among these measures.

Table 1. Correlations among sensation seeking, alcohol-sex outcome expectancies, alcohol use in sexual contexts, and HIV risk index scores

The sample was 98% indigenous African, 90% were unmarried, 87% had a high school education or less, 58% were unemployed, and 45% were under 25 years of age.  About 64% of men and 23% of women met criteria for problem drinking according to their AUDIT scores.  Controlling for marital status and gender, those with drinking problems scored higher than those without such problems on sensation seeking, alcohol-sex expectancy, and more HIV risk factors.  Problem drinkers were not more likely to have HIV, but only half had ever been tested.  Table 1 shows that HIV risk was associated positively with all three variables, and sensation seeking was significantly related to alcohol use within the context of sex, which was related to HIV risk.

Three factors limited the study methodologically.  The study used retrospective self-report.  Participants might have reported inaccurately past information about sex and substance use.  Second, correlations prohibit determining which variable causes another.  Finally, the authors only sampled one public health clinic within one geographic area, so these results might not generalize to other locations or to other populations.

A number of variables surrounding alcohol consumption reflect HIV risk behaviors.  Although we cannot assume causality from the data, providing education about the dangers of drinking in sexual contexts might lower alcohol-sex outcome expectancies and the risk of HIV, especially for sensation seekers.  Effective efforts to reduce incidence of HIV in South Africa can expand past targeting just safer sex practices, to targeting behaviors, personalities, and contexts that can compromise these practices.

What do you think?  Comments can be addressed to Leslie Bosworth.

References

Kalichman, S. C., Johnson, J. R., Adair, V., Rompa, D., Multhauf, K., & Kelly, J. A. (1994). Sexual sensations seeking: Scale development and predicting AIDS-risk behavior among homosexually active men. Journal of Personality and Assessment, 62, 385-397.

Kalichman, S. C., & Simbayi, L. C. (2003). HIV testing attitudes, AIDS stigmas, and voluntary HIV counseling and testing in the Western Cape, South Africa. Sexually Transmitted Infections, 79, 442-447.

Kalichman, S. C., Simbayi, L. C., Jooste, S., Cain, D., & Cherry, C. (2006). Sensation seeking, alcohol use and sexual behaviors among sexually transmitted infection clinic patients in Cape Town, South Africa. Psychology of Addictive Behaviors, 20(3), 298-304.

Saunders, B. J., Aasland, O. G., Barbot, T. F., de la Fuente, J. R., & Grant, M. (1993). Development of the alcohol use disorders identification test (AUDIT): WHO collaborative project on early detection of persons with harmful alcohol consumption: II. Addiction, 88, 791-804.

World Health Organization. (2006). Epidemiological Fact Sheets on HIV and Sexually Transmitted Infections: South Africa. Geneva, Switzerland: World Health Organization.

World Health Organization and UNAIDS. (2004). AIDS epidemic update. Geneva, Switzerland: World Health Organization.

The DRAM, 3(11) - Childhood Sexual Abuse, Age of First Drink, and Onset of Alcohol Dependence

Studies linking childhood sexual abuse (CSA) to higher likelihood of alcohol dependence (AD) later in life (i.e., Dinwiddie et al., 2000; Fergusson, Horwood, & Lynskey, 1996; Kendler et al., 2000; Kilpatrick et al., 2000; Molnair, Buka, & Kessler, 2001; Nelson et al., 2002) might lack adequate control for environmental and genetic factors that influence the relationship between CSA and AD.  These studies are also inconclusive because an earlier age of first drink is associated with later AD regardless of CSA status (DeWit, 2000).  This week’s DRAM reviews a study that controls for the influence of environmental and genetic characteristics, as well as the influence of drinking at an early age using a sample of twins. 

Sartor, Lynskey, Bucholz, McCutcheon, Nelson, Waldron, et al. (2007) conducted telephone surveys with 2061 (response rate 78%) 18-29 year old female monozygotic or dizygotic twin sets participating in the Missouri Adolescent Female Twin Study (Heath et al., 2002).  The survey included the Semi-Structured Assessment for the Genetics of Alcoholism (SSAGA; Bucholz, 1994; Hesselbrock, 1999), age at first drink, age of onset of AD, and Traumatic Events and Early Childhood Experiences.  Participants met researchers’ criteria for CSA if they endorsed having experienced rape, sexual molestation, and/or forced sexual contact before age 16, or sexual contact by someone five or more years older before they reached age 16.  The authors measured speed of progression to AD by measuring age at first drink and age at onset of AD. 

Table 1.  Alcohol use measures adjusted for environmental, genetic, or early age of first drink correlates among those with CSA history.

   

* Significant
** OR = Odds ratio, HR = Hazard ratio
Adapted from Sartor, et al. (2007)

The sample was 86% Caucasian and 14% African American.  Almost 12% endorsed a history of CSA and 7.9% qualified for AD.  The authors adjusted for the genetic influence expressed by the higher risk for AD if her twin had AD, especially if they were monozygotic twins.  Table 1 shows that, after adjusting for genetic factors, CSA was associated with a greater likelihood of lifetime alcohol use, having a first drink at an early age, and AD.  However, the effect of CSA on AD was mediated by age at first drink. After adjusting for the genetic influence on AD and the CSA-related age at first drink, CSA history did not increase the speed of AD onset from 1st drink.

The study was based on self-report and defines CSA narrowly; these factors might lead to under reporting CSA.  Rather than compare sets of twins where only one female experienced CSA, researchers group all twins together, and then statistically controlled for differences between twin pairs.  This yields a larger sample size and, therefore, statistical significance for smaller differences.  The study did not report the rate of co-occurrence in twin pairs of CSA and AD, and congruence across twins. Adjusting for AD co-occurrence might adjust for CSA co-occurrence if congruence is common.  The results might not generalize well to the general population because a very small subset of the overall sample had both a history of CSA and AD; the entire sample was female, and most of the sample was Caucasian.

Contrary to expectation, CSA did not predict time to AD after adjusting for age at first drink.  Drinking at an early age predicted a more rapid pace for AD development; however, both twins having AD was the best predictor of rapid onset of AD after their first drink, suggesting genetics and environment are greater risk factors for this effect than CSA.  Future research could examine an early intervention’s effectiveness for reducing rates of later AD among early adolescents reporting CSA history. 

What do you think?  Comments can be addressed to Leslie Bosworth.

References

DeWit, D., Adlaf, E.M., Offord, D.R., Ogborne, A.C. (2000). Age at first use: a risk factor for the development of alcohol use disorders. American Journal of Psychiatry, 157, 745-750.

Dinwiddie, S., Heath, A. C., Dunne, M. P., Bucholz, K. K., Madden, P. A. F., Slutske, W. S., et al. (2000). Early sexual abuse and lifetime psychopathology: a co-twin control study. Psychological Medicine, 30, 41-52.

Fergusson, D. M., Horwood, L. J., & Lynskey, M. T. (1996). Childhood sexual abuse and psychiatric disorders in early adulthood.  II.  Psychiatric outcomes of childhood abuse. Journal of the American Academy of Childhood and Adolescent Psychiatry, 35, 1365-1374.

Heath, A. C., Howells, W., Bucholz, K. K., Glowinski, A. L., Nelson, E. C., & Madden, P. A. (2002). Ascertainment of a mid-western U.S. female aldolescent twin cohort for alcohol studies: assessment of sample representativeness using brither record data. Twin Research, 5, 107-112.

Kendler, K. S., Bulik, C. M., Silberg, J., Hettema, J. M., Myers, J., & Prescott, C. A. (2000). Childhood sexual abuse and adult psychiatric and substance use disorders in women.  An epidemiological and co-twin control analysis. Archives of General Psychiatry, 57, 953-959.

Kilpatrick, D. G., Acierno, R., Saunders, B., Resnick, H. S., Best, C. L., & Schnurr, P. P. (2000). Risk factors for adolescent substance abuse and dependence: data from a national sample. Journal of Consulting and Clinical Psychology, 68, 19-30.

Molnair, B. E., Buka, S. L., & Kessler, R. C. (2001). Childhood sexual abuse and the subsequent psychopathology: results from the National Comorbidity Study. American Journal of Public Health, 91, 753-760.

