Discussion: Treatment of Substance Use Disorders Of the substance disorders, alcohol-related disorders are the most prevalen
Discussion: Treatment of Substance Use Disorders
Of the substance disorders, alcohol-related disorders are the most prevalent even though only a small percentage of individuals actually receive help. Recidivism in the substance treatment world is also very high. As research into treatment has developed, more and more evidence shows that genes for alcohol-metabolizing enzymes can vary by genetic inheritance. Women have been identified as particularly vulnerable to the impacts of alcohol. Native Americans, Asians, and some Hispanic and Celtic cultures also have increased vulnerability to alcohol misuse.
Even with these developments, treatment continues to spark debate. For many years, the substance use field itself has disagreed with mental health experts as to what treatments are the most effective for substance use disorders and how to improve outcomes. The debate is often over medication-assisted treatment (MAT) versus abstinence-based treatment (ABT). Recently the American Psychiatric Association has issued guidelines to help clinicians consider integrated solutions for those suffering with these disorders. In this Discussion, you consider your treatment plan for an individual with a substance use disorder.
To prepare: Read the case provided by your instructor for this week’s Discussion and the materials for the week. Then assume that you are meeting with the client as the social worker who recorded this case.
By Day 3
Post a 300- to 500-word response in which you address the following: (PLEASE RESPOND TO EACH BULLET POINT)
- Provide the full DSM-5 diagnosis for the client. Remember, a full diagnosis should include the name of the disorder, ICD-10-CM code, specifiers, severity, and the Z codes (other conditions that may need clinical attention). Keep in mind a diagnosis covers the most recent 12 months.
- Explain the diagnosis by matching the symptoms identified in the case to the specific criteria for the diagnosis.
- Describe the assessment(s) you would use to validate the client’s diagnosis, clarify missing information, or track her progress.
- Summarize how you would explain the diagnosis to the client.
- Explain how you would engage the client in treatment, identifying potential cultural considerations related to substance use.
- Describe your initial recommendations for the client’s treatment and explain why you would recommend MAT or ABT.
- Identify specific resources to which you would refer the client. Explain why you would recommend these resources based on the client’s diagnosis and other identity characteristics (e.g., age, sex, gender, sexual orientation, class, ethnicity, religion, etc.).
Note: You do not need to include an APA reference to the DSM-5 in your response. However, your response should clearly be informed by the DSM-5, demonstrating an understanding of the risks and benefits of treatment to the client. You do need to include an APA reference for the assessment tool and any other resources you use to support your response.
Required Readings
Morrison, J. (2014). Diagnosis made easier (2nd ed.). New York, NY: Guilford Press.
- Chapter 15, “Diagnosing Substance Misuse and Other Addictions” (pp. 238–250)
- American Psychiatric Association. (2013).Substance related and addictive disorders. In Diagnostic and statistical manual of mental disorders (5th ed.). Arlington, VA:Author. doi: 10.1176/appi.books.9780890425596.dsm.16
CASE OF RALPH
INTAKE DATE: May 2021
DEMOGRAPHIC DATA:
This is a voluntary admission for this 32 years old Caucasian male. This is Ralph’s first psychiatric hospitalization. Ralph has been married for 6 years and lives with his husband, Richie and son. Ralph has a two-year degree in nursing and works as an RN. Religious affiliation is agnostic.
CHIEF COMPLAINT:
"My life is spiraling out of control. I do not want to lose my family”.
HISTORY OF ILLNESS:
This admission was precipitated by Ralph’s increased depression with passive suicidal ideation in the past three months prior to admission. Ralph has had a past history of alcohol binges and these binges are intensified when there is a need for coping mechanisms in times of stress. Ralph was starting vacation from work just prior to admission and recognized that if he did not come to the hospital for treatment of depression and alcoholism, he worries he would have a serious alcohol binge. Ralph reports that in the past three months he has experienced sad mood, fearfulness, and passive suicidal ideation. He denies a specific suicidal plan. Ralph’s husband reports that during these past three months prior to admission, Ralph made a verbal suicidal threat.
