Treatment of Substance Use Disorders

Anonymous
timer Asked: Oct 29th, 2018
account_balance_wallet $20

Question description

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.


In preparation for the Assignment:

Read “The Case of Kaylin” and the materials for the Assignment (ATTACHED)

In your Assignment:

  • Assume that you are the social worker meeting with Kaylin and you recorded this case.
  • Provide the full DSM-5 diagnosis for Kaylin. 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).
  • Identify and describe the assessment(s) you would use to validate her diagnosis, clarify missing information, or track her progress.
  • Clearly describe how you would explain the diagnosis to Kaylin.
  • Explain how you would engage her in treatment
  • Identifying potential cultural considerations related to substance use.
  • Describe your initial recommendations for her treatment and explain why you would recommend MAT or ABT.
  • Identify specific resources to which you would refer her and explain for each resource why you would recommend it based on her diagnosis and other identity characteristics (e.g., age, sex, gender, sexual orientation, class, ethnicity, religion, etc.).
  • Thoroughly support each of your explanations with social work concepts, theory, and principles from the assigned learning materials and from the scholarly articles you selected.
  • Document your references and cite them throughout your post following APA guidelines.

The Case of Kaylin Ma Kaylin is 22 years old and the oldest child of two working-class parents. Her father is a heating and air conditioning technician, and her mother is an administrative assistant at a local community college. Both parents immigrated from Korea as children. Kaylin has one younger brother, aged 9, who has been diagnosed with attention deficit hyperactivity disorder (ADHD). Kaylin appeared normally dressed and is 5’4” tall. Kaylin’s childhood was otherwise unremarkable. She reported that she has always worked hard at school and generally was an “A” student through high school. She ran track and was involved in many activities, socializing with boyfriends and her wide friendship circle. She reported no particular difficulties with her parents other than fighting with them over her decision to leave the state for college. After delaying admission for a year and working, Kaylin left her home in New Hampshire at 20 to attend college in Florida. As a freshman, she lived off campus with three other roommates. She has been waitressing in Tampa since freshman year at a bar/restaurant to supplement financial aid for tuition. She had very good grades (B+ to A) in her first 2 years of college. Kaylin is now a junior. She complained of chronic anxiety and problems with concentration and attention. She still works long hours, and she recently took a course in bartending so she can serve drinks and “make more.” She had managed to maintain a B+ grade point average while studying juvenile justice up until this year. Kaylin initially began drinking with friends at the restaurant after closing during her second semester of sophomore year. She now drinks regularly on weekends with her college and “bar” friends. She reported that since her 21st birthday party she has at times been “out all night partying and drinking.” She missed enough classes this year that her grades have begun to suffer. She had to drop at least one course (and will need to retake it next year) due to nonattendance. “This is because I don’t get enough sleep,” Kaylin said, and she stated that she was simply unable to wake up in time for that course. Kaylin attended this session with the social work counselor on campus because she hadn’t been interested much in food this past semester. Her roommates insisted that she get some help, as she had gone from “slight” to “reed thin.” Kaylin stated that they are worried that she has an eating disorder. Kaylin denied any eating disorder, but she admitted that she often has no time for meals and at times has “no appetite.” She often reported mild nausea. Current weight was reported at 104 pounds. Upon further assessment, Kaylin reported that she spent much of the last 2 months of weekends drinking at her workplace as well as at college parties. She used “hair of the dog” practices—e.g., a morning Bloody Mary—to feel better this past month, as she sometimes had mild hand tremors in the morning and was strongly nauseous. She admitted to being “foggy.” During these weekend experiences, she claimed to have full memory (she denies blackouts) but reported that the hangovers make her “sound sensitive” with headaches. She said she “feels” normal by the end of the day most Mondays, but she also stated that she has trouble sleeping several nights a week without an evening beer. Her mood varies over the week, and she admitted to chronic anxiety and some tendency to get into “arguments” with her roommates when sober. She set some limits for herself, such as three cocktails per weekend evening, but she has often “not bothered” to maintain those limits for “other reasons.” She admitted occasional alcohol use in high school, but her status as a varsity athlete motivated her to limit her use. At the time of the assessment she was not involved in sports, clubs, or other steady exercise, and she stated that she “has no time” for that or for boyfriends.
Journal of Psychiatric Research 79 (2016) 108e115 Contents lists available at ScienceDirect Journal of Psychiatric Research journal homepage: www.elsevier.com/locate/psychires Insomnia brings soldiers into mental health treatment, predicts treatment engagement, and outperforms other suicide-related symptoms as a predictor of major depressive episodes Melanie A. Hom a, *, Ingrid C. Lim b, Ian H. Stanley a, Bruno Chiurliza a, Matthew C. Podlogar a, Matthew S. Michaels a, Jennifer M. Buchman-Schmitt a, Caroline Silva a, Jessica D. Ribeiro c, Thomas E. Joiner Jr. a a Department of Psychology, Florida State University, 1107 West Call Street, Tallahassee FL 32306, United States Office of the Army Surgeon General, 7700 Arlington Boulevard, Falls Church, VA 22041, United States c Department of Psychological Sciences, Vanderbilt University, 111 21st Avenue South, Nashville, TN 37240, United States b a r t i c l e i n f o a b s t r a c t Article history: Received 29 August 2015 Received in revised form 4 April 2016 Accepted 9 May 2016 Given the high rates of suicide among military personnel and the need to characterize suicide risk factors associated with mental health service use, this study aimed to identify suicide-relevant factors that predict: (1) treatment engagement and treatment adherence, and (2) suicide attempts, suicidal ideation, and major depressive episodes in a military sample. Army recruiters (N ¼ 2596) completed a battery of self-report measures upon study enrollment. Eighteen months later, information regarding suicide attempts, suicidal ideation, major depressive episodes, and mental health visits were obtained from participants’ military medical records. Suicide attempts and suicidal ideation were very rare in this sample; negative binomial regression analyses with robust estimation were used to assess correlates and predictors of mental health treatment visits and major depressive episodes. More severe insomnia and agitation were significantly associated with mental health visits at baseline and over the 18-month study period. In contrast, suicide-specific hopelessness was significantly associated with fewer mental health visits. Insomnia severity was the only significant predictor of major depressive episodes. Findings suggest that assessment of sleep problems might