Nelson, E. C., Heath, A. C., Madden, P. A. F., Cooper, M. L., Dinwiddie, S. H., Bucholz, K. K., et al. (2002). Association between self-reported childhood sexual abuse and adverse psychosocial outcomes.  Results from a twin study. Archives of General Psychiatry, 59, 139-145.

Sartor, C. E., Lynskey, M. T., Bucholz, K. K., McCutcheon, V. V., Nelson, E. C., Waldron, M., et al. (2007). Childhood sexual abuse and the course of alcohol dependence development: Findings from a female twin sample. Drug and Alcohol Dependence, 89, 139-144.

The DRAM, Vol. 3(10) - Comorbidity among Repeat Driving Under the Influence of Alcohol Offenders

Psychiatric comorbidity is elevated among people guilty of driving under the influence of alcohol (DUI; Lapham et al., 2001). Consequently, understanding the psychiatric profiles of repeat DUI offenders is important for developing and delivering effective treatment (Nelson et al., 2007).  This week’s DRAM presents epidemiological information about the frequency of psychiatric disorders among repeat DUI offenders attending a treatment program compared to the general US adult population (Shaffer, Nelson, LaPlante, LaBrie, & Albanese, 2007).

Massachusetts’ judges can offer adults who have received two or more DUI convictions participation in a two-week inpatient treatment program as an alternative to incarceration.  As part of the program’s intake procedure, trained counselors administer a truncated version** of the Composite International Diagnostic Interview (CIDI) to all eligible admissions (i.e., those who spoke English and had no cognitive difficulties). The CIDI is a comprehensive, standardized, and computer guided instrument to assess interviewees for the presence of substance use and other mental disorders according to ICD-10 and DSM-IV criteria.  The investigators compared the psychiatric profiles of 729 eligible  and consenting consecutive admissions during a period of 15 consecutive months (79% of eligible admissions) to data from the National Comorbidity Survey Replication (NCS-R; Kessler, Berglund, Demler, Jin, & Walters, 2005). The NCS-R used the same version of CIDI to survey a nationally representative household sample of adults (N = 9,282).

The sample was 81% male, 88% Caucasian, and the average age was 39.7 (SD = 11.6).  Thirty-two percent had an income lower than $20,000, 72% had a high school education or less, 63% were employed, and 97.6% qualified for a substance use disorder during their lifetime.   Figure 1 shows the prevalence of 1, 2, and 3 or more different disorders occurring during participants’ lifetime and within the past year. The DUI treatment population consistently had a significantly greater likelihood of disorder than the general population.

Figure 1.  Comparing the prevalence of psychiatric comorbidity between repeat DUI offenders and a general population community sample

Adapted from Shaffer, Nelson, LaPlante, LaBrie, & Albanese, 2007
Note:  Includes only disorders measured by both this study and the NCS-R. 
*p<.001

One limitation of the Shaffer et al. study is that the study sample might not generalize to all repeat DUI offenders in Massachusetts or elsewhere.  The treatment program only served 53% of those repeat offenders who were sentenced to treatment and agreed to that option instead of prison time.  Therefore, those who did not attend this program might have a different diagnostic profile of addictive and psychiatric disorders.  Furthermore both the DUI treatment group and the NCS-R group self-reported psychiatric symptoms, which might reflect a reporting bias.

Despite these limitations, these results demonstrate that multiple DUI offenders are more likely than the general population to suffer from comorbid psychiatric disorders in addition to a substance use disorder.  Therefore, DUI treatment programs will need to include a comprehensive assessment of pathology and develop treatment plans that account for complex psychiatric profiles.

**The truncated CIDI assessed past-year and lifetime alcohol abuse and dependence, drug abuse and dependence, nicotine dependence, pathological gambling, major depression, mania, hypomania, dysthymia, generalized anxiety disorder, posttraumatic stress disorder, and conduct disorder.  Authors added assessment for intermittent explosive disorder and attention-deficit hyperactivity disorder after 107 people had completed the CIDI.

What do you think?  Comments can be addressed to Leslie Bosworth

References

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

Lapham, S. C., Smith, E., C'de Baca, J., Chang, I., Skipper, B. J., Baum, G., et al. (2001). Prevalence of psychiatric disorders among persons convicted of driving while impaired. Archives of General Psychiatry, 58, 943-949.

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. Administration and Policy in Mental Health, 34, 489–493.

Shaffer, H. J., Nelson, S. E., LaPlante, D. A., LaBrie, R. A., & Albanese, M. (2007). The epidemiology of psychiatric disorders among repeat DUI offenders accepting a treatment-sentencing alternative option. Journal of Consulting and Clinical Psychology, 75(5), 795-803.

What do you think?  Comments can be addressed to Leslie Bosworth

The DRAM Vol. 3(9) - How Protective are Protective Factors: The Complexity of Risky Drinking Behavior

For young people, the use and abuse of alcohol is alarming because of their still developing neuroanatomy, and the ease with which they can develop a dependency to alcohol (Molina, 2007; Padget, 2006). Using scientific studies to understand what leads adolescents toward future problematic alcoholic use can facilitate the development of more effective public health initiatives targeting underage alcohol consumption. This week, The DRAM discusses research that examines how adverse childhood experiences and the age of onset for first alcohol use contribute to young adults’ current use of alcohol.

Young, Hansen, Gibson, and Ryan (2006) surveyed 18-20 year old Marine Corps recruits with the Recruit Assessment Program (RAP) Questionnaire to gather data about their demographic, family and general history, and childhood experiences. They administered these surveys at the Marine Corps Recruit Depot in San Diego California from June 2002 to April 2006.  After reviewing the initial 65,178 surveys, the researchers determined that 41,482 surveys were suitable for analysis; these surveys contained complete outcome and covariate data, and fit the criteria set forth by the researchers (i.e., 18-20 years old with no contradictory responses). The researchers conducted multivariate logistic regression analyses to assess associations between childhood factors (e.g., onset age, adverse childhood experiences) and young adult risky drinking patterns. The investigators Identified risky drinkers by using the AUDIT Alcohol Consumption Questionnaire.

Approximately one in seven (14.8%) of participants met the researchers’ criteria for risky drinking, and 45.1% satisfied their criteria for the non-risky drinker category; the other 40.2% were self-reported non-drinkers. The risky drinkers reported only slightly higher prevalence of experiencing child abuse or witnessing domestic violence compared to their non-risky drinking counterparts (see Table 1). However, those recruits who reported first drinking around 13 years were 5.5 times more likely to engage in riskier drinking behavior than recruits who reported first drinking after age 13. Other significant and anticipated predictors of young adult drinking were smoking, having a rural or small town background, having grown up with someone who was a problem drinker or having grown up with someone who suffered from mental illness. Some unexpected correlates of risky drinking were achieving a higher educational level, having more close family members or friends, and being raised by two parents.

This study of young adult drinking had three key limitations: (1) self-reported data collection; (2) the large number of excluded surveys, potentially limiting the study's representativeness; (3) the narrow sample (i.e., male military recruits). Despite these concerns, Young et al. (2006) provide support for the importance of age of onset to young adult drinking habits; however, they did not find adverse childhood experiences to be equally strong predictors of young adult drinking. Interestingly Young et al. (2006) noted that they did not expect to find that risky drinkers had a number of experiences that one might expect to be protective, such as, a higher number of close family and friends, a higher level of education, along with being slightly more likely to be raised by two parents. Young et al’s (2006) research shows that multiple and interactive factors, whether prototypically protective or detrimental, can be associated with harmful drinking behavior. The presence of protective childhood experiences does not guarantee a young adult life without substance abuse problems.

What do you think?  Comments can be addressed to Ingrid Maurice.

References

Molina, J. C., Spear, N.E., Mennella, J.A., Lewis, M.J. (2007). The International society for developmental psychobiology 39th annual meeting symposium: Alcohol and development: beyond fetal alcohol syndrome. Developmental Psychology, 49(3), 227-242.

Padget, A., Bell, M.L., Shamblen, S.R., Ringwalt, C.L. (2006). Does learning about the effects of alcohol on the developing brain affect children's alcohol use? Prevention Science, 7(3), 293-302.

Young, S. Y. N., Hansen, C.J, Gibson, R.L, Ryan, M.A.K. (2006). Risky Alcohol Use, Age at Onset of Drinking, and Adverse Childhood Experiences in Young Men Entering the US Marine Corps. Archives Pediatrics & Adolescent Medicine, 160(12), 1207-1214.