Ralph reports he has been increasingly withdrawn/non-communicative. His motivation has decreased, and he finds himself "sitting around and not interested in doing chores at home". He reports decreased concentration at work and increased distractibility. Ralph has experienced increased irritability, decreased self-esteem, and feelings of guilt/self-blame. There is no change in appetite, but Ralph reports an intentional weight loss of 20 pounds in the last 5 months with dieting. Ralph states that for many years he doesn’t really sleep ever since he worked double shifts when requested. Ralph reports his normal sleep pattern for many years has been generally three hours of unbroken sleep. Ralph reports past history of euphoria, although his husband reports to the intake worker having observed periods when Ralph’s mood is elevated; then in the next few hours, Ralph appears out of control with poor impulse control, increased arguing, temper tantrums and alleged shoving and pushing him and his son. After which Ralph feels tired and ends up sleeping more than average.
Ralph denies suicidal ideation at the present time while on the evaluation unit.
Ralph reports a history of some alcohol binges in the past. He began drinking beer in 2010, after he turned 21 years old. Ralph reports that until two years prior to admission his pattern of drinking was to get drunk with his social group approximately twice per month. He denies a history of blackouts. He admits to the alcohol binges and heavy use of cocaine (snorting and freebasing on weekends) in the past. Ralph has received a charge of driving while intoxicated in 03/2014 and had lost his driver’s license for six months. Ralph reports using alcohol as a coping mechanism for stress (reporting that he will only drink on weekends now).
PAST PSYCHIATRIC HISTORY:
Ralph was seen on an outpatient basis by Dr. S for a period of two months prior to admission. He was being seen for individual counseling because of depression. Dr. S recently referred Ralph for inpatient rehabilitation.
MEDICAL HISTORY:
In 2017, Ralph sustained a head injury when he hit his head on a coffee table. Ralph had a past history of fractured toes with pins being inserted in the third and fourth digits in his right foot after an accident in which he crushed his foot at work. Ralph denies a past history of seizures.
Ralph has had a weight loss of approximately 20 pounds secondary to dieting. Ralph is allergic to Codeine.
FAMILY MEDICAL AND PSYCHIATRIC HISTORY:
Father and grandfather have a history of cardiovascular disease and alcoholism.
PSYCHOSOCIAL AND DEVELOPMENTAL HISTORY:
Ralph reports that while growing up his parents maintained a satisfactory relationship. Father reportedly worked nights and slept during the day. Ralph did not have much contact with his father but now enjoys a close relationship with him. He states he has always had his parents support.
During Ralph’s school years, he reports he was an underachiever in elementary school. He denies having had a history of discipline problems or hyperactivity. He states he did well in high school and earned grades of A’s and B’s. Ralph played football in HS. After completing high school, Ralph furthered his education and earned his license as a registered nurse. He states he graduated at the top of his class from nursing school.
Ralph has been married for 6 years. Ralph and his husband have one adopted son, age 4. Ralph states he feels invested as a parent and feels close to his son.
Leisure time activities Ralph has enjoyed in the past include playing softball, reading, playing poker, and watching football. His husband has complained that he is doing less of that now since he is drinking more. Ralph states he has several close friends.
CURRENT FAMILY ISSUES AND DYNAMICS:
Ralph’s husband reports that Ralph’s difficulties began to get worse a few months ago due to Ralph’s increasing erratic behavior. Husband states that Ralph has been suffering from mood swings where he is "very up" and feeling great, firm in his direction and then within the next few hours, he is often out of control, arguing, throwing temper tantrums, pushing and shoving, and becoming verbally abusive.
Husband states Ralph has been drinking for several years in the amount of a 12 pack of beer per day plus shots of hard liquor. Although Ralph reported he has been using cocaine on and off for about two years, husband states he does not think that Ralph is presently using cocaine. At one point, after threats from his husband, Ralph told him that he had gone to a clinic for outpatient rehabilitation, but he did not believe him.
Husband describes Ralph as "extremely depressed" now and says Ralph states, "life is over…I wish I was dead…everything I touch turns to garbage. Husband adds that Ralph suffers from poor self-esteem, lack of sleep, and an extremely boastful attitude. In terms of strengths, he is a good father, compassionate, creative, and can be an outstanding person.