be useful in identifying at-risk military service members who may engage in mental health treatment. Additional research is warranted to examine the predictive validity of these suicide-related symptom measures in a more representative, higher suicide risk military sample. © 2016 Elsevier Ltd. All rights reserved. Keywords: Suicide Depression Sleep Agitation Treatment engagement 1. Introduction Suicide has become a growing problem in the U.S. military, with research indicating that service members die by suicide at higher rates than civilians (Kuehn, 2009). These elevated rates may be due, in part, to risk factors unique to military personnel, such as military-specific stress (e.g., exposure to killing, physical wounds), greater access to lethal means (e.g., firearms), and demographic composition (e.g., predominantly young males; Nock et al., 2013; Schoenbaum et al., 2014). Consequently, the development of military suicide prevention strategies has become a public health * Corresponding author. E-mail address: hom@psy.fsu.edu (M.A. Hom). http://dx.doi.org/10.1016/j.jpsychires.2016.05.008 0022-3956/© 2016 Elsevier Ltd. All rights reserved. priority, motivating a marked increase in research in this area (U.S. Department of Health and Human Services [HHS], 2012). In particular, connecting at-risk service members to care has been identified as critical to suicide prevention efforts (Kuehn, 2009; Brenner and Barnes, 2012). Although interventions to reduce suicide risk have yielded promising results among military populations (Britton et al., 2012; Knox et al., 2012; Rudd et al., 2015; Trockel et al., 2015), many service members remain reluctant to engage with mental health services, often due to stigma, negative beliefs about treatment, and concerns about career impact (Vogt, 2011; Blais et al., 2014; Britt et al., 2015). Thus, efforts must be made to understand patterns and predictors of mental health service use among military personnel, especially those at elevated suicide risk. As an initial step towards enhancing treatment engagement M.A. Hom et al. / Journal of Psychiatric Research 79 (2016) 108e115 among at-risk service members, it may be helpful to identify suicide risk factors associated with greater help-seeking behaviors. To maximize utility, symptoms used to screen for suicide risk should signal short-term, acute risk rather than long-term risk. Longerterm risk factors may be informative by revealing mechanisms by which risk is conferred, identifying at-risk sociodemographic or psychiatric groups (see Nock et al., 2008 for review), and informing public health prevention approaches (e.g., reducing access to means for suicide; Mann et al., 2005). However, in clinical settings, acute warning signs are arguably more useful in informing risk level categorization and treatment provision. Detection of acute warning signs may also be useful in gatekeeper training approaches to suicide prevention (e.g., equipping unit leaders to identify at-risk unit members). In considering the vast body of suicide risk factors, there are at least five short-term risk symptoms assessable via brief, self-report survey: (1) agitation; (2) insomnia; (3) suicide-specific hopelessness; (4) talk about suicide/reported suicidal ideation; and (5) interpersonal theory of suicide constructs (i.e., perceived burdensomeness, thwarted belongingness, and capability for suicide). Each of these factors is supported by a body of literature justifying its selection as a focus of suicide risk screening (Chu et al., 2015). Agitation has been shown to be a precursor to suicidal behaviors (Fawcett et al., 1990), correlated with near-lethal attempts (Hall and Platt, 1999), and related to higher suicidality among individuals with a higher capability for suicide (Ribeiro et al., 2015). Insomnia is also a robust predictor of future suicide risk, including among military samples (Fawcett et al., 1990; Bernert et al., 2005, 2014; Ribeiro et al., 2012), even when controlling for depression and hopelessness (Ribeiro et al., 2012). Relatedly, hopelessness appears to play an integral role both in the emergence and maintenance of suicidal thoughts (Beck, 1986; Rudd et al., 2001). Suicidal ideation itself and disclosure of ideation have also been well-established as warning signs for suicide (Rudd et al., 2006). Finally, the interpersonal theory of suicide (Joiner, 2005; Van Orden et al., 2010) proposes that three constructs interact to confer risk for suicide: capability for suicide (i.e., heightened pain tolerance, fearlessness about death), thwarted belongingness (i.e., unmet need to belong), and perceived burdensomeness (i.e., feeling like a burden on others). Capability for suicide may especially be impacted by military service (Selby et al., 2010), and there is evidence for the association between suicidal history and these constructs among service members (Bryan et al., 2010). 1.1. The present study Research identifying suicide risk factors associated with treatment engagement is critical to examine within a military sample since mental health services are more readily available and accessible in this population relative to civilians, for whom structural barriers are potent (Bruffaerts et al., 2011). Utilizing a large, diverse sample of U.S. Army recruiters, this study aimed to identify suiciderelated factors: (1) associated with treatment engagement and adherence; and (2) predicting future suicide risk in a military sample (i.e., attempts, ideation, major depressive episodes [MDEs]). Due to a dearth of research examining the relationship between these variables and treatment engagement indices, a priori hypotheses were not posited. This study examined predictors of any type of mental health care visits as well as visits excluding standard mental health screenings (i.e., Pre-Post-Deployment Health Assessments to detect deployment-related health concerns) to identify factors predicting voluntary visits. With regard to utilizing depression as an outcome measure, although most individuals with depression will not die by suicide (Bostwick and Pankratz, 2000), depression treatment is a 109 key avenue for suicide prevention since it is one of the most common psychiatric disorders among suicide decedents (Cavanagh et al., 2003) and is highly treatable (Mann et al., 2005). As a result, although MDEs are not the suicide risk factor with the greatest specificity, taking into account the potentially low rates of suicide ideation and attempts in this sampledboth of which are rare in the general populationdMDEs were included at the study’s outset as an additional outcome measure, with consideration that depression is related to but not the sole contributor to suicide risk. 2. Material and method 2.1. Participants A total of 3391 Army recruiters and recruiter candidates enrolled in the study and completed baseline self-report measures. Only those with available medical record data (N ¼ 2596) were included in analyses. There were no statistically significant demographic differences between those with missing medical record data and those included in the study. Included participants were primarily male (92.2%) and ranged from 20 to 57 years of age (M ¼ 29.8, SD ¼ 4.8; see Table 1). Regarding race/ethnicity, 66.4% identified as White/Caucasian, 14.8% as Black/African American, 13.4% as Hispanic/Latino, 2.8% as Asian, 1.4% as Native Hawaiian/Other Pacific Islander, and 1.2% as American Indian/Alaska Native. 2.2. Measures Due to study setting constraints, factor analyses of previous datasets were used to select a subset of items from each self-report measure to comprise a brief assessment battery. 