The DRAM Vol. 3(8) - Can In Utero Exposure to Alcohol Lead to Alcohol Disorders During Early Adulthood?

Preliminary reports indicate that maternal drinking during mid pregnancy is (1) an independent contributor to the likelihood of alcohol consumption by age 14 and (2) significantly related to the development of alcohol disorders by age 21(Baer, Barr et al. 1998; Baer, Sampson et al. 2003). This DRAM reviews a longitudinal investigation focusing on the associations between maternal drinking during early and late pregnancy and the development of alcohol disorders during early adulthood among offspring.

Alati, Al Mamun, Williams, O’Callaghan, Najman, and Bor (2006) interviewed 7,223 mothers who had their first antenatal visit at Mater Misericordiae Hospital in Brisbane, Australia between 1981 and 1984.  Throughout the next 21 years, the researchers were able to complete five follow-up visits with 2,555 mother and child pairs (35.4%).  At each interview, mothers recounted the quantity of alcohol they consumed during different stages of pregnancy as well as the amount they were consuming at the time of the interview.

Results indicated that 25% of offspring (n=640) met the DSM-IV criteria for a life-time diagnosis of alcohol disorders by age 21:  13% (n=333) reported the disorder before age 18 (early onset), and 12% (n=307) reported the disorder between ages 18 and 21 (late onset) (See Table 1).  Further analysis indicated that mothers who consumed more than two glasses of alcohol during early pregnancy, at an average of 18 weeks gestation, were 2.47 times more likely to have a child with an early onset alcohol disorder and 2.04 times more likely to have a child with a late onset alcohol disorder compared to mothers who drank two or fewer glasses of alcohol during pregnancy.  Overall, the association between maternal drinking during early pregnancy and having a child with an alcohol disorder was stronger for early onset than for late onset.

Table 1.  Univariable Associations Between Maternal Alcohol Use and Onset of Alcohol Disorders at Age 21 Years (Alati, Al Mamun et al. 2006) 

There were several limitations to this study.  When researchers asked participants the quantity of alcohol they consumed, no specific measurement was stated as the standard volume of a single drink.  The researchers assumed that the typical drink was 10 grams but participants could have used widely varying quantities.  It is important to remember that only 35.4% of the initial cohort completed all five phases of the study, which increases the possibility that the final sample might not be representative of the total study population.  Additionally, although the authors examined the effect of maternal alcohol and tobacco use during the follow-up period, there are many other unanalyzed risk and protective factors that could mediate the in utero exposure effect.

This study introduces the possibility that in utero alcohol exposure could be predictive of future alcohol disorders.  However, many of the study features, including the lack of controls for additional influences, limits our ability to interpret these findings.

What do you think? Comments can be addressed to Sara Kaplan.

References

Alati, R., A. Al Mamun, et al. (2006). "In utero alcohol exposure and prediction of alcohol disorders in early adulthood " Arch Gen Psychiatry 63: 1009-1016.

Baer, J. S., H. M. Barr, et al. (1998). "Prenatal alcohol exposure and family history of alcoholism in the etiology of adolescent alcohol problems." Journal of Studies on Alcohol 59: 533-543.

Baer, J. S., P. D. Sampson, et al. (2003). "A 21-year longitudinal analysis of the effects of prenatal alcohol exposure on young adult drinking." Archives of General Psychiatry 60: 377-385.

The DRAM Vol. 3(7) - Thirsty Thursdays: A Problematic Drinking Phenomenon among College Students?

Researchers have well-documented problematic alcohol use on college campuses (e.g., Presley & Cashin, 1996; Wechsler et al., 2000; Johnston et al., 2004).  Consequently, understanding the etiology of problem drinking on college campuses is important to college administrators, health professionals, and alcohol researchers.  This week’s DRAM reviews an investigation of ‘Thirsty Thursdays’: the prevalence of excessive drinking on Thursdays, and its relation to class time on Fridays.

Wood and Rutledge (2007) surveyed 3,720 first-time college students at a large Midwestern university.  Each semester for four years, the participants completed a web-based survey and self-reported their drinking behavior during the past seven days.  Participation rates varied from 66.5% to 74% across semesters; 90% of eligible participants completed a survey at least once.  Other measures in the survey included Fraternity/Sorority involvement, precollege reports of being drunk, academic aptitude, attempted credit hours, and academic schedule.

The number of drinks consumed on Thursdays was a function of the starting time of students’ first Friday class (see Figure 1), even after adjusting for participant sex, Fraternity/Sorority involvement, number of attempted credit hours, and student grade.  Compared to students with Friday classes before 11am, men with a later first class were 16-18% more likely and women were 8-11% more likely to consume binge amounts of alcohol (i.e., 5+ drinks for men and 4+ for women) on Thursday night.

Figure 1. Mean Number of Thursday Drinks By Time of First Friday Class (Adapted from Wood and Rutledge)

  Starting Hour of First Friday Class

Despite limitations to the current study, including reliance on self-reported drinking behavior and failure to measure student truancy from classes, it is possible that drinking less on Thursdays to fulfill responsibilities on Fridays represents rational drinking behavior on the part of college students.  However, if students who have early Friday classes engage in harmful ‘catch-up’ drinking (i.e., drinking more on the weekends to ‘make up’ for not drinking on Thursdays), then the Thirsty Thursday effect is problematic for reasons not previously anticipated.  To fully understand “Thirsty Thursday”, further research should determine the presence of ‘catch-up’ drinking by investigating weekend drinking behavior in relation to Thursday drinking patterns, as well as whether underlying problematic drinking behavior can better account for the rates of binge drinking on Thursdays rather than the influence of Thirsty Thursday and Friday class schedule.

References

Wood P, S. K., Rutledge P. (2007). College Student Alcohol Consumption, Day of the Week, and Class Schedule. Alcoholism: Clinical and Experimental Research., 31(7), 1195-1207.

The DRAM Vol. 3(6) Unlocking the mind: Study uncovers a possible neural pathway to explain how talk therapy works.

There are various forms of therapy, but for many people help revolves around talking over past experiences.  Researchers are working to uncover the mechanisms by which talk therapy operates.  Recently, Lieberman et al. (2007) utilized functional Magnetic Resonance Imaging (fMRI), to examine how affect labeling (i.e., putting feelings to words) diminishes activity in areas of the brain associated with emotional response.

Thirty subjects completed 10 trials each of 6 experimental tasks: (a) labeling emotional facial expressions; (b) matching emotional facial expressions; (c) observing emotional facial expressions; (d) labeling the gender of facial images; (e) matching the gender of facial images; and (f) matching shapes (see Figure 1).  Researchers used fMRI to scan participants’ brains as they completed each of the tasks.  The authors suggested that if affect labeling diminishes emotional reactivity to negative stimuli, the fMRI should reveal reduced activity in the amygdala .

FIGURE 1: A display of the 6 types of stimuli used in the experiment.

 

Engaging in affect labeling, the combination of observing an emotional stimulus and labeling the emotion (i.e., stimulus a), significantly reduced amygdala activity compared to the separate tasks of observing without labeling (stimulus c) or labeling a non-emotional stimulus (stimulus d).  Additional analyses revealed a plausible neural mechanism, showing that activity in the right ventrolateral prefrontal cortex (RVLPFC), mediated by the medial prefrontal cortex (MPFC), might have dampened the amygdala activity. 

This study illustrates the dampening effect that affect labeling has on affective activity in the limbic system.  The study also provides some early insight into one aspect of how talk therapy might act on the brain to help decrease emotional distress.  Future studies could benefit from observing the effect on the amygdala and the larger limbic system produced by having people experience and verbalize their reaction to negative emotional images.  It also might be helpful to observe activity in the limbic system while participants undergo psychotherapy to see what effect therapy has on the limbic system.  Evaluating treatment is an important part of the process.  Advances in technology might contribute to a better understanding of the effectiveness of various forms of treatment for those suffering from mental illness.

What do you think? Comments on this article can be addressed to John Kleschinsky.

References

Gerrig, R. J. Z., Philip G. (2002). Psychology and life (16th ed.). Boston: Allyn and Bacon.
Lieberman, M. D., Eisenberger, Naomi I., Crockett, Molly J., Tom, Sabrina M., Pfeifer, Jennifer H.,   Way, Baldwin M. (2007). Putting feelings into words:  Affect labeling disrupts amygdala activity in response to affective stimuli. Psychological Science, 18(5), 421-427.

