The Brief Addiction Science Information Source (BASIS)

The DRAM, Vol. 9(6) - State Pride? A look at the possible relationship between same-sex marriage policy and mental health

Later this month the US Supreme Court is expected to issue rulings on two high-profile cases related to same-sex marriage. Earlier this spring three states—Delaware, Minnesota, and Rhode Island—legalized same-sex marriage, bringing the total to twelve, while in the past decade 31 states have adopted constitutional amendments banning legal recognition of same-sex unions.  In light of these various legal actions, this week’s DRAM reviews a study that investigated the relationship between living in states that banned same-sex marriage and the mental health of lesbian, gay, and bisexual populations, including the prevalence of alcohol use disorders (Hatzenbuehler, McLaughlin, Keyes, & Hasin, 2010). 

Methods

• Researchers used data from the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC, 2006), a longitudinal, nationally representative study.  
  • The study included data from 2 waves: 2001-2002 and 2004-2005.  
  • Participants self-identified as heterosexual, gay or lesbian, bisexual, or not sure.
• NESARC used the Alcohol Use Disorder and Associated Disabilities Interview Schedule (AUDADIS-IV; Grant et al. 2003) to assess past-year DSM criteria for mood, anxiety, alcohol, and substance use disorders, as well as other disorders not reported here.
• For this study, researchers grouped NESARC participants according to sexual orientation and whether they lived in states that banned same-sex marriage between 2004 and 2005, which coincided with NESARC wave 2 data collection. Sample size varied among the four resulting groups:
  • LGB respondents in states with bans: n = 135
  • LGB respondents in states without bans: n = 442
  • Heterosexual respondents in states with bans: n = 9,963
  • Heterosexual respondents in states without bans: n = 24,113

Results

• Table 1, below, shows percentages of lesbian, gay, and bisexual respondents endorsing mood, anxiety, alcohol, or drug use disorders, compared to heterosexual respondents, and the percent change and adjusted odds ratios from wave 1 to wave 2.
• Among lesbian, gay, and bisexual respondents living in states that banned same-sex marriage between wave 1 and wave 2, the prevalence of alcohol use disorders increased by 41.9%.
  • Among lesbian, gay, and bisexual respondents living in states without such bans, the prevalence of alcohol use disorders increased by a smaller, non-significant, margin (28.9%).
  • Rates of alcohol use disorders increased by smaller but significant margins among heterosexuals living in states with (18.0%) and without (15.7%) same-sex marriage amendments.
  • Rates of drug use disorders increased significantly among both LGB (88.6%) and heterosexual (31.8%) residents of the states without amendments, but not among residents of states with amendments.
• Mood disorder rates increased significantly among LGB respondents in states with constitutional amendments (36.6%) and declined among LGB respondents in states without amendments (-23.6%). Rates were unchanged among heterosexual respondents.
• Anxiety disorder rates increased significantly among heterosexual respondents in both categories but not among LGB respondents.

Table 1: Percent change in prevalence of disorders from wave 1 to wave 2 among respondents by State Constitutional Amendment status. 

		Constitutional Amendment banning Same-sex marriage				No Constitutional Amendment  banning Same-sex marriage
		Wave 1 %	Wave 2 %	% Change	AOR	Wave 1 %	Wave 2 %	% Change	AOR
LGB	Alcohol disorder	21.7	30.8	41.9	1.80*	16.4	21.2	28.9	1.41
	Drug disorder	11.0	12.9	17.3	1.25	6.0	11.4	88.6	2.11*
	Any mood disorder	22.7	31.0	36.6	1.67*	22.5	17.2	-23.6	0.69
	Any anxiety disorder	14.4	18.0	25.1	1.34	13.2	15.9	21.0	1.27
Heterosexual	Alcohol disorder	8.6	10.2	18.0	1.22*	7.9	9.2	15.7	1.19*
	Drug disorder	2.1	2.1	0	1	1.8	2.3	31.8	1.34*
	Any mood disorder	10.9	11.2	2.8	1.03	7.9	8.2	3.7	1.06
	Any anxiety disorder	6.4	7.4	15.0	1.17*	5.8	6.7	15.4	1.17*

 

Asterisks indicate statistically significant changes (p<.05). Adjusted odds ratio demonstrates the odds of having a disorder at wave 2 as compared to wave 1. Odds ratio adjusted for age, gender, race/ethnicity, income, educational attainment, marital status, and region.

Limitations

• On its own, the correlation between same-sex marriage bans and increases in mental health disorders among LGB respondents does not establish causality. More evidence is needed before we can conclude that living in states that enact discriminatory policies causes mental health problems among LGB individuals.  
• Some of the conclusions of this study were based on comparing the statistical significance of odds ratios within the different groups. However, the significance of odds ratios is determined partly by sample sizes. The same size increase in prevalence rates might be significant among heterosexuals but not among LGB respondents due to much larger sample size among heterosexuals.
• Because of social stigma, especially in states with bans on same-sex marriage, some LGB individuals might not have felt comfortable reporting their sexual orientation and instead identified as heterosexual. The authors discuss how this might have inflated the increase in mental health disorders among heterosexuals in these states.  

Discussion

The gay marriage debate has been one of the most nationally polarizing issues in recent memory. Underlying all the controversy are the possible mental health consequences of same-sex marriage amendments. The results of this study suggest that institutional discrimination, in the form of same-sex marriage bans, might contribute to mental health disorders among lesbian, gay, and bisexual individuals. Considering alcohol use disorders, mood disorders, and anxiety disorders, the group that experienced the greatest increase in prevalence was LGB respondents living in states that banned same-sex marriage. The authors suggest that living in communities that are hostile to same-sex marriage might create a “cascade” of negative psychological consequences; more in-depth research is needed to confirm this explanation. On the other hand, this explanation does not account for the sharp increase in drug use disorders among LGB respondents in states without same-sex marriage bans. As US laws continue to change in the coming days, there will be more opportunity to study how public policy shapes private life.