2.2.1. Acquired Capability for Suicide Scale (ACSS; Van Orden et al., 2008; Ribeiro et al., 2014) An abbreviated 4-item version of the ACSS assessed perceived fearlessness about death and physical pain tolerance. Respondents rated four items (e.g., “I am not afraid to die”) on a 5-point Likert scale. Total scores on the abbreviated ACSS range from 0 to 16, with higher scores indicating greater perceived pain tolerance and Table 1 Participant demographics and characteristics (N ¼ 2596). Characteristic Sex Male Female Age (M ¼ 29.8, SD ¼ 4.8) 18e24 25e34 35e44 45e54 55e64 Race/Ethnicity American Indian or Alaska Native Asian Black or African American Hispanic or Latino Native Hawaiian or Other Pacific Islander White or Caucasian Rank Sergeant Staff Sergeant Sergeant First Class First Sergeant/Master Sergeant Command Sergeant Major/Sergeant Major Second Lieutenant Captain Valid % 92.2% 7.8% 12.6% 72.2% 14.5% 0.7% <0.1% 1.2% 2.8% 14.8% 13.4% 1.4% 66.4% 39.3% 46.7% 7.1% 1.4% <0.1% <0.1% 5.4% 110 M.A. Hom et al. / Journal of Psychiatric Research 79 (2016) 108e115 fearlessness about death. Previous research supports the ACSS as an internally consistent measure with good convergent, discriminant, and construct validity (Ribeiro et al., 2014). This study’s abbreviated ACSS demonstrated adequate internal consistency (a ¼ 0.76). 2.2.2. Brief Agitation Measure (BAM; Ribeiro et al., 2011) The BAM is a 3-item self-report questionnaire that asks participants to rate items (e.g., “I want to crawl out of my skin”) on a 7point Likert scale. BAM total scores range from 3 to 21, with higher scores indicating increased agitation. Previous research has shown that the BAM has good internal consistency and convergent validity (Ribeiro et al., 2011), and it also demonstrated good internal consistency within this study (a ¼ 0.85). 2.2.3. Depressive Symptom InventorydSuicidality Subscale (DSISS; Metalsky and Joiner, 1997) The DSI-SS is a self-report measure consisting of 4 items assessing suicidal thoughts, perceived control over these thoughts, suicide attempt plans, and suicidal urges. Participants rate each item on a 4-point Likert scale. Total scores range from 0 to 12, and higher scores are associated with increasing severity of suicidal symptoms. Research supports the DSI-SS’ construct validity and internal consistency (Joiner et al., 2002). Within this study, the DSISS demonstrated modest internal consistency (a ¼ 0.70). 2.2.4. Interpersonal Needs Questionnaire (INQ1; Van Orden et al., 2012) An adapted version of the INQ was used to measure thwarted belongingness (INQ-TB; 4 items) and perceived burdensomeness (INQ-PB; 4 items), with responses occurring on a 7-point response scale. Previous research has found good internal consistency for the belongingness (a ¼ 0.85) and burdensomeness items (a ¼ 0.89) in the 15-item version of the INQ (Van Orden et al., 2012), which was also demonstrated in this sample (a ¼ 0.90 and 0.87, respectively). 2.2.5. Insomnia Severity Index (ISI; Bastien et al., 2001) A 5-item version of the 7-item ISI assessed insomnia symptom severity. Individuals rated various sleep complaints (e.g., difficulty falling asleep) on a 0 to 4 scale. Total scores on the abbreviated ISI range from 0 to 20, with higher total scores indicating greater insomnia severity. Previous research supports the validity and internal consistency of the ISI (Bastien et al., 2001; Morin et al., 2011). This study’s abbreviated ISI demonstrated good internal consistency (a ¼ 0.87). 2.3. Procedures Participants were recruited from Army recruiter courses at the U.S. Recruiting and Retention School at Fort Jackson, South Carolina. Individuals electing to participate in the study completed selfreport measures as a subset of a larger battery of non-research assessments in an orientation survey. Then, demographics data and information regarding number of mental health visits, MDEs, and episodes of suicide ideation and attempts both (1) prior to study enrollment and (2) during the 18-months study period were obtained from participants’ military medical records. MDEs were defined using DSM-IV-TR diagnostic criteria and were assessed by military psychiatrists, along with episodes of suicide ideation and attempts. No compensation was provided for study participation. All participants provided informed consent after the nature of the procedures had been fully explained. The Institutional Review Boards (IRB) of Fort Jackson and the university leading the investigation approved all procedures. The investigation was carried out in accordance with the latest version of the Declaration of Helsinki. 2.4. Data analytic plan Due to the low rate of suicide ideation and attempts (<0.001% of participants) during the study, of our three suicide risk outcome measures, only MDE recurrences were evaluated. Due to the inclusion of over-dispersed count variables, negative binomial regression analyses with a robust estimation was used to assess the extent to which self-report measures were associated with the number of any type of mental health visits, mental health visits excluding standard screenings, and MDEs occurring (1) prior to study enrollment and (2) over 18 months, controlling for baseline mental health visits and MDEs. Consistent with similar prospective studies (e.g., Ribeiro et al., 2012), we controlled for these baseline variables to determine the unique contributions of suicide-related symptoms after accounting for the variance in our outcomes explained by their baseline values. An incidence rate ratio (IRR)2 was yielded for each predictor in each analysis. Betweenpredictor intercorrelations were in an acceptable range (VIF<5). Missing data analyses revealed that those with missing medical record data at follow-up were more likely to have had a prior MDE (t[1727] ¼ 4.0, p < 0.001) and reported greater baseline agitation (t [1415] ¼ 2.1, p ¼ 0.033) and capability for suicide (t[1279] ¼ 2.7, p ¼ 0.006). Sensitivity analyses revealed that we were powered to detect a minimum IRR of 1.013 (Power [1-b]>80%, Type 1 error [a] <0.05). Analyses were conducted using SPSS 20.0.0. 3. Results 2.2.6. Suicide Cognitions Scale (SCS; Rudd et al., 2008) An abbreviated 10-item version of the 25-item SCS assessed suicide-specific hopelessness along three subscales: unlovability, unbearability, and unsolvability (Ellis and Rufino, 2015). Individuals rate the extent to which they agree or disagree with each item using a 1 to 5 scale, with higher scores indicating greater suicide-specific hopelessness. Previous research supports the validity and reliability of the SCS as a measure of suicide-related cognitions among military personnel (Bryan et al., 2014). The independent subscales did not demonstrate adequate internal consistency (as <0.60); thus, only the SCS total score was utilized in analyses (a ¼ 0.87). 1 The adapted version of the INQ used within this study utilized items from the INQ-25 and INQ-15. It also included an item designed specifically to address perceived burdensomeness within a military population (“These days I think I am an asset to the people in my life”), which strongly loaded onto the perceived burdensomeness construct. 