Husband reports Ralph always had a bad relationship with his mother. Ralph is close to his father who is reported to have an alcohol problem and was allegedly loud and intimidating.
MENTAL STATUS:
Ralph presents as a casually dressed male who appears his stated age of 32. Posture is relaxed. Facial expressions are appropriate to thought content. Motor activity is appropriate. Speech is clear and there are no speech impediments noted. Thoughts are logical and organized. There is no evidence of delusions or hallucinations. Ralph admits to a recent history of passive suicidal ideation without a plan, but denies suicidal or homicidal ideation at the present time. Ralph admits to a history of decreased need for sleep but denies euphoric episodes. His husband has observed a history of notable mood swings. No manic-like symptoms are observed at the time of this examination.
On formal mental status examination, Ralph is found to be oriented to three spheres. Fund of knowledge is appropriate to educational level. Recent and remote memory appear intact. Ralph was able to calculate serial 7’s. In response to three wishes, Ralph replied "I wish that my marriage was better, that my son would be happy, and that someone would give me a million dollars.”
,
Vulnerability for Alcohol Use Disorder and Rate of Alcohol Consumption Joshua L. Gowin, Ph.D., Matthew E. Sloan, M.D., M.Sc., Bethany L. Stangl, Ph.D., Vatsalya Vatsalya, M.D., M.Sc., Vijay A. Ramchandani, Ph.D.
Objective: Although several risk factors have been identified for alcohol use disorder, many individuals with these factors do not go on to develop the disorder. Identifying early phenotypic differences between vulnerable individuals and healthy control subjects could help identify those at higher risk. Binge drinking, defined as reaching a blood alcohol level of 80 mg%, carries a risk of negative legal and health out- comes and may be an early marker of vulnerability. Using a carefully controlled experimental paradigm, the authors testedthehypothesisthatriskfactorsforalcoholusedisorder, including family history of alcoholism, male sex, impulsivity, and low level of response to alcohol, would predict rate of binging during an individual alcohol consumption session.
Method: This cross-sectional study included 159 young so- cial drinkers who completed a laboratory session in which they self-administered alcohol intravenously. Cox proportional
hazards models were used to determine whether risk factors for alcohol use disorder were associated with the rate of achieving a binge-level exposure.
Results:Agreaterpercentageofrelativeswithalcoholism(hazard ratio: 1.04, 95% CI=1.02–1.07), male sex (hazard ratio: 1.74, 95% CI=1.03–2.93), and higher impulsivity (hazard ratio: 1.17, 95% CI=1.00 to 1.37) were associated with a higher rate of binging throughout the session. Participants with all three risk factors had the highest rate of binging throughout the session compared withthelowestriskgroup(hazardratio:5.27,95%CI=1.81–15.30).
Conclusions: Binge drinking may be an early indicator of vulnerability to alcohol use disorder and should be carefully assessed as part of a thorough clinical evaluation.
AmJPsychiatry2017;174:1094–1101;doi:10.1176/appi.ajp.2017.16101180
Alcohol use disorder has a lifetime prevalence of nearly one in threeindividualsintheUnitedStates(1).Animportantgoalisto identify at-risk individuals prior to the development of this disorder so that they canbetargetedfor earlyintervention.One way to determine early phenotypic differences in those at risk is to examine behavior at the level of an individual drinking session. For example, the rate of drinking and total alcohol exposure may differ between those at high and low risk. These parameters, however, are difficult to quantify in the field be- cause of the lack of instruments that can continuously and accurately monitor blood alcohol concentration. Furthermore, asking individuals to report details about their rate of con- sumption does not account for variability in absorption and metabolism (2) and would likely be inaccurate because in- toxication impairs recall (3). Despite these measurement difficulties, there is evidence that the rapid consumption of large quantities of alcohol leading to a blood alcohol con- centration of 80 mg%, defined as binge drinking (4), affects psychological and physical well-being. Binge drinking is associatedwithgreater riskof negativehealth consequences
(e.g., myocardial infarction) and legal trouble (5, 6). Binge drinking may signify an innate preference for higher brain alcohol exposure and may begin before an individual meets criteria for an alcohol use disorder, but this hypothesis has never been empirically tested.