-Emily Shoov

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References

Hatzenbuehler, M. L., McLaughlin, K. A., Keyes, K. M., & Hasin, D. S. (2010). The impact of institutional discrimination on psychiatric disorders in lesbian, gay, and bisexual populations: A prospective study. American Journal of Public Health, 100(3), 452-459.

National Epidemiologic Survey on Alcohol and Related Conditions. (2006). Alcohol Alert Number 70. 2013

Grant, B. F., Dawson, D. A., Stinson, F. S., Chou, P. S., Kay, W., & Pickering, R. (2003). The Alcohol Use Disorder and Associated Disabilities Interview Schedule-IV (AUDADIS-IV): reliability of alcohol consumption, tobacco use, family history of depression and psychiatric diagnostic modules in a general population sample. Drug and Alcohol Dependence, 71(1), 7-16. 

The DRAM, Vol. 9(5) - A DUI but no diagnosis? How DSM-5 might impact the identification of Alcohol Use Disorders

Imagine a man who drinks in dangerous situations, such as when driving, but doesn’t meet any other criteria for alcohol use disorders. According to the DSM-IV, this man is experiencing alcohol abuse.  The DSM-IV separates alcohol use disorders (AUDs) into alcohol abuse, characterized by serious negative consequences due to drinking, and alcohol dependence, characterized by physical symptoms related to drinking (APA, 1994); it also requires only one criterion for a diagnosis of alcohol abuse. However, under the new DSM-5 guidelines scheduled to be released this month, our hypothetical man’s diagnostic situation would change. The DSM-5 does not make separate diagnoses for alcohol abuse and dependence but makes a single diagnosis of AUD along a severity continuum and requires a minimum of two criteria. In addition, the DSM-5 substitutes a current criterion related to legal consequences of alcohol for a new criterion related to alcohol craving. This week the DRAM reviews a study that investigated how these potential changes will affect diagnosis and prevalence of AUDs (Agrawal, Heath & Lynsky, 2011).

Methods

• Researchers used Wave II data from 34,653 respondents to the National Epidemiological Survey on Alcohol and Related Conditions (NESARC) (Grant et al, 2004).

• NESARC used the Alcohol Use Disorders and Associated Disabilities Schedule (AUDADIS-IV) to collect information about both DSM-IV and DSM-5 alcohol use disorder criteria (Grant et al., 2003). Criteria included:

* Each criterion is represented by one to three AUDADIS items
** Includes an item measuring drinking and driving.

Results

·         9.7% of NESARC Wave II respondents qualified for alcohol abuse or alcohol dependence (DSM-IV), while 10.8% of the sample qualified for an AUD as defined by DSM-5.

·         As shown in Table 1, 1,033 people who did not qualify for alcohol abuse or alcohol dependence according to DSM-IV criteria qualified for an alcohol use disorder according to DSM-5 criteria. All of these were classified as having a moderate AUD.

o   Only 16.1% (n= 166) of these respondents endorsed the new DSM-5 “craving” criterion.

·         A total of 659 people who qualified for alcohol abuse or alcohol dependence according to DSM-IV criteria did not qualify for an AUD according to DSM-5 criteria.

o   The overwhelming majority of these people (96.5%; n= 636) endorsed the criterion for drinking in hazardous situations, in particular an item measuring drinking and driving.

Table 1. Diagnoses of AUDs According to DSM-IV and DSM-5 in the NESARC Wave II Sample (adapted from Agrawal, Heath & Lynsky, 2011)

Limitations 

• As with any survey that relies on self-report, it is possible that the answers did not accurately reflect actual behavior or symptoms.

Conclusions

The changes in the DSM-5 had a modest positive effect on the prevalence of AUD , though the addition of the craving criterion did not play a major role in this re-classification. Those who met criteria for AUD under DSM-IV but not under DSM-5 were most likely to endorse the criterion for drinking in hazardous situations. This, combined with the fact that the DSM-5 requires a minimum of two criteria, means that people like the man described above switch from a diagnosis of alcohol abuse to no diagnosis at all. Under DSM-5, these individuals could lose important treatment resources. In addition, there is still debate about whether alcohol abuse and alcohol dependence are two fundamentally different disorders that require different treatment approaches; by condensing them into one, we may lose some important information. Finally, future research might focus on further refining and exploring the divisions of “moderate” and “severe” AUD within DSM-5 diagnoses.

-Katerina Belkin

 

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References

Agrawal, A., Heath, A.C., Lynsky, M.T. (2011). DSM-IV to DSM-5: the impact of proposed revisions on diagnosis of alcohol use disorders. Addiction, 106, 1935-1943.

American Psychiatric Association (1994). Diagnostic and Statistical Manual of Mental  Disorders, 4th edn, revised. Washington, DC: American Psychiatric Association.

Grant B. F., Dawson D. A., Stinson, F. S., Chou, 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, 223–34.

Grant B. F., Dawson D. A., Stinson F. S., Chou P. S., Kay W., Pickering R. (2003). The   Alcohol Use Disorder and Associated Disabilities Interview Schedule-IV  (AUDADIS-IV): reliability of alcohol consumption, tobacco use, family history of     depression and psychiatric diagnostic modules in a general population sample. Drug and Alcohol Dependence, 71, 7–16.

The DRAM, Vol. 9(4) – For Airmen returning from conflict, the risk associated with TBI is anything but mild

Since 2001, the U.S. government has deployed more than 1.6 million military personnel to Afghanistan or Iraq (Tanielian, et al. 2008). Among veterans returning from these conflicts, doctors have observed nearly 200,000 cases of mild-traumatic brain injury (i.e., TBI; Fischer, 2013). Symptoms of mild-TBI include being confused/disoriented or experiencing memory loss for less than 24 hours, losing consciousness for up to thirty minutes, and normal medical imaging results (Fischer, 2013). Left untreated, TBI and other invisible wounds of war can lead to substance use disorders and a variety of psychiatric comorbid disorders (Tanielian, 2008). This week’s DRAM reviews an article that assessed the risk for addiction following a mild-TBI among active-duty military personnel (Miller et al., 2013).