3.1. Descriptive statistics Table 2 presents descriptive statistics and intercorrelations for all self-report measures and outcome variables. The DSI-SS and SCS score distributions, in particular, had significant positive skews, which were expected given that suicidal ideation and cognitions are relatively rare. Since all measures assessed suicide-related symptoms, it is unsurprising that many were significantly associated with each other. Of note, the mean score for the DSI-SS was 2 IRRs are interpreted as follows: for every one-unit increase in a suicide-related symptom, the number of observations that occurred (e.g., number of MDEs or number of treatment visits) increases by a factor of the IRR. For example, if analyses examining the ISI’s ability to predict MDEs yields an IRR of 2, this can be interpreted as follows: for every one-unit increase in the ISI, participants’ number of MDEs increase by a factor of 2. M.A. Hom et al. / Journal of Psychiatric Research 79 (2016) 108e115 111 Table 2 Means, standard deviations, normality statistics, and intercorrelations of self-report measures, major depressive episodes, and mental health visits. 1. ACSS 2. BAM 3. DSI-SS 4. INQ-PB 5. INQ-TB 6. ISI 7. SCS 8. MDE (T1) 9. MDE (T2) 10. MHV (T1) 11. MHV (T2) 12. NS-MHV (T1) 13. NS-MHV (T2) M SD Range Skew Kurtosis a 1 2 3 4 5 6 7 8 9 10 11 12 13 1.00 0.04* 0.03 0.01 0.03 0.11** 0.01 0.01* 0.01* 0.01 0.03 e0.01 0.03 9.61 3.24 0e16 0.28 0.11 0.76 1.00 0.23** 0.42** 0.35** 0.41** 0.42** 0.11** 0.06** 0.11** 0.10** 0.09** 0.10** 4.37 2.56 3e21 2.58 7.97 0.85 1.00 0.33** 0.22** 0.11** 0.53** 0.06** 0.04** 0.05** 0.04* 0.04* 0.04* 0.03 0.28 0e7 13.61 239.05 0.70 1.00 0.37** 0.22** 0.56** 0.07** 0.06** 0.04* 0.06** 0.02 0.06** 4.45 1.77 4e28 6.67 60.89 0.87 1.00 0.28** 0.34** 0.10** 0.06** 0.07** 0.06** 0.02 0.05 7.22 4.50 4e28 2.04 4.93 0.90 1.00 0.21** 0.11** 0.08** 0.13** 0.11** 0.08** 0.11** 4.45 3.70 0e20 0.89 0.33 0.87 1.00 0.05** 0.06** 0.03 0.04* 0.02 0.04* 10.36 1.72 10e35 7.50 69.69 0.87 1.00 0.12** 0.45** 0.07** 0.33** 0.05** 0.18 0.60 0e13 7.33 105.38 e 1.00 0.11** 0.39** 0.07** 0.36** 0.03 0.25 0e4 9.49 99.44 e 1.00 0.11** 0.73** 0.11** 5.14 8.46 0e101 4.38 26.84 e 1.00 0.09** 0.98** 2.17 10.21 0e179 9.05 104.81 e 1.00 0.09** 2.83 7.11 0e101 5.80 47.47 e 1.00 2.10 10.06 0e179 9.23 109.57 e *p < 0.05; **p < 0.01. Note: ACSS ¼ Acquired Capability for Suicide Scale, BAM ¼ Brief Agitation Measure, DSI-SS ¼ Depressive Symptom InventoryeSuicidality Subscale, INQ-PB ¼ Interpersonal Needs QuestionnaireePerceived Burdensomeness, INQ-TB ¼ Interpersonal Needs QuestionnaireeThwarted Belongingness, ISI ¼ Insomnia Severity Index, SCS ¼ Suicide Cognitions Scale, MDE ¼ Major Depressive Episode, NS ¼ Non-Standard, MHV ¼ Mental Health Visits. particularly low (M ¼ 0.03, SD ¼ 0.28). This may represent an accurate picture of this sample since Army recruiters are considered relatively high-functioning and required to receive treatment for psychiatric problems before beginning recruiting duties; thus, they may have lower suicidal ideation than other personnel. These low scores may also be related to a reluctance to disclose suicidal thoughts (Anestis and Green, 2015), but this was not directly probed. 3.2.2. Follow-up Scores on BAM agitation (IRR ¼ 1.091; 95% CI: 1.004e1.185; p ¼ 0.040), ISI insomnia (IRR ¼ 1.080; 95% CI: 1.030e1.132; p ¼ 0.001), and SCS hopelessness (IRR ¼ 0.913; 95% CI: 0.839e0.993; p ¼ 0.033) were the only significant predictors of the number of any type of mental health visits at follow-up, controlling for mental health visits at baseline and MDEs at baseline and follow-up (see Table 3). 3.3. Mental health visits excluding standard assessment visits 3.2. Mental health visits of any type 3.2.1. Baseline Negative binomial regression analyses revealed that scores on BAM agitation (IRR ¼ 1.033; 95% CI: 1.007e1.060; p ¼ 0.013), ISI insomnia (IRR ¼ 1.028; 95% CI: 1.011e1.045; p ¼ 0.001), and SCS hopelessness (IRR ¼ 0.967; 95% CI: 0.940e0.995; p ¼ 0.020) were significantly associated with the number of visits at study enrollment, controlling for number of past MDEs (see Table 3). 3.3.1. Baseline Controlling for past MDEs, BAM agitation (IRR ¼ 1.052; 95% CI: 1.010e1.095; p ¼ 0.014), INQ-PB perceived burdensomeness (IRR ¼ 0.930; 95% CI: 0.874e0.990; p ¼ 0.023), and ISI insomnia (IRR ¼ 1.028; 95% CI: 1.004e1.053; p ¼ 0.021) were significantly associated with the number of treatment visits at study entry, excluding standard physical/mental health assessments (see Table 4). Table 3 Negative binomial regression results for any type of past mental health visits at baseline and during 18-month study period. Table 4 Negative binomial regression for any past mental health visits, excluding standard assessments, at baseline and during 18-month study period. Variable ACSS BAM DSI-SS INQ-PB INQ-TB ISI SCS MDE (T1) MDE (T2) MHV (T1) Baseline 18 Month follow-up IRR 95% CI 1.010 1.033* 1.106 0.991 0.999 1.028** 0.967* 2.466** e e 0.994, 1.007, 0.926, 0.967, 0.986, 1.011, 0.940, 2.210, e e 1.026 1.060 1.321 1.016 1.011 1.045 0.995 2.752 p IRR 95% CI 0.207 0.013 0.265 0.493 0.824 0.001 0.020 <0.001 e e 1.045 1.091* 1.020 0.996 1.004 1.080** 0.913* 1.029 4.643** 1.033** 0.992, 1.004, 0.683, 0.928, 0.968, 1.030, 0.839, 0.758, 3.390, 1.014, Variable p 1.102 1.185 1.522 1.069 1.043 1.132 0.993 1.398 6.360 1.053 0.099 0.040 0.923 0.906 0.816 0.001 0.033 0.853 <0.001 0.001 *p < 0.05; **p < 0.01. Note: ACSS ¼ Acquired Capability for Suicide Scale, BAM ¼ Brief Agitation Measure, DSI-SS ¼ Depressive Symptom InventoryeSuicidality Subscale, INQPB ¼ Interpersonal Needs QuestionnaireePerceived Burdensomeness, INQTB ¼ Interpersonal Needs QuestionnaireeThwarted Belongingness, ISI ¼ Insomnia Severity Index, SCS ¼ Suicide Cognitions Scale, MDE ¼ Major Depressive Episode, MHV ¼ Mental Health Visits. ACSS BAM DSI-SS INQ-PB INQ-TB ISI SCS MDE (T1) MDE (T2) NS MHV (T1) Baseline 18 Month follow-up IRR 95% CI 0.992 1.052* 0.974 0.930* 0.991 1.028* 1.016 2.871** e e 0.969, 1.010, 0.794, 0.874, 0.971, 1.004, 0.966, 2.456, e e 1.016 1.095 1.196 0.990 1.012 1.053 1.069 3.357 p IRR 95% CI 0.504 0.014 0.803 0.023 0.414 0.021 0.535 <0.001 e e 1.050 1.090* 1.055 0.997 1.005 1.085** 0.915* 1.068 4.499** 1.030** 0.994, 1.005, 0.684, 0.929, 0.966, 1.032, 0.839, 0.810, 3.272, 1.010, p 1.108 1.183 1.629 1.069 1.046 1.140 0.998 1.408 6.186 1.051 0.081 0.038 0.809 0.928 0.805 0.001 0.045 0.641 <0.001 0.004 *p < 0.05; **p < 0.01. Note: ACSS ¼ Acquired Capability for Suicide Scale, BAM ¼ Brief Agitation Measure, DSI-SS ¼ Depressive Symptom InventoryeSuicidality Subscale, INQPB ¼ Interpersonal Needs QuestionnaireePerceived Burdensomeness, INQTB ¼ Interpersonal Needs QuestionnaireeThwarted Belongingness, ISI¼Insomnia Severity Index, SCS ¼ Suicide Cognitions Scale, MDE ¼ Major Depressive Episode, NS ¼ Non-Standard, MHV ¼ Mental Health Visits. 112 M.A. Hom et al. / Journal of Psychiatric Research 79 (2016) 108e115 3.3.2. Follow-up Scores on BAM agitation (IRR ¼ 1.090; 95% CI: 1.005e1.183; p ¼ 0.038), ISI insomnia (IRR ¼ 1.085; 95% CI: 1.032e1.140; p ¼ 0.001), and SCS hopelessness (IRR ¼ 0.915; 95% CI: 0.839e0.998; p ¼ 0.045) significantly predicted the number of visits that were not standard assessments at follow-up, controlling for non-standard mental health visits at baseline and MDEs at baseline and follow-up (see Table 4). 3.4. Major depressive episodes 3.4.1. Baseline Only scores on ISI insomnia (IRR ¼ 1.043; 95% CI: 1.010e1.078; p ¼ 0.010) and INQ-TB thwarted belongingness (IRR ¼ 1.034; 95% CI: 1.010e1.059; p ¼ 0.005) were significantly associated with number of MDEs at study enrollment, controlling for baseline mental health visits (Table 5). In other words, for every one-unit increase in participants’ baseline ISI scores, the number of prior MDEs increased by a factor of 1.034. 3.4.2. Follow-up ISI insomnia was the only significant predictor of MDEs during the study period (IRR ¼ 1.104; 95% CI: 1.024e1.190; p ¼ 0.010), controlling for baseline MDEs and mental health visits. That is, for every one-unit increase in participants’ baseline ISI scores, the number of MDEs experienced during the study increased by a factor of 1.104. Of note, prior MDEs also significantly predicted number of MDEs over the study period (IRR ¼ 1.429; 95% CI: 1.023e1.997; p ¼ 0.037). Otherwise stated, for every prior MDE, the number of MDEs experienced by a participant during the study increased by a factor of 1.429. 