The DRAM 3(5) - Addiction and Technology − Reliability of Online Alcohol Use Assessments

Compared to traditional paper and pencil methods, online studies can benefit from the convenience, lower cost, reduced time to completion, access to larger samples, and reduced self-presentation biases offered by the Internet (Miller, 1997; Schmidt, 1997). Reducing the extent that participants bias their self-presentation can benefit addiction-related research. Until now, researchers have not studied the reliability or validity of commonly used addiction-related surveys administered via the Internet. This week’s DRAM reviews a study that, in part, compares the reliability of web-based addiction-related surveys to traditional paper and pencil surveys.

Miller, E.T., Neal, D.J., Roberts, L.J., Baer, J.S., Cressler, S.O., Metrik, J., Marlatt, G.A. (2002) administered a survey to a sample of 255 undergraduates who were assigned randomly to one of three experimental conditions: pencil and paper (PP), web-based (W), and web-based with interruption (WI). The PP and W groups completed the questionnaire without stopping, but the WI group stopped for 1 to 48 hours before finishing the survey. All groups completed the same questionnaire again one week later under the same experimental conditions. In addition to basic demographic information and drinking patterns, the survey included the Alcohol Use Disorders Identification Test (AUDIT;Babor, De la Fluente, Saunders, & Grant, 1992), the Alcohol Dependence Scale (ADS: Skinner & Allen, 1982), the University of Rhode Island Change Assessment (URICA; McConnaughy, DiClemente, Prochaska, & Velicer, 1993) and the Rutgers Alcohol Problem Index (RAPI; White & Labouvie, 1989).

Table 1 – Means scores at time 1, correlation with score at time 2, with significant differences across condition.

The groups did not differ on demographic variables, including age, gender, ethnicity, residence, and also did not differ on measures of alcohol use. Using correlations to measure agreement between first and second administration of the test instruments, the researchers found for all groups that all Pearson correlations were statistically significant (p < .01) and ranged from .54 to .93. Between group comparisons indicated that few differences were statistically significant out of the 48 comparisons (see Table 1). The authors suggested that the indicated differences might be chance observations. We also suggest that some statistically significant differences were not clinically meaningful because they were between correlations that both indicated temporal reliability.

Limitations of the study include that the college sample might not generalize to other population segments (e.g., adults, children, etc.). Also, a one week interval might not be long enough to reduce possible memory effects. Another limitation is that the researchers dismissed some significant test-retest differences that were quite large; for example, the AUDIT dependence scale. Finally, the study utilized a self-report methodology, which could mean participants consistently presented the same inaccurate information on both tests, yielding acceptable reliability estimates at the cost of validity.

This study shows the promise of online survey techniques, and researchers might glean various benefits by using this technology. Important benefits might include: cutting research costs, reaching more and different people, and reducing time and travel restrictions on potential research participants. Researchers should continue to study this methodology to further understand its benefits and costs.

What do you think? Click Here and let us know! Comments can be addressed to Leslie Bosworth.

References       

Babor, T. F., De la Fluente, J. R., Saunders, J., & Grant, M. (1992). The alcohol use disorders identification test: Guidelines for use in primary health care. Geneva, Switzerland: World Health Organization.

McConnaughy, E. A., DiClemente, C. C., Prochaska, J. O., & Velicer, W. F. (1993). Stages of change in psychotherapy: Measurement and sample profiles. Psychotherapy: Theory, Research and Practice, 20, 368-375.

Miller, E. T. (1997). Predicting successful self-initiated health-related behavior change in the context of New Year's resolutions: Utilizing the Internet for survey research. University of Washington.

Miller, E. T., Neal, D. J., Roberts, L. J., Baer, J. S., Cressler, S. O., Metrik, J., & Marlatt, G. A. (2002). Test-retest reliability of alcohol measures: Is there a difference between Internet-based assessment and traditional methods? Psychology of Addictive Behaviors, 16(1), 56-63.

Schmidt, W. C. (1997). World Wide Web survey research: Benefits, potential problems, and solutions Behavior Research Methods, Instruments and Computers, 29, 274-279.

Skinner, H. A., & Allen, B. A. (1982). Alcohol dependence syndrome, measurement and validation. Journal of Abnormal Psychology, 91, 199-209.

White, H. R., & Labouvie, E. W. (1989). Towards the assessment of adolescent problem drinking. Journal of Studies on Alcohol, 50, 30-37.

The DRAM Vol. 3(4) - Addiction and Technology - Internet-based Treatment for Alcohol Related Disorders: Clinical Characteristics of People who Seek Help on the Internet

Traditional substance abuse programs cannot always successfully serve every help seeking substance abuser. Roadblocks include logistical reasons, such as transportation, money to pay for treatments or childcare, as well as clinical reasons, such as the dynamics of treatment or group therapy (Gillaspy, Wright, Campbell, Stokes, & Adinoff, 2002; Saunders, Zygowicz, & D'Angelo, 2006). Online treatment programs potentially can expand the availability of care to people who cannot obtain traditional treatment. The effectiveness of these non-traditional treatments requires a better understanding. First, we need to learn more about the characteristics of those individuals who are willing to seek help via the Internet. This week’s DRAM discusses the clinical characteristics of treatment seekers using an online alcohol evaluation program and the implications for effective online interventions.

Lieberman (2005) designed a website to evaluate the clinical characteristics of those who sought help online. Using search terms like “alcohol abuse,” and “drinking problem,” 1,432 people searched for, registered at, and gave consent to participate in the study at the researcher’s website. Most registrants, 1,297 (90%), completed the evaluation. Participants responded to a number of scales, including the Alcohol Use Disorder Identification Test (AUDIT; Babor, De la Fluente, Saunders, & Grant, 1992). For a variety of measures, Lieberman compared the sample of online participants to participants in the Matching Alcoholism Treatments to Client Heterogeneity study (Miller & Tonigan, 1996; Project MATCH Research Group, 1997). Project MATCH evaluated 1,726 substance abusing or dependent adults from outpatient and aftercare programs. The MATCH sample represented people currently seeking treatment from traditional substance abuse programs.

Table 1: Comparison of Traditional and Non-Traditional Help Seekers

The Internet sample averaged 29.5 (+/- 22.4) drinks per week and 10.1 (+/- 5.6) drinks on the heaviest drinking day. The Internet sample averaged 17.3 (+/- 8.9) on the AUDIT, for which 8 or above indicates a problem with alcohol. Approximately 11% of the sample scored below an 8 on the AUDIT. Consequently, a portion of the Internet sample did not meet traditional criteria for an alcohol problem. This differs from the MATCH study, which required participants to satisfy criteria for alcohol abuse or dependence.

Table 1 provides a side-by-side comparison of the Internet sample, which represents a potential non-traditional treatment group, to the MATCH sample, which represents a traditional treatment group. A higher rate of women accessed the website than participated in the MATCH study, but age and years of formal education were very similar. All MATCH participants were in treatment; Lieberman did not specify the portion of Internet users in treatment. Internet users had a high rate of employment as compared to the MATCH sample.

Compared to the MATCH sample, the Internet sample was not as uniformly disordered, and was more likely to be employed. These two factors suggest that the Internet sample might be healthier than the MATCH sample. Unfortunately, Lieberman did not inquire about Internet participants’ current or past alcohol treatment experience. So, it is impossible to determine whether the samples varied on this factor. It is important to note that the Internet sample is one of convenience; this limits our ability to generalize from this sample to a broad population of online treatment seekers. The participants also self-reported their previous experiences, a method of data collection subject to errors of memory, self presentation biases, and other factors.

Websites are accessible, private, demand less accountability and commitment than group therapy, and eliminate logistical reasons for not seeking traditional treatment (e.g., transit and childcare). Relatively high rates of participation by women suggest that online resources might facilitate help-seeking among underserved populations. Further, the absence of self-reported alcohol use disorders among the Internet population (i.e., 11%) might indicate that online treatment could be effective as an early intervention for those approaching, but not ready to commit to traditional alcohol treatment. Just as some people slowly acquire problems with alcohol, they also can acquire a habit of monitoring drinking patterns and seeking treatment slowly. Online treatments have the potential to be an effective strategy to provide systematized screening and personalized feedback to educate and raise awareness about potential alcohol problems.