Methods

• Researchers conducted a historical prospective study of nearly 50,000 US Air Force service members drawn from a sample of 500,000 who were on active duty for at least half a year during the years 2001 and 2008.  
  • The sample was made up of mostly white males with less than a high school diploma who have never been married.
  • Researchers identified both mild-TBI and addiction-related disorders by looking for the corresponding diagnostic codes in each airman’s medical records. They classified addiction-related disorders as having occurred at either 1-30 days, 31-179 days, or at least 180 days post-injury.
  • Researchers excluded airmen with a history of mild-TBI, other head injuries, or addiction-related disorder within 2 years prior to inclusion in the study.
• The study sample included a cohort of 5,065 airmen who experienced a mild-TBI during the study period and a comparison cohort of 44,733 airmen who experienced a different outpatient injury, but no TBI, during the study period.
• Researchers conducted Cox proportional hazard regression analyses (a form of survival analysis), while controlling for confounders, to determine the risk of substance abuse or dependence among those suffering from mild-TBI compared to the rest of the sample.

Results

• As Figure 1 shows, compared to other injured military personnel, those with mild-TBI were at increased risk for certain addiction-related disorders.

• Alcohol dependence was significantly elevated at all three time points post-diagnosis for personnel with mild-TBI.
  • The risk for alcohol dependence was most elevated for these personnel at 1-30 days post-diagnosis with consistent decreases at each subsequent time interval.
• For drug dependence and nondependent abuse of drugs or alcohol, the risk for mild-TBI personnel was only elevated for the period of observation immediately following the mild-TBI diagnosis.

Figure 1. Adjusted Hazard Ratios for Alcohol and Drug Abuse and Dependence Among Active-Duty US Airmen with Mild-TBI compared to Other-Injured Airmen (adapted from Miller et al., 2013)

Limitations

• Some airmen who truly had mild-TBI might have been misclassified as not having had mild-TBI. This could happen as a result of diagnostic errors or airmen’s reluctance to seek medical help. This misclassification would lead to an underestimate of risk for substance use disorders.
• Along the same lines, some airmen with addiction-related disorders might have been misclassified as not having had addiction-related disorders, particularly if those disorders go unreported and untreated. This misclassification of the outcome would lead to an underestimate of the risk for substance use disorders.

Conclusions

Military personnel who suffer mild traumatic brain injuries are at increased risk for substance abuse and dependence for a variety of substances, mainly in the period immediately following their injury. In particular, alcohol dependence might be a long lasting effect of mild-TBI. It’s possible airmen are abusing substances to cope with the symptoms of mild-TBI or because their injury has made them more susceptible to addiction. Substance misuse might inhibit the healing process and lead to long-term health problems, which could then exacerbate substance use problems. Screening for substance use and abuse following a mild-TBI should be considered for all military personnel at risk for TBI.

- John Kleschinsky

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

References

Fischer, H. (2013). U.S. military casualty statistics: Operation new dawn, operation iraqi freedom, and operation enduring freedom (RS22452). Washington DC: Congressional Research Service.

Miller, S.C., Baktash, S.H., Webb, T.S., Whitehead, C.R., Maynard, C., Wells, T.S., Otte, C.N., Gore, R.K. (2013). Risk for Addiction-Related Disorders Following Mild Traumatic Brain Injury in a Large Cohort of Active-Duty U.S. Airmen. American Journal of Pschiatry, EPub ahead of print 2013 Feb 22.

Tanielian T., Jaycox L.H., Schell T.R., Marshall G.N., Burnam M.A., Eibner C., Karney B.R., Meredith L.S., Ringel J.S., Vaiana M.E. (2008). Invisible wounds of war: summary and  recommendations for addressing psychological and cognitive injuries. Santa Monica, Calif, Rand Corp. http://rand.org/pubs/monographs/2008/RAND_MG720.1.pdf

The DRAM, Vol. 9(3) – Shots and Ladders: A look at drinking games and alcohol consumption

Drinking games are very prevalent among college students, and participation in drinking games predicts heavy drinking (Bosari, 2004). However, not all drinking games are created equal. There is a lack of research comparing which drinking games are preferred by various populations and how these different types of drinking games relate to alcohol consumption. This week’s DRAM reviews a study that examines different categories of drinking games and their association with drinking behavior in college students (LaBrie, Ehret, & Hummer, 2013).

Methods

• Researchers recruited students from two west-coast universities via a Web-based screening survey.
  • The current analysis included only participants who consumed at least one alcoholic drink per week (N = 3421, 68.3% of survey respondents)
• Participants provided demographic information, including Greek status, and answered four free response questions about their drinking game behavior:
  • “In the past 30 days, how many days did you play drinking games?”
  • “What drinking games did you play?”
  • “In which of the games listed above did you consume the most alcohol?”
  • “How many drinks did you typically consume when you played the above game?”
• Researchers used Internet searches and student focus groups to obtain descriptions of the games listed by respondents.
• Independent coders grouped the 100 distinct games in the sample into five categories of drinking games derived from previous drinking game research (Bosari, 2004). (See Table 1.)