4. Discussion This study identified suicide-related symptoms that bring soldiers to mental health treatment and predict treatment engagement and MDEs. Greater agitation, more severe insomnia, and lower suicide-specific hopelessness predicted the number of mental health visits at baseline and over the course of the study, above and beyond other symptoms. These three symptoms were also the only significant predictors of number of voluntary mental health visits attended across 18 months, even controlling for past visits. Finally, only insomnia was significantly associated with prior and future MDEs. These findings have implications for research and Table 5 Negative binomial regression for major depressive episodes at baseline and during 18-month study period. Variable ACSS BAM DSI-SS INQ-PB INQ-TB ISI SCS MDE (T1) MHV (T1) Baseline 18 Month follow-up IRR 95% CI 0.986 1.022 1.035 1.005 1.034** 1.043* 0.997 e 1.075** 0.954, 0.975, 0.803, 0.957, 1.010, 1.010, 0.942, e 1.061, 1.019 1.071 1.334 1.055 1.059 1.078 1.056 1.088 p IRR 95% CI 0.392 0.363 0.790 0.840 0.005 0.010 0.923 e <0.001 0.966 0.996 0.944 1.007 1.041 1.104* 1.058 1.429* 1.030** 0.891, 0.910, 0.543, 0.921, 0.974, 1.024, 0.971, 1.023, 1.008, p 1.048 1.090 1.640 1.101 1.114 1.190 1.152 1.997 1.052 0.408 0.930 0.838 0.879 0.235 0.010 0.201 0.037 0.008 *p < 0.05; **p < 0.01. Note: ACSS ¼ Acquired Capability for Suicide Scale, BAM ¼ Brief Agitation Measure, DSI-SS ¼ Depressive Symptom InventoryeSuicidality Subscale, INQPB ¼ Interpersonal Needs QuestionnaireePerceived Burdensomeness, INQTB ¼ Interpersonal Needs QuestionnaireeThwarted Belongingness, ISI ¼ Insomnia Severity Index, SCS ¼ Suicide Cognitions Scale, MDE ¼ Major Depressive Episode, MHV ¼ Mental Health Visits. practice. First, findings suggest that insomnia and agitation are important motivating factors both for connection and ongoing contact with mental health services, independent of MDE occurrences. One interpretation of this finding is that sleep problems and agitation are particularly distressing and consequently prompt individuals to seek services and continue attending treatment visits. However, since suicidal ideation and suicide-specific hopelessness are likely to be similarly distressing, this finding may instead signal that service members are more willing to report agitation and insomnia symptoms on self-report surveys, and, relatedly, are more comfortable seeking out services for these problems. As discussed previously, stigma is a barrier to care among military service members (Vogt, 2011; Blais et al., 2014; Britt et al., 2015), so it is understandable that military personnel may be less likely to seek support for suicide-related hopelessness or perceptions of being burdensome than for sleep problems. Thus, sleep disturbances may represent a useful, nonstigmatizing entry point into mental health care, emphasizing the importance of its assessment by primary care providers, with whom at-risk individuals may be more likely to interface (Luoma et al., 2002). Additionally, since sleep disturbances are relatively observable, improving recognition of symptoms by laypersons may be useful, particularly in light of evidence suggesting that family and friend encouragement facilitates mental health help-seeking (Warner et al., 2008; Hipes, 2012). At-risk service members can then be referred to insomnia treatment, and perhaps, ultimately, treatment for other psychiatric symptoms. Fortunately, behavioral treatments for insomniadwhich can be cost-effectively disseminated in group formatdhave been shown to be efficacious in treating insomnia (Edinger and Means, 2005) as well as facilitating improvements in depression and posttraumatic stress disorder (PTSD) symptoms (Manber et al., 2008, 2011; Ulmer et al., 2011). Therefore, the assessment and treatment of sleep problems may indirectly improve other mental health problems germane to military personnel. Interestingly, individuals with greater suicide-related hopelessnessdand thus, likely greater suicide riskdwere less likely to seek care than those with lower hopelessness. This aligns with past literature suggesting that those with more severe suicidal symptoms are also less likely to engage in treatment (Carlton and Deane, 2000; Deane et al., 2001). Though reasons for these findings cannot be determined from our data, prior research suggests that poor coping skills and hopelessness regarding treatment effectiveness may in part explain this relationship (see Hom et al., 2015 for review). Regardless of underlying mechanisms, this finding is concerning and emphasizes the importance of following large-scale risk screening with connection of at-risk individuals to care. Motivational interviewing (Miller and Rollnick, 2013) may be particularly helpful in enhancing motivation and self-efficacy to engage in treatment. Considering agitation as a treatment engagement predictor, there is a dearth of literature on non-pharmacological methods that therapeutically impact an agitated state. Agitation may prompt individuals to seek care, but it is also a state that may resolve with the passage of time, perhaps making it more difficult to address in a treatment setting. Due to this, while agitation may be an indicator of willingness to engage in treatment, at the present time, sleep problems may be a more helpful focus of ongoing treatment efforts (though acute agitation is clinically worrisome and pharmacological management of it, as well as ongoing assessment of its severity and co-occurrence with other suicide risk factors, should be considered). Relatedly, these findings highlight insomnia as a potentially useful proxy for gauging future MDE risk. This aligns with M.A. Hom et al. / Journal of Psychiatric Research 79 (2016) 108e115 113 longitudinal studies that have identified insomnia as a risk factor for MDEs (Ford and Kamerow, 1989; Buysse et al., 2008). Indeed, a meta-analysis of 21 studies found that insomnia conferred an approximately two-fold risk for subsequent depression (Baglioni et al., 2011). Our study extends these findings by establishing the predictive relationship between insomnia and depression in an Army recruiter sample, even after accounting for the effects for other suicide-related constructsdwhich has, to our knowledge, not previously been examined. While insomnia severity may have predicted MDEs because insomnia itself is a DSM-5 MDE symptom, our findings and past research suggest otherwisedpast research has demonstrated a temporal relationship between insomnia and depression (see Baglioni et al., 2011 for review). Additionally, MDEs were controlled for in present analyses. Thus, our findings suggest that insomnia severity conferred risk for future MDEs in this military sample independent of other depression symptoms (e.g., agitation, suicidal ideation). Given this, as previously noted, it may be useful to screen for clinically significant insomnia symptoms to identify service members who may benefit from treatment for sleep problems and/or depression symptoms, if present or emerging (Franzen and Buysse, 2008). As demonstrated within this study, insomnia complaints can be easily assessed using a brief self-report measure and behavioral insomnia treatments are brief and extremely effective (Edinger and Means, 2005). Thus, assessment and treatment of insomnia are feasibly incorporable into current military mental health treatment and other health