One method to assess alcohol consumption that overcomes many of these measurement difficulties is intravenous alcohol self-administration (7). This method has shown good test- retest reliability and external validity (8, 9) and has been employed in pharmacological (10) and genetic studies (11). Intravenous alcohol self-administration has several advan- tages over oral self-administration. Whereas oral adminis- tration at fixed doses can result in up to threefold variability in alcohol exposure between individuals as a result of phar- macokinetic differences (12, 13), intravenous administration standardizes alcohol exposure by bypassing gastrointestinal absorption and first-pass metabolism. Interindividual differ- ences in alcohol distribution and elimination are accounted for by using an infusion algorithm that adjusts for age, sex, height, and weight (2). Accordingly, each infusion increases
See related features: Editorial by Dr. Petrakis (p. 1034), Clinical Guidance (Table of Contents), CME course (p. 1127), AJP Audio (online), and Video by Dr. Pine (online)
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ARTICLES
alcohol levels by a fixed quantity, allowing the infusion software to provide continuous esti- mates of blood alcohol levels that closely track brain alcohol exposure (14) and breathalyzer readouts(15).Theseestimatescanthenbeusedto measureanindividual’stotalalcoholexposure,as wellashowquicklytheindividualreachesabinge level of exposure. This paradigm also eliminates specific cues associated with oral alcoholic bev- erages, including taste, smell, andappearance.As a result, intravenous self-administration should be driven primarily by alcohol’s pharmacody- namic effects, such as dopamine release in the nucleus accumbens (16). This method is there- fore ideal to determine whether preference for higher alcohol exposure is evident prior to the development of alcohol use disorder among in- dividuals with biological risk factors.
The DSM-5 lists the following genetic and physiological risk factors for alcohol use dis- order (17): family history of alcoholism (18), male sex (1), impulsivity (19), absence of acute alcohol-related skin flush (20), pre-existing schizophrenia or bipolar disorder (21), and low levelofresponsetoalcohol(22).Althoughthesefactorsmarkedly increase the risk of developing alcohol use disorder, it remains unclear how they affect the likelihood of risky drinking patterns prior to disorder onset. In the present study, we examined the largest community sample to date of young adult social drinkers using intravenous alcohol self-administration. We investigated whetherthegeneticandphysiologicalriskfactorslistedinDSM-5 (except for skin flush and comorbid psychiatric disorders, which were exclusion criteria) were associated with the rate of binge- level exposure during an individual drinking session. We hy- pothesized that individuals at higher risk for developing an al- cohol use disorder would exhibit a preference for higher brain alcohol exposure as demonstrated by higher rates of binging throughoutthesessionandhigherlevelsoftotalalcoholexposure.
METHOD
Participant Characteristics A total of 162 social drinkers between the ages of 21 and 45 were recruited through newspaper advertisements and the National Institutes of Health (NIH) Normal Volunteer Office (for detailed demographic information, see Table 1 and Tables S1–S3 in the data supplement accompanying the online version of this article). To be included, participants must have consumed at least five drinks on one occasion at one point in their life. Participants completed a telephone screen and sub- sequently completedan in-personassessmentattheNIHClinical Center in Bethesda, Md. The study protocol was approved by the NIH Addictions Institutional Review Board, and participants were enrolled after providing written, informed consent.
Participants were excluded if they met any of the fol- lowing 10 exclusion criteria: 1) nondrinker; 2) lifetime history
of mood, anxiety, or psychotic disorder; 3) current or lifetime history of substance dependence (including alcohol and nicotine); 4) recent illicit use of psychoactive substances; 5) history of acute alcohol-related skin flush; 6) regular to- bacco use (.20 uses/week); 7) history of clinically significant alcohol withdrawal; 8) lifetime history of suicide attempts; 9) current or chronic medical conditions, including cardiovascular conditions, requiring inpatient treatment or frequent medical visits; or 10) use of medications that may interact with alcohol within 2 weeks prior to the study. Females were excluded if they were breastfeeding or pregnant or if they intended to become pregnant.