Table 1: Distribution of drinking games played and peak drinking of students who reported playing drinking games in the past 30 days. (Adapted from LaBrie, et al., 2013)

Category	Description	% of sample who played in the past 30 days*	% of game players who reported game as the one in which they consumed the most alcohol (i.e., peak drinking game)	Average # of drinks consumed during game play by those listing game as peak drinking game
Targeted / Skill (e.g., Quarters)	Games based on individual ability where either the loser(s) drink or the winner selects a target to drink	26.3	36.5	5.16
Communal (e.g., Movie Drinking Game)	Group activities where drinking is based off external cues	4.7	46.8	n/a**
Chance (e.g., Kings)	Games based on random chance such as the value of playing cards or a roll of the dice	55.5	53.7	4.43
Extreme Consumption (e.g., Edward Fortyhands)	Games with minimal structure where the main object is simply to rapidly consume multiple drinks in a short time	7.2	58.3	5.83
Even Competition                  (e.g., Beer Pong)	Individual or team games where the losing side must drink as punishment	72.8	74.7	4.61

*70.1% of drinkers played drinking games in the past 30 days

**Result not reported by LaBrie, et al. because of small number of respondents engaging in game type.

Results

• Competition drinking games were the most commonly played drinking games, followed by Chance games, Targeted games, Extreme games, and Communal games (See Table 1).
• Three quarters of the respondents who reported playing Competition games also selected these as the games in which they drank the most alcohol (i.e., peak drinking game).
• Peak drinking game types differed significantly by gender, race, and Greek affiliation
  • Males, Caucasians, and Greek-affiliated students were more likely to report peak drinking during Competition games.
  • Females, non-Caucasians, and non-Greek-affiliated students were more likely to report peak drinking during Targeted drinking games.
• Students reported the highest number of drinks during peak drinking game drinking in Extreme Consumption games followed by Targeted and Skill games, then Chance and Even Competition games, which did not differ from each other, F (2, 2178) = 11.42, p < .001.

Limitations

• The data have the usual limitations of self-report and recall bias .
•  For peak drinking in games, it is difficult to determine whether students reported peak drinking due to the fact that they were more likely to play that category of game or because they drank more when playing games in that category as compared to other categories.
• The open ended nature of the drinking game question might have affected results. Students might not realize some games qualify as drinking games. For example, one of the extreme drinking games listed in the study, “Chugging” was likely not considered a game by many students.

Conclusions

This study provides an initial foray into how different drinking game categories relate to peak drinking in college students. The authors found that peak drinking differed by game type, and that different subpopulations were more likely to report peak drinking in different drinking game categories. However, there are issues with how each drinking game category was defined. For example, both Even Competition games and Target and Skill games involve player skill. It would be interesting to see an empirical derivation of drinking game categories using clustering of factor analytic techniques. The categories defined in the current study provide a starting point for future research on student drinking. Since students reporting peak drinking in Extreme Consumption games and Targeted and Skill games consumed more alcohol as compared to other drinking game categories, these games might be potential intervention targets.

- Jed Jeng

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

References

Bosari, B. (2004). Drinking games in the college environment: A review. Journal of Alcohol and Drug Education, 48(2), 29-51.

LaBrie, J. W., Ehret, P. J., & Hummer, J. F. (2013). Are they all the same? An exploratory, categorical analysis of drinking game types. Addictive Behaviors, 38(5), 2133-2139.

The DRAM, Vol. 9(2) – Drinking just to fit in? The effect of drinking norms and perceived social support on alcohol consumption in college students.

Alcohol norms are one of the strongest predictors of alcohol consumption: people who expect that others drink heavily tend to drink heavily themselves. Many factors can influence this relationship, including perceived social support. Research indicates that those who perceive low support from their social groups tend to be more greatly influenced what they think other people are doing, as they change their behavior as an attempt to ‘fit in’ (Cullum, O’Grady, & Tennen, 2011). Today’s DRAM reviews a study that explores the relationship between alcohol norms, behavior, and perceived social support in college students.  (Cullum, O’Grady, Sandoval, Armeli, & Tennen, 2013).  The authors predicted that perceived social support would moderate the effect of alcohol norms on drinking behavior among college students.

Methods

• The sample consisted of 498 college students, primarily freshman and sophomores, recruited from introductory psychology classes for a larger study of health behaviors.
• Participants kept a daily online diary of their drinking behavior for 30 days. Researchers converted the data from the diary into two outcome measures.
  • Drinking frequency: the total number of days on which a participant drank, divided by 30 days.
  • Drinking quantity: the total number of drinks divided by the number of days the participant reported any drinking (i.e., drinks per drinking day).
• Participants also completed a survey assessing drinking norms and perceived social support at the beginning of the study.
  • For drinking norms, participants answered one question about how frequently they expected college students of their gender consumed (None, 1-3 drinks per month, 1-3 drinks per week, 1 drink per day, or More than 1 drink per day).
• For perceived social support, participants completed the five-item Perceived Social Support from Friends Scale (Procidano & Heller, 1983).
• The scale includes questions such as “My friends give me the moral support I need” answered on a Likert-type scale from 1 (Strongly Disagree) to 7 (Strongly Agree).
• Researchers then entered the data into a multiple regression model to determine how social support and alcohol norms related to drinking behavior.

Results

•  Researchers found that drinking norms significantly predicted drinking frequency, b = .04, SE = .01, p = .001, and drinking quantity, b = .33, SE = .16, p = .03. Those reporting higher drinking norms also reported higher drinking frequency and quantity. Social support did not relate directly to either measure.
• There was a significant social support x drinking norm interaction in relation to both frequency and quantity. Figure 1 illustrates the nature of the interaction.
  • For both drinking measures, the behavior of participants who reported high social support did not vary by the norms they held; however, participants who reported low social support demonstrated much lighter drinking behavior if they held low norms and much heavier drinking behavior if they held high norms.  

Figure 1: Drinking quantity and frequency as a function of perceived drinking norms and social support. SS = social support. (Reprinted with permission from Cullum et al., 2013).

Note: The Y axis represents the number of drinks difference from the sample average for drinking quantity, and the percent difference from the sample average for drinking frequency.

Limitations

• The study relies entirely on self-report data.
• The drinking norm question was very vague, asking about college students in general. The results might be different if researchers used a more targeted version of the question, asking about students at a specific school or specific class year.
• We cannot conclude causality based on the results of this study. While there might be a relationship between norms, social support and drinking behavior, we cannot be sure which variables influence which. For example, those with limited social support and low norms might have fewer friends and thus fewer opportunities for drinking.