settings. significant predictors of mental health visits. This suggests that both are meaningful signifiers of willingness to engage in care. Finally, it is possible that insomnia and agitation were only associated with past mental health visits because these symptoms were unsuccessfully treated by prior care. Despite this, that these symptoms predicted future treatment visits even when controlling for past visits suggests that these are important treatment engagement motivators. Other study limitations include its relatively long follow-up period since short-term outcomes may be more clinically useful. As such, it is recommended that outcomes are assessed within a shorter time frame (e.g., days, weeks). However, this longer followup period also served as a strength by enhancing our ability to assess treatment engagement behaviors and MDE onset, which may precede suicidal behaviors. The use of self-report measures at baseline but medical record data at follow-up may have also limited our ability to assess symptom fluctuations. Yet, this design likely reduced the inflation of correlations due to common method variance. Sole reliance on military medical record data was another limitation of this study since individuals may have sought care elsewhere. To circumvent barriers to disclosure, future studies would also benefit from inclusion of multiple modes of observation and implicit ideation measures (Nock and Banaji, 2007; Nock et al., 2010). Relatedly, assessment of factors impacting treatment engagement (e.g., attitudes towards care) could be incorporated in future studies. 4.1. Limitations and future directions 5. Conclusion There are a number of study limitations. Because our sample was of relatively low suicide risk, these findings’ direct relevance to suicide prevention is limited, and further research is warranted to test study aims in a higher suicide risk military sample. Furthermore, in terms of our findings’ clinical utility, given the relatively small effect sizes for significant effects, we caution against overinterpretation of these results and emphasize the need for replication of these findings. However, these results represent positive preliminary work and signal that important associations may exist between these constructs. To address this study’s methodological constraints, it will be useful to utilize a more clinically heterogeneous and high-risk sample, self-report and clinician-assessed symptom data at multiple follow-up time points, measures of depression symptom severity, and explicit assessment of participants’ rationale for treatment engagement. By employing these techniques, results will likely better reflect the true magnitude of effects. Replication of findings may also inform the establishment of clinical cutoffs that will allow for the targeted study of treatment engagement among those with elevated psychiatric symptoms. Such cutoffs would also aid in identifying the extent to which a service member with clinically significant insomnia is more likely to develop depression as compared to one without marked insomnia. Additionally, it would be useful to identify clinically meaningful effect sizes for various predictors of MDEs and service use. The notable portion of missing data may have also impacted findings. It is also important to consider the possibility that insomnia and agitation predicted mental health visits simply because they are depression symptoms, and more severe depression symptoms may prompt service members to seek care. Both are also symptoms of other psychiatric disorders, so some participants may have sought care for non-depression complaints. Still, other constructs assessed (e.g., suicidal ideation, hopelessness) are also depression symptoms but did not significantly predict mental health visits. Additionally, even when controlling for MDEs, insomnia and agitation remained In sum, results revealed that self-reported agitation and sleep problems were both associated with greater past engagement in mental health services and predicted greater treatment engagement over the course of 18 months, independent of other suiciderelated symptoms. Insomnia severity also outperformed these other symptoms as a predictor of MDEs. Consequently, assessment of insomnia and agitation may be promising in identifying military service members both in need of and willing to engage in treatment. We look forward to additional research that addresses the gaps of this study, including further investigation into symptoms that may best predict suicidal behaviors among a higher suicide risk, more representative military sample. Conflicts of interest The authors have no conflicts of interest to disclose. Contributors Study Concept and Design: Hom, Lim, Joiner. Acquisition, Analysis, or Interpretation of Data: Hom, Lim, Ribeiro, Joiner. Drafting of the Manuscript: Hom, Stanley, Chiurliza, Podlogar, Michaels, Buchman-Schmitt, Joiner. Critical Revision of the Manuscript: Hom, Lim, Stanley, Chiurliza, Podlogar, Michaels, Buchman-Schmitt, Silva, Ribeiro, Joiner. All authors have approved the final article. Role of funding source The funding source had no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; and the decision to submit the article for publication. 114 M.A. Hom et al. / Journal of Psychiatric Research 79 (2016) 108e115 Acknowledgment This work was supported in part by a United States Army Military Operational Medicine Research Program (MOMRP) grant (W81XWH-09-1-0737); a grant from the Military Suicide Research Consortium (MSRC), an effort supported by the Office of the Assistant Secretary of Defense for Health Affairs under Award No. (W81XWH-10-2-0181); and a training grant (T32MH18921) from the National Institute of Mental Health. Opinions, interpretations, conclusions and recommendations are those of the author and are not necessarily endorsed by the Department of Defense, Department of Veterans Affairs, MSRC, or National Institute of Mental Health. We also gratefully acknowledge the critical feedback provided by the anonymous reviewers. References Anestis, M.D., Green, B.A., 2015 Oct. The impact of varying levels of confidentiality on disclosure of suicidal thoughts in a sample of United States National Guard personnel. J. Clin. Psychol. 71 (10), 1023e1030. Baglioni, C., Battagliese, G., Feige, B., Spiegelhalder, K., Nissen, C., Voderholzer, U., et al., 2011. Insomnia as a predictor of depression: a meta-analytic evaluation of longitudinal epidemiological studies. J. Affect Disord. 135 (1e3), 10e19. res, A., Morin, C.M., 2001. Validation of Bastien, C.H., Vallieres, A., Morin, C.M., Vallie the Insomnia Severity Index as an outcome measure for insomnia research. Sleep. Med. 2 (4), 297e307. Beck, A.T., 1986. Hopelessness as a predictor of eventual suicide. Ann. N. Y. Acad. Sci. 487, 90e96. Bernert, R.A., Joiner Jr., T.E., Cukrowicz, K.C., Schmidt, N.B., Krakow, B., 2005. Suicidality and sleep disturbances. Sleep 28 (9), 1135e1141. Bernert, R.A., Turvey, C.L., Conwell, Y., Joiner, T.E., 2014. Association of poor subjective sleep quality with risk for death by suicide during a 10-year period: a longitudinal, population-based study of late life. JAMA Psychiatry 71 (10), 1129e1137. Blais, R.K., Renshaw, K.D., Jakupcak, M., 2014. Posttraumatic stress and stigma in active-duty service members relate to lower likelihood of seeking support. J. Trauma Stress 27 (1), 116e119. Bostwick, J.M., Pankratz, V.S., 2000. Affective disorders and suicide risk: a reexamination. Am. J. Psychiatry 157 (12), 1925e1932. Brenner, L.A., Barnes, S.M., 2012. Facilitating treatment engagement during highrisk transition periods: a potential suicide prevention strategy. Am. J. Public Health 102 (Suppl. S12e514). Britt, T.W., Jennings, K.S., Cheung, J.H., Pury, C.L., Zinzow, H.M., 2015. The role of different stigma perceptions in treatment seeking and dropout among active duty military personnel. Psychiatr. Rehabil. J. 38 (2), 142e149. Britton, P.C., Conner, K.R., Maisto, S.A., 2012. An open trial of motivational interviewing to address suicidal ideation with hospitalized veterans. J. Clin. Psychol. 