What do you think? Click Here and let us know! Comments can be addressed to Leslie Bosworth.

References

Babor, T. F., De la Fluente, J. R., Saunders, J., & Grant, M. (1992). The alcohol use disorders identification test:
Guidelines for use in primary health care. Geneva, Switzerland: World Health Organization.

Gillaspy, J. A., Wright, A. R., Campbell, C., Stokes, S., & Adinoff, B. (2002). Group alliance and cohesion as predictors of drug and alcohol abuse treatment outcomes. Psychotherapy Research, 12(2).

Miller, W. R., & Tonigan, J. S. (1996). Assessing drinkers' motivation for change: the Stages of Change Readiness and Treatment Eagerness Scale (SOCRATES). Psychology of Addictive Behaviors, 10, 81-89.

Project MATCH Research Group. (1997). Matching alcoholism treatments to client heterogeneity: Project MATCH posttreatment drinking outcomes. Journal of Studies on Alcohol, 58(1), 7-29.

Saunders, S. M., Zygowicz, K. M., & D'Angelo, B. R. (2006). Person-related and treatment-related barriers to alcohol treatment. Journal of Substance Abuse Treatment, 30(3), 261-270.

The DRAM Vol. 3(3) - Utilization of Alcohol Treatment Services among Minority Groups

Researchers and federal institutes have indicated the need to attend to disparities in minority access to healthcare services (Ibrahim, Thomas, & Fine, 2003; National Institute on Alcohol Abuse and Alcoholism, 2001). Recent studies show that, although rates of alcohol problems and dependence among Whites have remained fairly stable or declined, such rates have increased among America’s largest minority groups (Grant et al., 2004). This is very important because current projections anticipate substantial growth in the minority population (Bergman, 2004; Cheeseman Day, 2001)1. Thus, an increasingly large portion of the population might have inadequate healthcare. This week’s DRAM reviews research about the lifetime utilization of alcohol treatment services among White, Hispanic, and Black individuals who report alcohol-related problems (Schmidt, Ye, Greenfield, & Bond, 2007).

Using data from both the 1995 and 2000 National Alcohol Surveys (NAS; Kerr, Greenfield, Bond, Ye, & Rehm, 2004), researchers analyzed lifetime need for alcohol treatment services, utilization of alcohol treatment services (e.g., specialty treatment programs, AA, hospital or clinic, private physician, welfare, and social services) and alcohol-related problems. Both nationally representative surveys used similar instruments in English and Spanish. The 1995 NAS consisted of a total of 5,345 cases, included 1,585 Hispanics, 1,582 Blacks, and 2,178 non-Hispanic Whites; the overall response rate was 77%. The 2000 NAS had an overall sample size of 7,612, included 869 Hispanics, 1,341 Blacks, and 5,402 non-Hispanic Whites; this study achieved a response rate of 58%.

The authors’ estimated that 30% of White participants, 27% of Hispanic participants, and 22% of Black participants had a need for alcohol treatment services at some point during their lives. Hispanics reported more severe lifetime alcohol problems than Whites (p<0.01 in pair-wise comparison with Whites; no comparison with Blacks was indicated). Logistic regression analyses predicting alcohol service utilization indicated a significant ethnicity x alcohol problem severity interaction (p<.05). Researchers observed that both Hispanics and Blacks at the higher end of the problem-severity distribution were significantly less likely to have received treatment than similar Whites (see Figure 1).

Figure 1. Likelihood of treatment by number of alcohol dependence symptoms among Whites, Blacks, and Hispanics (adapted from Schmidt, Ye, Greenfield, & Bond, 2007).

There were several limitations to this study. The researchers focused only on lifetime measures of disorder instead of past-year or past-month measures. This could have exaggerated the disparity between groups, by overestimating the number of individuals who are actually in need of service currently (e.g., someone could have been in need of service early in life, had no subsequent need for care, and they still would have been classified in need of services). In addition, these individuals had to remember events that might have happened many years in the past; this circumstance can introduce recall bias.

This study highlights an imminent public health concern: the lack of healthcare access among growing population segments that are at-risk for severe substance use problems. Although, this study does not provide any causal explanation for the disparity, there are many reasons why Hispanics and Blacks might not be accessing treatment: language barriers, geographical barriers, socioeconomic barriers, cultural barriers (e.g., Hispanics or Blacks may have a more negative social perception of addiction than Whites), immigration status, etc. Future studies should begin to address these issues in the hopes of informing policies that will shorten or eliminate this gap. Hispanics and Blacks comprise an increasing portion of the total population and it is imperative that we examine and resolve disparities in healthcare services (e.g., alcohol treatment) now so that we can prevent more public health problems in the future.

What do you think? Click Here and let us know! Comments can be addressed to Juan Molina.

Notes

1. From 2000 to 2050 the non-Hispanic, white population is projected to increase from 195.7 million to 210.3 million and comprise 50.1 percent of the population compared with 69.4 percent in 2000; the Hispanic population is projected to increase from 35.6 million to 102.6 by 2050 doubling their share of the nation’s population from 12.6 percent to 24.4 percent; the black population is projected to increase from 35.8 million to 61.4 million in 2050, increasing their share of the population from 12.7 to 14.6 percent.

References

Bergman, M. (2004). Census Bureau Projects Tripling of Hispanic and Asian Populations in 50 Years; Non-Hispanic Whites May Drop to Half of Total Population. Washington, D.C.: U.S. Census Bureau.

Cheeseman Day, J. (2001). National Population Projections. In U. S. C. Bureau (Ed.).

Grant, B. F., Dawson, D. A., Stinson, F. S., Choua, S. P., Dufour, M. C., & Pickering, R. P. (2004). The 12-month prevalence and trends in DSM-IV alcohol abuse and dependence: United States, 1991-1992 and 2001-2002. Drug and Alcohol Dependence, 74, 223-234.

Ibrahim, S., Thomas, S., & Fine, M. (2003). Achieving health equity: an incremental journey. American Journal of Public Health, 93, 1619-1621[Editorial].

Kerr, W. C., Greenfield, T. K., Bond, J., Ye, Y., & Rehm, J. (2004). Age, period and cohort influences on beer, wine and spirits consumption trends in the US national alcohol surveys. Addiction, 999, 1111-1120.

National Institute on Alcohol Abuse and Alcoholism. (2001). Forcast for the Future: Strategic Plan to Address Health Disparities. Bethesda, MD: National Institute on Alcohol and Alcoholism.

Schmidt, L. A., Ye, Y., Greenfield, T. K., & Bond, J. (2007). Ethnic Disparities in Clinical Severity and Services for Alcohol Problems: Results from the National Alcohol Survey. Alcoholism: Clinical & Experimental Research, 31(1), 48­56.

The DRAM Vol. 3(2) - Addiction-as-Disease: It Is All It’s Constructed to Be

Social scientists who study addiction generally agree that the disease concept is historically and culturally constructed (e.g., Gusfield, 1996; Levine, 1978; MacAndrew & Edgerton, 1969; Room, 2003; Seeley, 1962). Reinarman (2005) makes the latest case, arguing that clinical research has yet to pinpoint the source or site of addiction. This is because addiction-as-disease “is a different species of social accomplishment” (Reinarman, 2005, p. 308) whose origins lay in historical, political-institutional, and interactional achievements.

In the early nineteenth century, temperance advocates insisted that beverage alcohol was inherently addicting. Social problems in industrialized America were erroneously attributed to a drinker’s powerlessness to abstain. After the repeal of Prohibition in 1933, however, addiction-as-disease had to be reworked; most drinkers did not become habitual drunkards. Alcoholics Anonymous, the Yale Center of Alcohol Studies, and the National Council on Alcoholism “modernized” addiction-as-disease, convincing medical professionals and the public that only certain individuals were biologically susceptible to alcohol addiction. In the early 1970s, the National Institute on Alcohol Abuse and Alcoholism legitimized this model, providing “crucial institutional support, political legitimacy, and cultural momentum to the more general concept of addiction-as-disease” (Reinarman, 2005, p. 313). Today, addiction-as-disease is accepted as a scientific fact. “This completes the loop and conceals, like a good magic trick, the actual procedures by which it was accomplished” (Reinarman, 2005, p. 315).