Conclusions

The results of this study show that drinking norms are significantly associated with drinking behavior, particularly for college students with low social support. Those with low social support and high drinking norms showed on average the highest drinking behavior in the sample. These findings provide greater insight into the complicated world of social drinking in colleges and the motivations behind drinking. Studies like these might eventually be used by colleges to create interventions that identify and target students most at risk for drinking problems—specifically, those who both feel isolated and believe others are drinking heavily.

-Daniel Tao

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

References

Cullum, J., O’Grady, M., Sandoval, P., Armeli, S., Tennen, H. (2013). Ignoring norms with a little help from my friends: Social support reduces normative influence on drinking behavior. Journal of Social and Clinical Psychology, 32(1), 17-33.

Cullum, J., O’Grady, M., Tennen, H. (2011). Affiliation goals and health behaviors. Social and Personality Psychology Compass, 5, 694-705.

Procidano, M.E., Heller, K. (1983). Measures of perceived social support from friends and from family: Three validation studies. American Journal of Community Psychology, 11, 1-24.

The DRAM, Vol. 9(1) - Attempted suicide and alcohol use disorder in the elderly

Many studies have investigated comorbidity between substance use disorders and other mental health issues. In particular, some researchers have studied possible associations between alcohol use disorder (AUD) and attempted and completed suicide in various populations (Conwell et al, 2000; Chiu et al., 2004). This week’s BASIS reviews a study that investigates this association in an elderly population in Western Sweden (Morin et al., 2012).

Methods

• The researchers targeted people 70 years or older who were admitted to a medical emergency department after a suicide attempt. One hundred thirty-three were invited to participate, and 103 (77%; 47 men) agreed to participate.
• The researchers selected comparison subjects from the Prospective Population Study of Women and the Gerontological and Geriatric Population Studies. They selected 4 comparison participants each for 99 patients and 3 comparison participants each for the remaining 4 patients, for a total of 408 controls.
• All participants completed a cognitive evaluation and an interview with a psychologist regarding current and previous alcohol consumption and any previous suicide attempts. For all participants, researchers reviewed medical records for evidence of AUD[1] and other previous episodes of mental illness.

•  
  • The patients also answered questions regarding physical health, symptoms of depression, circumstances at the time of the suicide attempt, and psychiatric symptoms during the month prior to the suicide attempt.
  • The comparison participants answered questions about psychiatric symptoms during the month prior to the interview.

• Morin and his colleagues used logistic regression to compare lifetime AUD prevalence between the patient and comparison groups. In an additional multivariate model they controlled for histories of previous suicide attempts and/or psychiatric treatment.

Results

• As Figure 1 shows, twenty six percent of those who had a hospital-treated suicide attempt had an AUD, while only four percent of those who did not have a hospital-treated suicide attempt had an AUD, OR = 10.5 95% Confidence Interval (CI) [4.97,22.53], p < .001.

• After controlling for previous suicide attempt and history of psychiatric treatment, the difference between the hospital-treated suicide attempt rates for those with AUD and without AUD remained significant, OR = 5.7, 95% CI [1.92,16.98], p < .01.

Figure 1: Number of people with and without alcohol use disorder, patients versus comparison participants (Morin et al., 2012)

Limitations

• The patient group was recruited in emergency departments. As a result, the study did not include elderly people who attempted suicide but did not receive emergency department treatment.
• Given their circumstances, the patients may have been more open about problems with alcohol than the interviewees in the comparison group. They may have been less likely than comparison participants to underreport alcohol use disorders.
• It is not clear from the study whether the factors that led to the suicide attempt also led to alcohol use disorder, or if the alcohol use disorder led to the circumstances that resulted in the suicide attempt.

Conclusions

The results of this study suggest that within the elderly population, those with AUD are significantly more at risk for attempting suicide than those without AUD. Future studies will be necessary to determine the directions or mechanisms of causality between AUD and suicidal tendencies. Based on just the association, however, the authors recommend that clinicians who work with elderly patients evaluate these patients for alcohol use, depressive symptoms and suicidal feelings. Evidence of one of these risk factors (e.g., AUD) ought to lead to further screening for the others (e.g., depressive symptoms or suicidal feelings).

-Matthew Tom

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

References

Conwell, Y., Lyness, J.M., and Duberstein, P. (2000). Completed suicide among older patients in primary care practices: a controlled study.  Journal of the American Geriatric Society. 48(1), 23-29.

Chiu, H.F.K. and Yip, P.S.F., Chi, I., et al. (2004). Elderly suicide in Hong Kong: a case-controlled psychological autopsy study. Acta Psychiatrica Scandinavica. 109 (4), 299-305.

Morin, J., Wiktorsson, S., Marlow, T., Olesen, P.J., Skoog, I., Waern, M. (2012). Alcohol use disorder in elderly suicide attempters: a comparison study. American Journal of Geriatric Psychiatry, 2012 Apr 16, e-publication ahead of print.

Skoog, I. (2004). Psychiatric epidemiology of the old age: the H70 study – the NAPE lecture 2003. Acta Psychiatrica Scandinavica, 109 (1), 4-18.

Lissner, L., Skoog, I., Anderson, K., et al. (2003). Participation bias in longitudinal studies: experience from the Population Study of Women in Gothenburg, Sweden. Scandanavian Journal of Primary Health Care, 21(4), 242-247.

Pfaff, J.J., Almeida, O.P., Witte, T.K., et al. (2007). Relationship between quantity and frequency of alcohol use and indices of suicidal behavior in an elderly Australian sample. Suicide & Life Threatening Behavior, 37(6), 616-626.

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[1] The researchers used a broad definition of AUD in accordance with DSM-IV. It included cases with social complications of problematic alcohol use, cases with physiological dependency, and cases with multiple admissions for detox treatment. Interviewees could be classified as having lifetime AUD based on either interviews or medical records.