68 (9), 961e971. Bruffaerts, R., Demyttenaere, K., Hwang, I., Chiu, W.T., Sampson, N., Kessler, R.C., et al., 2011. Treatment of suicidal people around the world. Br. J. Psychiatry 199 (1), 64e70. Bryan, C.J., Morrow, C.E., Anestis, M.D., Joiner, T.E., 2010. A preliminary test of the interpersonal-psychological theory of suicidal behavior in a military sample. Pers. Individ. Dif. 48 (3), 347e350. Bryan, C.J., Rudd, M.D., Wertenberger, E., Etienne, N., Ray-Sannerud, B.N., Morrow, C.E., et al., 2014. Improving the detection and prediction of suicidal behavior among military personnel by measuring suicidal beliefs: an evaluation of the suicide cognitions scale. J. Affect Disord. 159, 15e22. €ssler, W., 2008. Prevalence, Buysse, D.J., Angst, J., Gamma, A., Ajdacic, V., Eich, D., Ro course, and comorbidity of insomnia and depression in young adults. Sleep 31 (4), 473e480. Carlton, P.A., Deane, F.P., 2000. Impact of attitudes and suicidal ideation on adolescents’ intentions to seek professional psychological help. J. Adolesc. 23 (1), 35e45. Cavanagh, J.T., Carson, A.J., Sharpe, M., Lawrie, S.M., 2003. Psychological autopsy studies of suicide: a systematic review. Psychol. Med. 33 (3), 395e405. Chu, C., Klein, K.M., Buchman-Schmitt, J.M., Hom, M.A., Hagan, C.R., Joiner, T.E., 2015. Routinized assessment of suicide risk in clinical practice: an empirically informed update. J. Clin. Psychol. 71 (12), 1186e1200. Deane, F.P., Wilson, C.J., Ciarrochi, J., 2001. Suicidal ideation and help-negation: not just hopelessness or prior help. J. Clin. Psychol. 57 (7), 901e914. Edinger, J.D., Means, M.K., 2005. Cognitive-behavioral therapy for primary insomnia. Clin. Psychol. Rev. 25 (5), 539e558. Ellis, T.E., Rufino, K.A., 2015. A psychometric study of the suicide cognitions scale with psychiatric inpatients. Psychol. Assess. 27 (1), 82e89. Fawcett, J., Scheftner, W.A., Fogg, L., Clark, D.C., Young, M.A., Hedeker, D., et al., 1990. Time-related predictors of suicide in major affective disorder. Am. J. Psychiatry 147 (9), 1189e1194. Ford, D.E., Kamerow, D.B., 1989. Epidemiologic study of sleep disturbances and psychiatric disorders. An opportunity for prevention? JAMA 262 (11), 1479e1484. Franzen, P.L., Buysse, D.J., 2008. Sleep disturbances and depression: risk relationships for subsequent depression and therapeutic implications. Dialogues Clin. Neurosci. 10 (4), 473e481. Hall, R.C., Platt, D.E., 1999. Suicide risk assessment: a review of risk factors for suicide in 100 patients who made severe suicide attempts. Evaluation of suicide risk in a time of managed care. Psychosomatics 40 (1), 18e27. Hipes, C., 2012. The stigma of mental health treatment in the military: an experimental approach. Curr. Res. Soc. Psychol. (5), 18. Hom, M.A., Stanley, I.H., Joiner, T.E., 2015. Evaluating factors and interventions that influence help-seeking and mental health service utilization among suicidal individuals: a review of the literature. Clin. Psychol. Rev. 40, 28e39. Joiner, T.E., 2005. Why People Die by Suicide. Harvard University Press, Cambridge, MA. Joiner, T.E., Pfaff, J.J., Acres, J.G., 2002. A brief screening tool for suicidal symptoms in adolescents and young adults in general health settings: reliability and validity data from the Australian National General Practice Youth Suicide Prevention Project. Behav. Res. Ther. 40 (4), 471e481. Knox, K.L., Stanley, B., Currier, G.W., Brenner, L., Ghahramanlou-Holloway, M., Brown, G., 2012. An emergency department-based brief intervention for veterans at risk for suicide (SAFE VET). Am. J. Public Health 102, 33e37. Kuehn, B.M., 2009. Soldier suicide rates continue to rise: military, scientists work to stem the tide. JAMA 301 (11), 1111e1113. Luoma, J.B., Martin, C.E., Pearson, J.L., 2002 Jun. Contact with mental health and primary care providers before suicide: a review of the evidence. Am. J. Psychiatry 159 (6), 909e916. Manber, R., Bernert, R.A., Suh, S., Nowakowski, S., Siebern, A.T., Ong, J.C., 2011. CBT for insomnia in patients with high and low depressive symptom severity: adherence and clinical outcomes. J. Clin. Sleep. Med. 7 (6), 645e652. Manber, R., Edinger, J.D., Gress, J.L., San Pedro-Salcedo, M.G., Kuo, T.F., Kalista, T., 2008. Cognitive behavioral therapy for insomnia enhances depression outcome in patients with comorbid major depressive disorder and insomnia. Sleep 31 (4), 489e495. Mann, J.J., Apter, A., Bertolote, J., Beautrais, A., Currier, D., Haas, A., et al., 2005. Suicide prevention strategies: a systematic review. JAMA 294 (16), 2064e2074. Metalsky, G.I., Joiner, T.E.J., 1997. The hopelessness depression symptom questionnaire. Cogn. Ther. Res. 21 (3), 359e384. Miller, W.R., Rollnick, S., 2013. Motivational Interviewing: Helping People Change, third ed. Guilford Press, New York, NY. langer, L., Ivers, H., Belanger, L., Ivers, H., 2011. The Morin, C.M., Belleville, G., Be insomnia severity index: psychometric indicators to detect insomnia cases and evaluate treatment response. Sleep 34 (5), 601e608. Nock, M.K., Banaji, M.R., 2007. Assessment of self-injurious thoughts using a behavioral test. Am. J. Psychiatry 164 (5), 820e823. Nock, M.K., Borges, G., Bromet, E.J., Cha, C.B., Kessler, R.C., Lee, S., 2008 Jan. Suicide and suicidal behavior. Epidemiol. Rev. 30, 133e154. Nock, M.K., Deming, C.A., Fullerton, C.S., Gilman, S.E., Goldenberg, M., Kessler, R.C., et al., 2013. Suicide among soldiers: a review of psychosocial risk and protective factors. Psychiatry Interpers. Biol. Process 76 (2), 97e125. Nock, M.K., Park, J.M., Finn, C.T., Deliberto, T.L., Dour, H.J., Banaji, M.R., 2010. Measuring the suicidal mind: implicit cognition predicts suicidal behavior. Psychol. Sci. 21 (4), 511e517. Van Orden, K.A., Cukrowicz, K.C., Witte, T.K., Joiner, T.E., 2012. Thwarted belongingness and perceived burdensomeness: construct validity and psychometric properties of the interpersonal needs questionnaire. Psychol. Assess. 24 (1), 197e215. Van Orden, K.A., Witte, T.K., Cukrowicz, K.C., Braithwaite, S.R., Selby, E.A., Joiner Jr., T.E., 2010. The interpersonal theory of suicide. Psychol. Rev. 117 (2), 575e600. Van Orden, K.A., Witte, T.K., Gordon, K.H., Bender, T.W., Joiner, T.E., 2008. Suicidal desire and the capability for suicide: tests of the interpersonal-psychological theory of suicidal behavior among adults. J. Consult Clin. Psychol. 76 (1), 72e83. Ribeiro, J.D., Bender, T.W., Buchman, J.M., Nock, M.K., Rudd, M.D., Bryan, C.J., et al., 2015. An investigation of the interactive effects of the capability for suicide and acute agitation on suicidality in a military sample. Depress Anxiety 32 (1), 25e31. Ribeiro, J.D., Bender, T.W., Selby, E.A., Hames, J.L., Joiner, T.E., 2011. Development and validation of a brief self-report measure of agitation: the brief agitation measure. J. Pers. Assess. 93 (6), 597e604. Ribeiro, J.D., Pease, J.L., Gutierrez, P.M., Silva, C., Bernert, R.A., Rudd, M.D., et al., 2012. Sleep problems outperform depression and hopelessness as cross-sectional and longitudinal predictors of suicidal ideation and behavior in young adults in the military. J. Affect Disord. 