All participants were assessed for psychiatric diagnoses, history of acute alcohol-related skin flush, drinking history, andotherriskfactorsforalcoholusedisorder.Diagnoseswere assessed by the Structured Clinical Interview for DSM-IV Axis I disorders (23). History of acute alcohol-related skin flush was assessedusingtheAlcoholFlushingQuestionnaire(24).Drinking history was assessed using the Alcohol Use Disorder Identifi- cation Test (25). Two participants were excluded from this analysisbecausetheywereheavydrinkersbasedontheTimeline Followback Interview (.20 drinks/week for males, .15 drinks/ week for females). One participant was excluded because software failure caused the session to be terminated prior to minute 20 of the alcohol self-administration session, resulting in a final sample size of 159 participants.
Alcohol Use Disorder Risk Factor Measures Family history. Participants completed the Family Tree Questionnaire (26) to identify first- and second-degree rel- atives who may have had alcohol-related problems. They subsequently completed the family history assessment plus
TABLE 1. Characteristics of the Sample by Sex
Characteristic Male (N=86) Female (N=73)
Mean SD Mean SD
Age (years) 26.4 5.2 25.8 5.0 Family history densitya,b 3.6 8.5 2.6 6.9 Delay discountinga,c –4.7 1.8 –4.5 1.7 Level of alcohol responsed,e 4.8 2.1 3.7 1.7 Alcohol Use Disorder Identification Test score 5.8 2.5 5.1 2.8
N % N %
Family history positive 17 19.8 11 15.1 Current alcohol abusea 2 2.4 2 2.7
a Data were missing for some participants (family history, N=158; delay discounting, N=134; current alcohol abuse, N=158).
b Family history density was obtained by dividing the number of first- and second-degree relatives withanalcoholusedisorderbythetotalnumberoffirst-andsecond-degreerelatives;it isreported as a percentage. The value displayed represents the mean and SD for the whole sample (see Table S1 in the online data supplement for family history density in the family history positive group).
c Delaydiscountingisabehavioralmeasureofimpulsivityinwhichparticipantschoosebetweensmaller immediate or larger delayed rewards; values are reported as the natural logarithm of the discounting constant, k; lower values of ln(k) indicate lower degrees of delay discounting and less impulsivity.
d Level of alcohol response is derived from the Self-Rating of the Effects of Alcohol form, assessing response during the first five drinking occasions; the final score represents the mean of the number of drinks needed to achieve four possible intoxication-related outcomes, with a higher number indicating a lower level of response to alcohol.
e Male and female participants showed statistically different distributions for level of alcohol re- sponse using the Mann-Whitney test (Zu=3.7, p,0.01).
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individual assessment modules of the Semi-Structured As- sessment for Genetics of Alcoholism for all identified relatives (27). This assessment is widely used in family history-based studies, including large genetic studies, such as the Collabo- ration on the Genetics of Alcoholism (28). If no information was availableaboutarelative,thenthatrelativewasscoredasa 0. Relatives with a known history of alcohol-related problems werescoredasa1.Afamilyhistorydensityscorewascalculated by dividing the number of relatives with alcohol problems by the total number of first- and second-degree relatives. One participant did not complete this measure, and his value was imputed with the sample median of 0 given that family history density was not normally distributed (Shapiro-Wilk test: p,0.001). We conducted all models with and without this participant and found that his exclusion did not alter our findings, and thus we report the results with this participant included.
Behavioral impulsivity. Participants completed a delay dis- counting task (29), which is a well-validated measure of behavioral impulsivity that has a robust association with alcohol use disorder (30, 31). During this task, participants chose between smaller immediate rewards or $100 re- ceived after a delay (e.g., $90 now or $100 in 7 days). Im- mediate rewards ranged in value from $0 to $100, and delay periods ranged from 7 to 30 days. The degree of discount- ing delayed rewards, k, can be calculated using the equation developed by Mazur et al. (32). Since k values were not normally distributed, they were normalized using a loga- rithmic transformation and reported as ln(k). Lower values of ln(k) suggest less impulsivity and lower degrees of dis- counting. A portion of the sample did not complete this task (N=25), and missing values of ln(k) were imputed with the sample mean.