The DRAM, Vol. 8(11) – What do you think when you drink? The relationship between implicit associations, explicit expectations, and drinking behavior

People’s underlying thoughts and expectations toward drinking are related to self-reported alcohol consumption (Wiers, Van Woerden, Smulders, & De Jong, 2002).However, few previous studies have examined the effect of cognitions on actual drinking behavior in naturalistic settings. This week’s DRAM reports on a semi-naturalistic study examining the associations between implicit (i.e., not consciously elaborated) and explicit (i.e., consciously elaborated) beliefs about alcohol and observed drinking behavior (Larsen, Engels, Wiers, Granic, & Spijkerman, 2012). 

Methods

• One hundred twenty-one undergraduate students (64 women) who reported drinking alcohol participated in a semi-naturalistic drinking study in the Netherlands.
• Participants completed an implicit association test (IAT) measuring implicit attitudes toward alcohol, a questionnaire on alcohol use (weekly consumption) and a measure of explicit positive expectations toward drinking (i.e., the degree to which they expected to experience various positive outcomes as a result of drinking). During the IAT task, participants had to match target concepts such as “soda” and “alcohol” to attribute concepts, such as “positive” and “negative” as quickly as possible. Positive scores indicated a stronger association between alcohol and positive attributes.
• Next, a participant and a confederate (always of the same gender) sat in a bar for a waiting period of 30 minutes. Both non-alcoholic and alcoholic drinks were available to them. A confederate drank either two sodas (control condition), or one alcoholic drink + one soda (light condition) or multiple alcoholic drinks (heavy condition). Researchers measured how many alcoholic drinks each participants consumed; this served as the outcome measure for the study.

Results

• Positive implicit associations demonstrated significant positive correlations with explicit positive expectations about alcohol (r = .25, p < .01), and with self-reported weekly drinking (r = .14; p = .05).
• Table 1 displays the results of a hierarchical regression testing the prediction of observed drinking behavior from gender, implicit associations, explicit positive expectancies, weekly self-reported drinking, and the confederate’s behavior. The results primarily showed that participants drank more when
  • they were with a heavy-drinking confederate (B = .52; p <.01);
  • they held more positive explicit  expectancies about alcohol (B = .17; p < .01).
• By contrast, implicit associations were not related to observed drinking.
• Participants in the heavy drinking confederate condition who reported high positive expectancies toward alcohol consumed more than twice as much alcohol (M_{alcoholic drinks consumed} = 1.62, SE = .26) compared to participants with low positive expectancies in the same condition (M = .68; SE = .24), F _(1,36) = 6.3, p < .01. 

Table 1: Results of hierarchical regression on the association between independent variables and observed drinking. 

	Observed drinking (B)
Gender  (female)	-.12
Control vs. light confederate drinking	.17
Control vs. heavy confederate drinking	.52**
Positive implicit associations	.07
Explicit positive expectancies	.17*

* p < .0.05; ** p < .001

Limitations   

• Experiments conducted in lab settings benefit from experimental controls, but the artificial environment also decreases the reality of the situation for participants. potentially decreasing the generalizability of the results and the validity of the findings.
• The study used a convenience sample (students); this limits the generalizability of the results to other populations. 

Conclusions   

These results suggest that positive expectancies toward alcohol predict “real-life” alcohol consumption in a dyadic setting, as does peer drinking behavior. People who hold positive expectations about drinking will drink more than others, particularly when in a heavy drinking environment.  By contrast, implicit associations about alcohol do not appear to predict actual drinking behavior when positive expectations are controlled.  This implies that in social settings, individual’s expectations about drinking may be a better predictor of actual behavior than implicit attitudes. Future research could investigate whether implicit associations hold value for prediction drinking behavior in contexts where self-report might be particularly biased.

-Julia Braverman

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References

Larsen, H., Engels, R. C. M. E., Wiers, R. W., Granic, I., & Spijkerman, R. (2012). Implicit and explicit alcohol cognitions and observed alcohol consumption: Three studies in (semi)naturalistic drinking settings. Addiction, 107(8), 1420-1428. doi: 10.1111/j.1360-0443.2012.03805.x

Wiers, R. W., Van Woerden, N., Smulders, F. T. Y., & De Jong, P. J. (2002). Implicit and explicit alcohol-related cognitions in heavy and light drinkers. Journal of Abnormal Psychology, 111(4), 648-658. doi: 10.1037/0021-843x.111.4.648

The DRAM, Vol. 8(10) - The more you drink, the more you (think you can) drive? The effects of objective and subjective intoxication on DUI behavior in college students

The majority of research on predictors of DUI behavior focuses on individual-level factors, such as personality, sensation seeking, and comorbid psychiatric disorders (Nelson & Tao, 2012). This research, however, does not take into account event-specific factors that occur immediately before DUI behavior. This week’s DRAM reviews a study that investigates some of these situational factors. In this study, Quinn and Fromme (2012) examine how objective intoxication (i.e., BAC), and subjective intoxication affect the probability of DUI behavior among a sample of college students. In particular, researchers were interested in whether the disinhibiting effects of alcohol increased the probability of DUI behavior.

Methods

• The sample consisted of 1,350 first year college students who were already participating in a four-year longitudinal study on DUI behavior.
  • Researchers included participants in the current analysis if they reported any drinking behavior and completed at least 14 diary entries per year.
• Participants kept an online diary everyday regarding their past-day alcohol consumption, subjective intoxication (i.e., how intoxicated they felt they were), and DUI behavior. Participants completed this diary for up to 30 days per year, for four consecutive years.
  • Researchers estimated BAC using a formula including body weight, the number and type alcoholic drinks, and the duration of the drinking episode.
  • Participants rated how drunk they thought they had become on a scale from 0 to 100, with 0 being not at all drunk, and 100 being extremely drunk.
• Researchers modeled the effects of BAC and subjective intoxication on DUI behavior using generalized estimating equations.