Reinarman’s (2005) work is important because it sees past the “conceptual elasticity” of addiction-as-disease to consider how this construct affects those to whom it is applied (p. 307). People with addiction learn what addiction-as­disease looks like and “act” accordingly (Reinarman, 2005). This observation should not be misconstrued, argues Reinarman. Addiction is “real” to those who experience it (p. 316). Yet these interactional processes imply that addiction-as-disease is therapeutically hollow: “the diagnostic inference of a latent state (i.e., addiction) rests upon the consequences of that very same latent state” (Shaffer et al., 2004, p. 371).

What are the alternative paradigms to addiction-as-disease? Two come to mind. The first examines how drug users lose control by studying those who sustain it. Zinberg (1984) popularized this approach, and in his footsteps other researchers suggest that “to rail against the risks of this or that demon drug without understanding the ways in which our own culture makes drug use a thinkable and ‘do-able’ thing is an abdication of analysis” (Waldorf, Reinarman, & Murphy, 1991, p. 282). A syndrome model of addiction is also gaining scientific momentum. Shaffer, LaPlante, LaBrie, Kidman, Donato, and Stanton (2004) posit that addictive disorders follow a similar developmental pattern based on biological and psychological susceptibilities, drug or activity (e.g., alcohol or gambling) exposure, and personal experiences with an addictive object (Odegaard & Shaffer, 2005). “This [syndrome] model requires clinicians to develop multidimensional treatment plans that account for the many relationships among the multiple influences and consequences of addiction” (Shaffer et al., 2004, p. 372).

Zinberg and Shaffer et al. refute pharmacological and object determinism (Reinarman & Levine, 1997), therefore achieving what Reinarman says the concept of addiction-as-disease cannot. Their paradigms, and ones alike in aim and function, “trigger a shift of gaze” (Reinarman, 2005, p. 317) from a discourse about sickness and addicts to a dialogue about health and people.

What do you think? Click Here and let us know! Comments can be addressed to Christopher R. Freed.

References

Gusfield, J. R. (1996). Contested meanings: The construction of alcohol problems. Madison, WI: The University of Wisconsin Press.

Levine, H. G. (1978). The discovery of addiction: Changing conceptions of habitual drunkenness in America. Journal of Studies on Alcohol, 39(1), 143-174.

MacAndrew, C., & Edgerton, R. (1969). Drunken comportment: A social explanation. Chicago: Aldine Publishing Company.

Odegaard, S., & Shaffer, H. J. (2005). Addiction as syndrome. Paradigm, 9(3), 12-13, 22.

Reinarman, C. (2005). Addiction as accomplishment: The discursive construction of disease. Addiction Research and Theory, 13(4), 307-320.

Reinarman, C., & Levine, H. G. (1997). Crack in context: America's latest demon drug. In C. Reinarman & H. G. Levine (Eds.), Crack in America: Demon drugs and social justice (pp. 1-17). Berkeley: University of California Press.
Room, R. (2003). The cultural framing of addiction. Janus Head, 6(2), 221-234.

Seeley, J. R. (1962). Alcoholism is a disease: Implications for social policy. In D. J. Pittman & C. R. Snyder (Eds.), Society, culture, and drinking patterns (pp. 586-593). New York: John Wiley & Sons, Inc.

Shaffer, H. J., LaPlante, D. A., LaBrie, R. A., Kidman, R. C., Donato, A. N., & Stanton, M. V. (2004). Toward a syndrome model of addiction: Multiple expressions, common etiology. Harvard Review of Psychiatry, 12, 367-374.

Waldorf, D., Reinarman, C., & Murphy, S. (1991). Cocaine changes: The experience of using and quitting. Philadelphia: Temple University Press.

Zinberg, N. E. (1984). Drug, set, and setting: The basis for controlled intoxicant use. New Haven, CT: Yale University Press.

The DRAM Vol. 3(1) - It runs in the family: Maternal and paternal influence on adolescent drinking

Evidence related to family factors and adolescents’ decisions about using alcohol is mixed. Brody, Ge, Katz, and Arias (2000) found that parental alcohol-related norms and alcohol use was related to children’s drinking. However, Yu (2003) found that parents’ attitudes about alcohol and alcohol use had no impact on their children’s alcohol use; however, this study also found that spending time with parents who drank related positively to adolescent drinking. Thus, to clarify the specific factors that are involved in adolescents’ decisions regarding drinking, it is important to explore further the relationship between parents and teen drinking. For example, parental gender differences might influence adolescent drinking. In this week’s DRAM, we review a longitudinal study that examined several parental factors and their relationship to teen drinking (Seljamo et al., 2006).

Seljamo et al. recruited participants from health care clinics in South Western Finland. During 1986-1987, the first wave of data collection, 1,287 (i.e., a 75% response rate) parents answered questions about their alcohol use while pregnant with the child participant. Seven years later, 846 of these parents answered questions about their alcohol use (66% of Wave 1 participants responded); eight years after that both parents and children completed a final survey (75% of the Wave 2 participants responded). The parents answered questions about their age of first alcohol use and the frequency of their drinking during the past six months; they also completed items about socio­demographic factors (e.g., education, employment, martial status and if there were new adults in the child’s life). The teen participants answered questions about alcohol use and alcohol-related injuries. The researchers conducted several multivariate analyses to assess the relationship between adolescent drinking (dependant variable) and mothers’ alcohol-related behavior, fathers’ alcohol-related behavior, and socio-demographic variables (independent variables).

Table 1 Independent predictors of children’s problematic alcohol use: significant mother-related and father-related variables (Adapted from Seljamo et al., 2006).



The results showed that 83% of girls and 79% of boys used alcohol by the age of 15. Forty-two adolescents reported alcohol-related injuries of their own or their friends. Children whose parents, both mothers and fathers, were early drinkers were more likely to drink than children with parents who drank later in life (See Table 1) (mother’s odds ratio = 1.7, .07, 1.0, p < 0.01; father’s odd ratio = 2.5, 1.1, p < 0.01). Table 1 also shows that father’s consumption of alcohol (father’s odd ratio = 1.8, 1.2, 1.0 p < 0.05) and mother’s drunkenness during the past 6 months (mother’s odds ratio = 2.4, 1.8, 1.0, p < 0.01) were positively related to adolescent drinking. In addition, parental separation was a strong general predictor of adolescent drinking (odds ratio = 1.9, 1.0, p < 0.01 ).

This study has several limitations. First, the study lacked any measure of alcohol dependence. More items assessing alcohol dependence would better indicate whether problem drinking was present. The study also could have included questions about alcohol attitudes and beliefs, since there are contradictory reports about how these impact teen drinking. Another limitation was the self-report data; for example, both parents and children might have under or over reported alcohol use due to the perception of social norms. The findings might not generalize to adolescents from other regions due to the lack of variation among the sample, as well as geographic variations associated with legal drinking age (i.e., the legal drinking age in Finland is 18) (Poikolainen, Tuulio-Henriksson, Aalto-Setälä, Marttunen, & Lönnqvist, 2001). This study also experienced declining participation rates, a common limitation in longitudinal studies. This problem might have biased the data because dropouts could have been different from remaining participants. In addition, teens who reported no drinking could still be using other drugs like marijuana; further research should ask additional questions about other drug use.

This study illustrates the importance of parents’ influence on their children’s drinking behavior. Public health prevention workers need to consider these findings when implementing prevention programs as well as treatment for alcohol misuse. For example, there are differences in how drinking behaviors of mothers versus fathers relate to adolescent drinking. Researchers need to examine the reasons for these gender differences and further explore their implications. In addition, investigators need to examine closely how structural changes in family (e.g., separation and divorce) relate to teen drinking. Future research focusing on this complex relationship might yield important information that can help prevention programs develop strategies to effectively target at-risk adolescents. We encourage health organizations and families to work together in their efforts to combat alcohol misuse.

What do you think? Click Here and let us know! Comments can be addressed to Sarbani Hazra.

References

Brody, G. H., Ge, X., Katz, J., & Arias, I. (2000). A longitudinal analysis of internalization of parental alcohol-use norms and adolescent alcohol use. Applied Developmental Science, 4(2), 71-79.

Poikolainen, K., Tuulio-Henriksson, A., Aalto-Setälä, T., Marttunen, M., & Lönnqvist, J. (2001). Predictors of alcohol intake and heavy drinking in early adulthood: a 5-year follow-up of 15–19-year-old Finnish adolescents. Alcohol and Alcoholism 36(1), 85-88.