Level of response to alcohol. Participants also completed the Self-Rating of the Effects of Alcohol form (33). This in- strument assesses response to alcohol during the first five drinking occasions of a person’s life, their heaviest drinking period, and their most recent drinking period. For each period, it asks how many drinks it took for them to feel different, to feel dizzy, to begin stumbling, and to pass out. The final score represents the mean of the number of drinks needed to achieve each outcome, with a higher number of drinks indicating a lower level of response to alcohol. We focused on the first five drinking occasions in the present analyses to reduce the potentially confounding impact of tolerance.
Intravenous Alcohol Self-Administration Participants were instructed not to drink alcohol in the 48 hours prior to study procedures. Upon arrival, they provided a breathalyzer reading to confirm abstinence. Participants also provided a urine sample that was tested for illicit drugsand,for females, pregnancy; both hadto benegative to proceed with the study session. After the participant ate a
standardized (350 kcal) meal, an intravenous catheter was inserted into a vein in the forearm. Self-administration was conducted using the computer-assisted alcohol infusion systemsoftware,whichcontrolledtherateofinfusionof6.0% v/v alcohol in saline for each individual using a physiologi- cally based pharmacokinetic model for alcohol distribu- tion and metabolism that accounts for sex, age, height, and weight (2).
The alcohol self-administration session consisted of a 25-minuteprimingphaseanda125-minutefree-accessphase. During the first 10 minutes of the priming phase, participants were required to push a button four times at 2.5-minute intervals. Each button press resulted in an alcohol infu- sion that raised blood alcohol concentration by 7.5 mg% in 2.5 minutes, such that participants achieved a peak con- centration of approximately 30 mg% at minute 10. During the next 15 minutes, the button remained inactive while par- ticipants experienced the effects of the alcohol. At minute 25, thefree-accessphasebegan,andparticipantswereinstructed to “try to recreate a typical drinking session out with friends.” Participants could self-administer ad libitum, but they had to wait until one infusion was completed before initiating another. Blood alcohol concentration was estimated con- tinuously by the software based on infusion rate and model- estimated metabolism, and a readout was provided at 30-second intervals. Breath alcohol concentration was also obtained via breathalyzer at 15-minute intervals to confirm the software-calculated estimates; these readings were en- tered into the software to provide the model feedback, and the infusion rate was automatically adjusted accordingly (2). Software estimates of blood alcohol concentration were used to determine whether a participant reached binge- level exposure, defined as achieving an estimated blood alcohol concentration greater than 80 mg% (4). A limit was imposed such that estimated blood alcohol concentration could not exceed 100 mg% to prevent adverse events due to intoxication.
Statistical Analysis To examine whether risk factors for alcohol use disorder were predictors of rate of binging throughout the free- access phase of the intravenous alcohol self-administration session, we plotted Kaplan-Meier survival curves and conducted Cox proportional hazards models.We generated the following four Kaplan-Meier survival curves using binary variables (Figure 1): 1) male compared with female; 2) family-history positive compared with negative; 3) high compared with low impulsivity (median split); and 4) high compared with low level of response to alcohol (median split). For the Cox proportional hazards analyses, the outcome variable was time to binge (estimated blood al- cohol concentration of 80 mg%), and participants were censored when they reached a binge or ended the session early (one participant). For the initial Cox proportional hazards model, five independent variables were included: sex was coded as a binary variable (0 for females, 1 for
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VULNERABILITY FOR ALCOHOL USE DISORDER AND RATE OF CONSUMPTION
males), and delay discounting, family history density, level of response to alcohol, and age were entered as continuous variables.
To determine whether faster rate of consumption translated into greater overall exposure to alcohol, we cal- culated the area under the curve for the estimated breath alcohol concentration by time plot during the free-access phase of the session. Three individuals ended the session early due to software malfunction or adverse events (at minutes 59, 88.5, and 99.5); thus, in order to generate the area under the curve for these participants, we imputed values for
the remainder of the session by carrying their last observed alcohol concentration forward. To confirm the validity of this approach, we applied the same imputation procedure for 20 random participants starting at minute 59 and found that the imputed values correlated highly with the actual values(Spearman’srho.0.9).WeconductedMann-Whitney tests to compare area under the curve distributions for each risk factor, as area under the curve values were not normally distributed (Shapiro-Wilk test: p,0.05). For these analy- ses, we used the binary categorical risk factors described above.