Results

• More than 25% of participants reported DUI behavior in their first year of college and more than 33% reported DUI behavior in their fourth year of college.
• Researchers found a significant main effect of estimated BAC on probability of DUI behavior, Odds Ratio (OR) = 1.02, 95% Confidence Interval (CI) [1.01-1.03]. As estimated BAC increased, so did the probability of DUI behavior.
• Researchers found a significant interaction effect between daily estimated BAC and subjective intoxication on probability of DUI behavior, OR = 0.996, 95% CI [0.99-0.998]. As estimated BAC increased, the probability of DUI behavior increased more rapidly for those reporting low subjective intoxication compared to those reporting high intoxication (see Figure 1).

Figure 1: Effect of estimated BAC and subjective intoxication on probability of DUI (Reproduced with permission from Quinn & Fromme, 2012).

Limitations

• The study relies on retrospective self-report. Participants reported their drinking behavior the day after an episode. Additionally, diary entries could be completed up to a week after the drinking event. These reports might have been prone to memory or self-representation biases.
• The study uses an estimated BAC. This does not take into account many factors that affect BAC, such as meals consumed.
• Participants only rated their highest subjective intoxication. This does not necessarily represent their rated intoxication immediately before the DUI behavior.
• While the results were significant, the effect size is relatively small, meaning that the variables (i.e., BAC and subjective intoxication) and their interaction) only explain a small amount of the variation in DUI behavior.

Conclusions

This study represents an advance in our understanding the complicated decision making process behind DUI behavior. More generally, this study underscores the notion that not just individual-level factors, but also situational factors also contribute to DUI behavior. The probability of DUI behavior increased with estimated BAC. This effect was more pronounced for those who did not think they were intoxicated. Previous research suggests that alcohol impairs decision making processes, so that only the most salient or obvious cues are processed while intoxicated (Casbon, Curtin, Lang, & Patrick, 2003). For participants who feel drunk, this high subjective intoxication is a highly salient sign that tells them they should not be driving (Moss & Albery, 2009). However, for people who do not feel as drunk, this internal “warning system” might be silent and easily overridden by the impulse or desire to drive. The next step in this line of research is to further investigate factors that contribute to or reduce subjective intoxication among people with elevated BACs.

-Daniel Tao

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References

Casbon, T.S., Curtin J.J., Lang, A.R., Patrick, C.J. (2003). Deleterious effects of alcohol intoxication: Diminshed cognitive control and its behavioral consequences. Journal of Abnormal Psychology, 112, 476-487.

Moss, A.C., Albery, I.P. (2009). A dual-process model of the alcohol-behavior link for social drinking. Psychological Bulletin, 135, 516-530. 

Nelson, S. E., & Tao, D. (2012). Driving under the influence: Epidemiology, etiology, prevention, policy, and treatment. In H. J. Shaffer, D. A. LaPlante & S. E. Nelson (Eds.), The APA Addiction Syndrome Handbook (Vol. 2. Recovery, Prevention, and Other Issues, pp. 365-407). Washington, DC: American Psychological Association Press.

Quinn, P.D., Fromme, K. (2012). Event-level associations between objective and subjective alcohol intoxication and driving after drinking across the college years. Psychology of Addictive Behaviors, 26(3), 384-392. DOI: 10.1037/a0024275.

The DRAM, Vol. 8(9) – The skinny on bariatric procedures and alcohol use disorder

Bariatric surgery is often proposed as a treatment for severe obesity. More than 200,000 such procedures are performed in the United States each year (Elliott, 2012). Some bariatric procedure patients notice an increased sensitivity to alcohol after surgery, potentially because the procedure causes alcohol to be absorbed faster (Klockhoff, Naslund and Jones, 2002). In this issue of the DRAM, we review a study that investigates a possible link between bariatric surgery and alcohol use disorder (King et al., 2012).

Methods

• King et al. (2012) recruited 4,476 patients, age 18 or older, between February 2006 and February 2009 who were seeking a first bariatric procedure at one of 10 centers in the United States.
• The participants were asked to complete the Alcohol Use Disorder Identification Test (AUDIT) before the procedure, one year after the procedure, and two years after the procedure.
• Patients (n = 1,945) who were included in the dataset (1) completed the AUDIT before the procedure, (2) underwent the procedure, and then (3) completed the AUDIT again at least once after the procedure.
• Researchers tested models predicting post-procedure alcohol use disorder (AUD) from pre-procedure substance use behaviors and other psychosocial variables.

Results

• As Figure 1 shows, the proportion of participants qualifying for AUD was significantly higher in the 2^nd year post-procedure than either pre-procedure or in the 1^st year post-procedure.

• Almost two-thirds (62.3%) of those who qualified as having an AUD before their procedure also qualified for an AUD sometime in the two years after the procedure. Eight percent (7.9%) of those who did not qualify for an AUD pre-procedure qualified for an AUD in the two years after the procedure.
• Predictors for detection of post-procedure AUD included:
  • Pre-procedure AUD (odds ratio [OR] = 4.5 at age 20, p < .001; OR = 11.14 at age 45, p < .001);
  • Male gender (OR =2.14, p < .05);
  • Pre-procedure Smoking (OR = 2.58, p < .05);
  • Pre-procedure regular alcohol consumption (at least 2 drinks per week) (OR = 6.37, p < .001);
  • Pre-procedure recreational drug use (OR = 2.38, p < .05);
  • Outcome two years after procedure, rather than one year (OR = 1.57, p < .001).

Figure 1: AUD Prevalence before, 1 year after, and 2 years after bariatric procedure.

Note. Percents are based on the 1,389 participants who responded to the relevant questions on the AUDIT at all three time periods (before procedure, one year after procedure, and two years after procedure).
2yr Post-procedure prevalence significantly different than other two time points, p < .01.