Seljamo, S., Aromaa, M., Koivusilta, L., Rautava, P., Sourander, A., Helenius, H., et al. (2006). Alcohol use in families: A 15-year prospective follow-up study. Addiction, 101(7), 984-992.

Yu, J. (2003). The association between parental alcohol-related behaivors and children's drinking. Drug and Alcohol Dependence, 69(3), 253-262.

The DRAM, Volume 2(10) - Happy anniversary! Alcohol prohibition ended on December 5, 1933 – but could it be effective today?

The prohibition of beverage alcohol in the United States between 1919 and 1933 and its success or failure continues to be a topic of scientific research and public debate. A recent article in the American Journal of Public Health provides an interesting contribution to this topic from the perspective of a historian (Blocker, 2006). Moreover, it raises interesting questions: Could alcohol prohibition return? Can prohibitions of any kind and in any form be viable public health options?

The article begins with an overview of the historical processes leading to the Prohibition Act and its repeal. Next, he summarizes expected and unexpected effects of the Prohibition Act. The author concludes that, as expected, prohibition actually did lower per capita alcohol consumption. However, not as expected, women emerged as a new group of public drinkers.

Blocker goes on to derive lessons for today’s prohibitions on tobacco, illicit drugs, and guns. His view is that prohibitions can be a public health option, but effectiveness might vary depending on the type of banned object or activity and, most importantly, depending on historical context. “Historical context” means that prohibitions could work in one place but not another, in one time but not another, and in one population but not another (Tyrrell, 1997). Blocker argues that for prohibitions to succeed, the aim should not be a legislation of morals and not a regulation of economy, but should be a concern for public health. He argues that prohibitions can succeed when widespread public consensus is behind a prohibition and its enforcement. For example, regarding passive smoking laws or illicit drugs, at least partial prohibitions are in place today and are driven by strong public support. Further, the author explains that qualities of the banned articles (e.g., the conditions of production, the value to an illicit trade, or the ability to conceal the article) will affect the success of prohibitions. Health and social costs but also potential benefits of prohibitions and effects on both the individual and the society at large are important to consider.

The article has some limitations because of its editorial style. It neither reports an empirical study nor provides a comprehensive review of research findings about the effects of the Prohibition Act. Rather, it presents a narrative summary of selected arguments to support the author’s opinion. While this is a justifiable approach, it is also open to certain biases. For example, the author argues that alcohol consumption decreased during the prohibition but he does not cite any empirical evidence to support this claim. Also, he fails to balance this claim by discussing that crime rates might have increased during the prohibition (see Figure). From Blocker’s perspective, changes in historical context could result in new and renewed legislated prohibitions, which he considers to be public health innovations.

Figure: Alcohol consumption may have decreased while crime rates may have increased during the prohibition in the United States between 1919 and 1933 (Miron, 2001)

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References

Blocker, J. S. (2006). Did prohibition really work? Alcohol prohibition as a public health innovation. American Journal of Public Health, 96(2), 233-243.

Miron, J. (2001). Alcohol Prohibition. In R. Whaples (Ed.), EH.Net Encyclopedia: http://eh.net/encyclopedia/article/miron.prohibition.alcohol. Website accessed Nov 22, 2006.

Tyrrell, I. (1997). The US prohibition experiment: myths, history and implications. Addiction, 92(11), 1405-1409.

The DRAM, Volume 2(9) - Examining AA Participant Typology: Who does AA Help?

Alcoholics Anonymous (AA) is one of the best known fellowships for those who have experienced drinking related problems. Research shows that attending AA meetings is related to short term as well as long term abstinence (Kaskutas et al., 2005; Moos & Moos, 2006). Following these findings, researchers are examining factors that relate to AA attendance. This week’s DRAM reviews a study by Tonigan, Bogenschutz, & Miller (2006), which examined how a drinking typology relates to AA attendance and AA related behaviors.

The investigators used Project MATCH’s (P.M.R.G., 1997) samples of aftercare and outpatient clients (N=454). The researchers assessed participants before treatment, at the end of treatment, and every three months post treatment for a year. Assessments included AA attendance, alcohol abstinence, and the Alcoholic Anonymous Inventory (AAI), which measures AA beliefs and behaviors. Participants also answered demographic questions. The researchers classified participants into two drinking typologies: Type A (less impaired drinkers) and Type B (more impaired drinkers). The investigators used the MacAndrew Alcoholism score and the Physical Consequences of Drinking index to help group participants by typology. The researchers used two repeated measures multiple analysis of covariance (MANCOVA) to assess the differences between Type A and Type B participants regarding their AA attendance and engagement (i.e., AA beliefs and behaviors). The investigators used stepwise logistic regression to measure whether typological differences could predict one year abstinence rates.

Table 1 Differences between Type A and Type B AA participants for AA attendance, AA engagement, and abstinence at 12-month follow-up (N = 454)

The investigators classified 57% of the participants in the Type A group. Table 1 shows that Type B participants attended more AA meetings than Type A participants across the 12 month follow-up. However Type A participants and Type B participants did not report significantly different AA beliefs and practices. In addition, 30.3% of Type A participants reported abstinence for at least one year, while 25.3% of Type B participants reported abstinence for a year.

One limitation of the study is how the researchers classified the participants. The impairment drinking typology employed in this study might not provide the best classification strategy, be the only way to subgroup AA participants, or to predict AA attendance and affiliation. Other factors like religiosity, access to social networks, and belief systems might add strength to our ability to predict AA attendance and affiliation.

Still, this study provides insight about the relationship between participant typologies and AA. Although, Type B participants had higher AA attendance than Type A participants, Type A participants reported more abstinence than Type B participants. Yet, Type A and Type B participants were similarly engaged in AA beliefs and practices. Further research needs to assess the validity of this drinking typology among AA participants (i.e., impairment) and compare it to alternative strategies. In addition, future studies should measure additional AA participant characteristics to determine how these variables might relate to AA affiliation and abstinence.

What do you think? Comments can be addressed to Sarbani Hazra.

References

Kaskutas, L. A., Ammon, L., Delucchi, K., Room, R., Bond, J., & Weisner, C. (2005). Alcoholics Anonymous Careers: Patterns of AA Involvement Five Years after Treatment Entry. Alcoholism: Clinical and Experimental Research, 29(11), 1983-1990.

Moos, R. H., & Moos, B. S. (2006). Participation in Treatment and Alcoholics Anonymous: A 16-Year Follow-Up of Initially Untreated Individuals. Journal of Clinical Psychology, 62(6), 735-750.

P.M.R.G. (1997). Matching alcoholism treatments to client heterogeneity: Project MATCH posttreatment drinking outcomes. Journal of Studies on Alcohol, 58, 7-29.

Tonigan, J. S., Bogenschutz, M. P., & Miller, W. R. (2006). Is alcoholism typology a predictor of both Alcoholics Anonymous affiliation and disaffiliation after treatment? Journal of Substance Abuse Treatment, 30(4), 323-330.

The Dram, Volume 2(8) - Early onset alcohol dependence, severity, and treatment utilization

Current research shows that starting to drink during adolescence is linked to a number of alcohol-related problems that can emerge during adulthood (Grant and Dawson, 1997). In the last issue of the DRAM (2(7)), we reviewed an article that examined the association between age of drinking onset and the age of onset for alcohol dependence. Compared to individuals who waited to drink until age 21, participants that began drinking alcohol before age 14 were significantly more likely to develop lifetime and past-year alcohol dependence, dependence within 10 years of starting to drink, and dependence before age 25. This week’s DRAM builds upon these findings by reviewing a study that examined whether individuals who become alcohol dependent at early ages are less likely to seek alcohol-related help or treatment compared with individuals who became dependent later in life.

Hingson, Heeren, and Winter (2006) used data from the National Epidemiological Survey on Alcohol and Related Conditions (NESARC), a cross sectional survey of 43,093 adults (18 or older; response rate 81%) conducted during 2001-2002. Researchers used multistage probability sampling to obtain a representative sample of US adults. NESARC includes measures of lifetime and past-year alcohol dependence, age of onset of drinking, lifetime and pastyear use of 10 different types of drugs, and history of treatment. Study participants met criteria for alcohol dependence if they reported any 3 of 7 DSM-IV criteria. Participants reported age of onset for their first dependence episode, total number of lifetime episodes, and duration of these episodes. Participants also reported if they had ever sought treatment, age at which they first sought help, whether they ever thought they should seek help, and reasons for not getting help. The researchers used odds ratios (OR) and 95% confidence intervals (CI) to compare individuals with early onset of dependence to individuals first dependent at age 30 or older on the likelihood of seeking treatment.