FIGURE 1. Cumulative Probability of Achieving Binge-Level Exposure by Each Alcohol Use Disorder Risk Factora
Time (minutes)
1200 20 40 60 80 100
1200 20 40 60 80 100
1200 20 40 60 80 100
1200 20 40 60 80 100
P e
rc e
n t
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a c
h in
g B
in g
e
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20
0
100
80
60
40
20
0
100
80
60
40
20
0
100
80
60
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20
0
Low (N=67)
High (N=67)
Censored
Time (minutes) P
e rc
e n
t R
e a
c h
in g
B in
g e
Negative (N=130)
Positive (N=28)
Censored
Time (minutes)
P e
rc e
n t
R e
a c
h in
g B
in g
e
High (N=86)
Low (N=73)
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P e
rc e
n t
R e
a c
h in
g B
in g
e
F (N=73)
M (N=86)
Censored
Delay Discounting Family History
Level of Response to Alcohol Sex
a Cumulative probability of achieving a binge-level exposure (estimated breath alcohol concentration of 80 mg%) was higher in males compared with females, in family-history positive compared with family-history negative individuals, in high compared with low delay discounters, and in low compared with high responders to alcohol.
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To assess the additive effects of significant variables from the aforementioned analyses, we coded individuals according to their number of risk factors for alcohol use disorder. For this analysis, we only used the binary risk factors described above, excluding level of response to al- cohol, which did not contribute to the aforementioned models. We thus created four groups: zero-, one-, two-, and three-risk factor groups. The zero-risk factor group served as the reference group. We plotted Kaplan-Meier survival curves to examine differences between groups and also to fit a Cox proportional hazards model additionally adjusted for age. We also tested whether there was evidence of additive effects of risk factors on overall alcohol exposure during the session by comparing the area under the curve values for different risk groups using a Jonckheere-Terpstra test (34, 35).
RESULTS
Effect of Risk Factors on Rate of Binging Overall, 60 participants achieved a binge-level exposure, and 99 participants had estimated blood alcohol concentrations beneath 80 mg% across the entire session. A higher per- centage of bingers was found in family-history positive compared with negative individuals (57.1% and 33.1%, respectively), males compared with females (43.0% and 31.5%, respectively), high compared with low delay-discounting in- dividuals (49.3% and 29.9%, respectively), and those with a low compared with high level of response to alcohol (43.8% and 32.6%, respectively) (Figure 1).
We tested whether risk factors for alcohol use disorder predicted the rate of binging throughout the session using a Cox proportional hazards model with all four risk factors and age as independent variables (model 1). Family history density was a significant predictor (hazard ratio=1.04, 95% confidence interval [CI]=1.02–1.07, p=0.001), whereas male sex (hazard ratio=1.71, 95% CI=1.00–2.94, p=0.052) and delay discounting (hazard ratio=1.17, 95% CI=1.00–1.37, p=0.056) were marginally significant. Level of response to alcohol was not a significant predictor of the rate of binging throughout the session (hazard ratio=1.01, 95% CI=0.89–1.15, p=0.840) (Table 2). Because the level of response was not contribut- ing to the model and was significantly correlated with sex (Spearman’s rho=0.29, see Table S4 in the online data supplement), we dropped it from the model. In this second analysis (model 2), male sex (hazard ratio=1.74, 95% CI=1.03–2.93, p=0.038), delay discounting (hazard ratio=1.17, 95% CI=1.00–1.37, p=0.048), and family history density (hazard ratio=1.04, 95% CI=1.02–1.07, p=0.002) all signifi- cantly predicted binge rate throughout the session. The effects of these risk factors remained consistent when controlling for the Alcohol Use Disorder Identification Test score (model 3). As would be expected, participants with a higher Alcohol Use Disorder Identification Test score were more likely to binge (hazard ratio=1.14, 95% CI=1.04–1.24, p=0.004).
Effects of Individual Risk Factors on Total Alcohol Exposure We also tested whether each individual risk factor was as- sociated with total alcohol exposure
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