Discussion

This study investigated alcohol use disorder rates in bariatric surgery patients both before and after surgery. One important finding was that the prevalence of AUD two years after surgery was greater than the prevalence rate one year after surgery. Future studies should continue to investigate the longer-term effects of bariatric surgery. Clearly, not all patients who undergo bariatric procedures go on to develop AUD; future research should also examine factors that increase or decrease risk among this population.

-Matthew Tom

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

References

Barbor, T.F., Higgins-Biddle, J.C., Saunders, J.B., and Monteiro, M.G. (2001). AUDIT The Alcohol Use Disorders Identification Test: Guidelines for Use in Primary Care. 2nd ed. Geneva, Switzerland: World Health Organization.

Elliott, V.S. (2012, April). Bariatric surgery maintains, doesn’t gain. American Medical News, http://www.ama-assn.org/amednews/2012/04/23/bisa0423.htm.

King, W.C., Chen, J.Y., Mitchell, J.E., Kalarchian, M.A., Steffen, K.J., Engel, S.G., Courcoulas, A.P., Pories, W.J., and Yanovski, S.Z (2012). Prevalence of alcohol use disorders before and after bariatric surgery. Journal of the American Medical Association, 307 (23), 2516-2525.

Klockhoff, H., Naslund, I. and Jones, A.W. (2002). Faster absorption of ethanol and higher peak concentration of women after gastric bypass surgery. British Journal of Clinical Pharmacology, 54 (6), 587-591.

The DRAM, Vol. 8(8) - When is a binge a binge? College students’ understanding of binge drinking

According to the NIAAA (2012a), four out of five college students drink alcohol, and two out of five report binge drinking. Binge drinking is excessive drinking within about a 2 hour timeframe resulting in a BAC level of at least 0.08g/dL (NIAAA, 2012b). Researchers, however, often use a different definition of binge drinking: 5 drinks for men or 4 drinks for women consumed on one occasion (Wechsler, Davenport, Dowdall, Moeykens, & Castillo, 1994). Importantly, it is unknown how college students define binge drinking. This week, the DRAM reviews a study that assessed college students’ current definitions and understanding of binge drinking (Bonar, Young, Hoffman, Gumber, Cummings, Havlick, & Rosenberg, 2012).

Methods

• 424 undergraduates (299 women, 125 men) at a large state university responded to an email containing a survey and questionnaire.
• The Binge Drinking Definition Survey (BDDS) consisted of four closed and two open-ended questions:
  • The two open-ended questions asked students to define a binge and provide the rationale for their definition. Researchers coded these answers based upon previous research;
  • The four closed questions asked participants to indicate how many beers, shots, glasses of wine, and servings of combined alcohol types constituted a binge.
• The researchers also assessed demographics and drinking history, including frequency, quantity, and alcohol-related problems. To evaluate whether the mean number of drinks students thought comprised binge drinking varied with gender, alcohol type, and number of binges (i.e., 5+/4+ drinks on one occasion) in the past two weeks, researchers used a mixed model ANOVA.

Results

• As Figure 1 shows, the number of drinks that participants thought was needed for a binge varied by the type of alcohol, as well as the gender and drinking status of the participant.
  • Participants thought more beers were needed for a binge than shots, glasses of wine, or other combinations of alcohol, F(3,403) = 75.5, p < .001.
  • Students who binged less frequently during the past two weeks reported that fewer drinks were necessary to comprise a binge than students who binged more frequently, F(2, 403) = 3.3, p < .05,
  • Male students reported more drinks necessary to comprise a binge than did female students, F(1,403) = 6.6, p < .05.
  • About a quarter of students (23%) referred to specific drinking motivations in their free response definitions of binge drinking (e.g., “drinking to forget…” – Bonar et al. 2012, p. 192).

Figure 1 - Mean number of drinks participants thought comprised binge drinking based on types of alcohol, their past two week binge status, and their gender (adapted from Bonar et al., 2012).

Note. Error bars represent standard deviations.

Limitations

• The sample was recruited from a single university so it might not be representative of all college students in the US.
• Drinking history information was based on self- report so it might be subject to self-report biases.

Discussion

This study highlights the importance of considering student perceptions when attempting to design effective alcohol education and prevention programs in colleges. For example, some students incorrectly assumed that more beers were required for a binge than other alcohol types, suggesting the importance of educating college students about the equivalencies of different types of alcohol and about the alcohol content in each standard drink. Participants in this sample also highlighted drinking motivation as an important component of binge drinking. Though researchers’ definitions of binge drinking do not include motivation, students are correct to highlight its importance. For example, people who drink to cope with emotional problems tend to have more drinking problems than their counterparts absent such motivation. Therefore, prevention programs might be well-served by addressing the negative motivations for drinking among students and providing alternate coping mechanisms.

-Elisha Thapa

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References

Bonar, E. B., Young, K. M., Hoffmann, E., Gumber, S., Cummings, J. P., Pavlick, M., & Rosenberg, H. (2012) Quantitative and Qualitative Assessment of University Students’ Definitions of Binge Drinking. Psychology of Addictive Behaviors, 26 (2), 187-193. doi: 10.1037/a0026440

Wechsler, H., Davenport, A., Dowdall, G., Moeykens, B., & Castillo, S. (1994). Health and behavioral consequences of binge drinking in college: A national survey of students at 140 campuses. The journal of the American Medical Association, 272, 1672-1677. doi:10.1001/jama.272.21.1672

National institute on alcohol abuse and alcoholism (NIAAA). (2012a). College drinking. Retrieved July 03, 2012, from http://pubs.niaaa.nih.gov/publications/CollegeFactSheet/CollegeFactSheet.pdf

National institute on alcohol abuse and alcoholism (NIAAA). (2012b). Moderate & binge drinking. Retrieved July 03, 2012, from http://www.niaaa.nih.gov/alcohol-health/overview-alcohol-consumption/moderate-binge-drinking