Of the participants who reported ever having a dependence episode, 15% were dependent before age 18, 32% by age 18 to 20, 22% by age 21 to 24, 11% by age 25 to 29, and 21% by the age of 30 or older. Twenty-five percent of participants who reported ever having a dependence episode sought help or treatment at some point. Participants who reported being alcohol dependent at younger ages were less likely to have sought help than individuals who reported being alcohol dependent at or after age 30; 28% before age 18; and 17% between age 18 and 20 compared to 35% of individuals first dependent at age ³ 30 (P < .0001)(see figure 1). Furthermore, among individuals who did seek help, those who had earlier onset of dependence took longer to do so. Individuals who waited ³10 years to seek help after dependence onset were 31%, 24%, 28%, 21% and 10% for those dependent before age 18, between 18 and 20, between 20 and 24, between 25 and 30, and after 30 (p < .0001), respectively
(see Figure 2).

Figure 1. Percentage of Group That Sought Treatment by Age of Onset for Alcohol Dependence

* Data for ages 21-24 and 25-29 were not reported in the paper.

Figure 2. Percentage of Group That Waited ³ 10 Years to Seek Help by Age Onset for Alcohol Dependence

As shown in figures 1 and 2, individuals dependent at younger ages were both less likely to seek treatment as well as to wait longer after dependence onset to seek treatment, however there is no clear linear trend. A comparison between the group of individuals who became alcohol dependent before age 18 and those dependent after 30 presents the strongest evidence for a linear trend, but this is weakened by three very important study limitations. Recall bias might have affected the results since respondents were asked to recall events that had happened many years earlier. Second, respondents might have underreported their use and misuse to create a more socially desirable impression of themselves. Third, as explained in DRAM(2(7)), there is some debate whether the meaning of DSM-IV criteria for dependence should differ for adolescents and adults; this circumstance might have resulted in people being wrongly categorized as dependent at earlier ages.

Also, this study might not have measured all the confounding variables that are associated with both age of onset for alcohol dependence or treatment seeking behavior. For example, genetic predisposition to addiction might have an effect on age of onset as well as subsequent treatment seeking. Further, other life experiences (e.g., physical or psychological abuse as children, parental psychopathology, or parental separation when they were children) could have contributed to individuals developing early onset dependence and affected subsequent treatment seeking.

Despite these limitations the findings of this study are important and helpful. In light of the role alcohol causally plays in stimulating or sustaining the many negative consequences of drinking throughout society (e.g., DUI, psychiatric morbidity, and mortality), it is important to shed light on the treatment seeking patterns of this population and develop public policies that will increase these rates for those with alcohol related problems. In particular, intervention and prevention efforts should focus on reaching early adolescents who might be developing early onset dependence; further, education programs should be adapted to (1) teach children about the risk factors and symptoms of alcohol dependence, and (2) introduce them to treatment options to facilitate these individuals entering treatment at earlier ages.

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References

Grant, B. F., & Dawson, D. A. (1997). Age At Onset Of Alcohol Use And Its Association With DSM-IV Alcohol Abuse and Dependence: Results from the National Longitudinal Alcohol Epidemiologic Survey. Journal of Substance Abuse, 9, 103-110.

Hingson, R. W., Heeren, T., & Winter, M. R. (2006). Age of Alcohol-Dependence Onset: Associations With Severity of Dependence and Seeking Treatment. Pediatrics, 118, 755-763.

The Dram, Volume 2(7) - Early Onset Drinking and the Development of Alcohol Dependence: A Fresh Look

Starting to drink during adolescence is associated with alcohol-related problems throughout adulthood (Grant and Dawson, 1997). According to analyses of National Longitudinal Alcohol Epidemiologic Study (NLAES), a study conducted by the NIAAA from 1991-1992, 45% of individuals who began drinking before age 14 met DSM-IV criteria for alcohol dependence, compared with 10% of individuals who waited until age 21 to begin drinking (Grant and Dawson, 1997). But, what is the nature of the relationship between youthful drinking of beverage alcohol and the development of dependence on alcohol? This week’s DRAM reviews a study using a recent dataset to re-examine the association between age at drinking onset and onset of alcohol dependence.

Hingson, Heeren, and Winter (2006) used data from NIAAA’s National Epidemiological Survey on Alcohol and Related Conditions (NESARC) to examine whether individuals who began drinking at earlier ages experienced: (1) higher rates of alcohol dependence or (2) experienced alcohol dependence at earlier ages. The NESARC is a national survey of 43, 093 adults in the US aged 18 years or older conducted from 2001-2002. Researchers used multistage probability sampling to obtain a representative sample of US adults. The survey response rate was 81%. NESARC includes measures of lifetime and past-year alcohol dependence, and age of onset of drinking. Study participants met criteria for alcohol dependence if they reported any 3 of 7 DSM-IV criteria. Participants also reported at what age they started drinking alcohol, tastes and sips not included. Researchers used bivariate analyses to test the significance of relationships between age of drinking onset and alcohol dependence measures; they also employed logistic regression analyses to describe the relationship between alcohol dependence and drinking onset.

As would be expected from Grant and Dawson (1997), this study found significant associations between age of drinking onset and the development of alcohol dependence. More specifically, Table 1 shows a comparison of individuals who began drinking before age 14 and individuals who waited until they were 21 years or older to begin drinking. Logistic regression analysis revealed that, compared to individuals who waited to drink until age 21, participants that began drinking alcohol before age 14 were significantly more likely to develop lifetime dependence (47% vs. 9%; Hazard Ratio = 1.78; 95% CI, 1.51-2.11), dependence within 10 years of starting to drink (27% vs. 4%; Hazard Ratio = 1.69; 95% CI, 1.38-2.07), dependence before age 25 (33% vs. 2%; Hazard Ratio = 8.12; 95% CI, 6.33-10.43), and past-year dependence (13% vs. 2%; Hazard Ratio = 1.93; 95% CI, 1.40-2.64).

TABLE 1. ALCOHOL DEPENDENCE ACCORDING TO AGE STARTED DRINKING (ADAPTED FROM HINGSON, ET. AL., 2006)

There are a number of limitations to this study. First, recall bias might have altered the results because respondents were asked to recall events that had happened many years earlier. Second, respondents might have under- or overreported their use or age at onset of drinking to conform to perceived social desirability. Finally, some of the DSM-IV criteria might not apply in the same way to individuals across a wide age range. For example, increased tolerance during adolescence might be a normal developmental phenomenon, whereas in adults it is a sign of dependence. Adolescents who begin drinking have low tolerance and through increased drinking exposure they acquire more tolerance. This phenomenon does not necessarily translate into a clinical problem, though it can be identified wrongfully as one. If this researcher bias did occur, then the wrongful classification of individuals as dependent may have skewed the results by mistakenly increasing the amount of dependence episodes in individuals who started drinking before reaching 21 years old.

Despite these caveats, the findings of this study show that there is a need for intervention programs targeting preadolescents. Current programs and policies are not enough to deter adolescents from beginning to drink alcohol at early ages. More effective programs targeting younger children might have a two-fold effect by (1) delaying alcohol consumption as long as possible and (2) limiting the number of individuals who will become alcohol dependent during their lifetime. Furthermore, considering the vulnerable developmental states of adolescents many other psychosocial problems might be avoided by limiting the number and extent of substances adolescents ingest during these formative stages of brain development.

What do you think? Comments can be addressed to Juan Molina.

REFERENCES

Grant, B. F., & Dawson, D. A. (1997). Age At Onset Of Alcohol Use And Its Association With DSM-IV Alcohol Abuse and Dependence: Results from the National Longitudinal Alcohol Epidemiologic Survey. Journal of Substance Abuse,9, 103-110.

Hingson, R. W., Heeren, T., & Winter, M. R. (2006). Age at Drinking Onset and Alcohol Dependence: Age at Onset, Duration, and Severity. Archives of Pediatrics and Adolescent Medicine, 160, 739-746.