136 (3), 743e750. Ribeiro, J.D., Witte, T.K., Van Orden, K.A., Selby, E.A., Gordon, K.H., Bender, T.W., et al., 2014. Fearlessness about death: the psychometric properties and construct validity of the revision to the acquired capability for suicide scale. Psychol. Assess. 26 (1), 115e126. Rudd, M.D., Berman, A.L., Joiner, T.E., Nock, M.K., Silverman, M.M., Mandrusiak, M., et al., 2006. Warning signs for suicide: theory, research, and clinical applications. Suicide Life Threat Behav. 36 (3), 255e262. Rudd, M.D., Bryan, C.J., Wertenberger, E.G., Peterson, A.L., Young-McCaughan, S., Mintz, J., et al., 2015. Brief cognitive-behavioral therapy effects on posttreatment suicide attempts in a military sample: results of a randomized clinical trial with 2-year follow-up. Am. J. Psychiatry 172 (5), 441e449. Rudd, M.D., Joiner, T.E., Rajab, H., 2001. Treating Suicidal Behavior: an Effective M.A. Hom et al. / Journal of Psychiatric Research 79 (2016) 108e115 Time-limited Approach. Guilford Press, New York, NY. Rudd, M.D., Schmitz, B., McClenen, R., Joiner, T., Elkins, G., 2008. Development of a measure of suicide-specific hopelessness: the suicide cognitions scale. Unpubl. Manuscr. Schoenbaum, M., Kessler, R.C., Gilman, S.E., Colpe, L.J., Heeringa, S.G., Stein, M.B., et al., 2014. Predictors of suicide and accident death in the army study to assess risk and resilience in servicemembers (Army STARRS): results from the army study to assess risk and resilience in servicemembers (Army STARRS). JAMA Psychiatry 71 (5), 493e503. Selby, E.A., Anestis, M.D., Bender, T.W., Ribeiro, J.D., Nock, M.K., Rudd, M.D., et al., 2010. Overcoming the fear of lethal injury: evaluating suicidal behavior in the military through the lens of the interpersonal-psychological theory of suicide. Clin. Psychol. Rev. 30 (3), 298e307. Trockel, M., Karlin, B.E., Taylor, C.B., Brown, G.K., Manber, R., 2015. Effects of 115 cognitive behavioral therapy for insomnia on suicidal ideation in veterans. Sleep 38 (2), 259e265. U.S. Department of Health and Human Services [HHS], 2012. Office of the Surgeon General and National Action Alliance for Suicide Prevention. National Strategy for Suicide Prevention 2012. Goals and Objectives for Action, Washington, DC. Ulmer, C.S., Edinger, J.D., Calhoun, P.S., 2011. A multi-component cognitivebehavioral intervention for sleep disturbance in veterans with PTSD: a pilot study. J. Clin. Sleep. Med. 7 (1), 57e68. Vogt, D., 2011. Mental health-related beliefs as a barrier to service use for military personnel and veterans: a review. Psychiatr. Serv. 62 (2), 135e142. Warner, C.H., Appenzeller, G.N., Mullen, K., Warner, C.M., Grieger, T., 2008. Soldier attitudes toward mental health screening and seeking care upon return from combat. Mil. Med. 173 (6), 563e569.
APA OFFICIAL ACTIONS The American Psychiatric Association Practice Guideline for the Pharmacological Treatment of Patients With Alcohol Use Disorder Victor I. Reus, M.D., Laura J. Fochtmann, M.D., M.B.I., Oscar Bukstein, M.D., M.P.H., A. Evan Eyler, M.D., M.P.H., Donald M. Hilty, M.D., Marcela Horvitz-Lennon, M.D., M.P.H., Jane Mahoney, Ph.D., R.N., PMHCNS-B.C., Jagoda Pasic, M.D., Ph.D., Michael Weaver, M.D., Cheryl D. Wills, M.D., Jack McIntyre, M.D. (Consultant), Jeremy Kidd, M.D. (Consultant), Joel Yager, M.D. (Systematic Review), Seung-Hee Hong (Systematic Review) At its July 2017 meeting, The APA Board of Trustees approved the APA Practice Guideline Writing Group’s “Practice Guideline for the Pharmacological Treatment of Patients with Alcohol Use Disorder.” The full guideline is available at APA’s Practice Guidelines website. INTRODUCTION The goal of this guideline1 is to improve the quality of care and treatment outcomes for patients with alcohol use disorder (AUD), as defined by DSM-5 (American Psychiatric Association, 2013). The guideline focuses specifically on evidence-based pharmacological treatments for AUD but also includes statements related to assessment and treatment planning that are an integral part of using pharmacotherapy to treat AUD. AUD pharmacotherapy is a topic of increasing interest given the availability of several medications approved by the U.S. Food and Drug Administration (FDA) for this disorder and the burden of AUD in the population. Worldwide, the estimated 12-month adult prevalence of AUD is 8.5%, with an estimated lifetime prevalence of 20% (Slade et al., 2016). In the United States (U.S.), AUD has estimated values for 12-month and lifetime prevalence of 13.9% and 29.1%, respectively, with approximately half of individuals with lifetime AUD having a severe disorder (Grant et al., 2015). AUD places a significant strain on both the personal and public health of the U.S. population. According to a 2006 Centers for 1 Practice Guidelines are assessments of current scientific and clinical information provided as an educational service and should not be considered as a statement of the standard of care or inclusive of all proper treatments or methods of care and are not continually updated and may not reflect the most recent evidence. They are not intended to substitute for the independent professional judgment of the treating provider. The ultimate recommendation regarding a particular assessment, clinical procedure, or treatment plan must be made by the clinician in light of the psychiatric evaluation, other clinical data, and the diagnostic and treatment options available. The guidelines are available on an “as is” basis, and APA makes no warranty, expressed or implied, regarding them. APA assumes no responsibility for any injury or damage to persons or property arising out of or related to any use of the guidelines. 86 ajp.psychiatryonline.org Disease Control and Prevention-sponsored study (Bouchery et al., 2011), AUD and its sequelae cost the U.S. $223.5 billion annually and account for significant excess mortality (Kendler et al., 2016). Despite its high prevalence and numerous negative consequences, AUD remains undertreated. Effective and evidence-based interventions are available, and treatment is associated with reductions in the risk of relapse (Dawson et al, 2006) and AUD-associated mortality (Timko et al., 2006). Nevertheless, fewer than 1 in 10 individuals in the U.S. with a 12-month diagnosis of AUD receive any treatment (Substance Abuse and Mental Health Services Administration, 2014; Grant et al., 2015). Receipt of evidence-based care is even less common. For example, one study found that of the 11 million people in the U.S. with AUD, only 674,000 received psychopharmacological treatment (Mark et al., 2009). Accordingly, this practice guideline provides evidence-based statements aimed at increasing knowledge and the appropriate use of medications for AUD. The overall goal of this guideline is to enhance the treatment of AUD for millions of affected individuals, thereby reducing the significant psychosocial and public health consequences of this important psychiatric condition. Overview of the Development Process Since the publication of the Institute of Medicine (IOM; now known as National Academy of Medicine) report, Clinical Practice Guidelines We Can Trust (Institute of Medicine, 2011), there has been an increasing focus on using clearly defined, transparent processes for rating the quality of evidence and the strength of the overall body of evidence in systematic reviews of the scientific literature. This guideline was developed using a process intended to be consistent with the recommendations of the IOM 2011 report, the Principles for the Development of Specialty Society Clinical Guidelines (Council of Medical Specialty Societies, 2012), and the requirements of the Agency for Healthcare Research and Quality (AHRQ) for inclusion of a guideline in the National Guidelines Clearinghouse. Parameters used for the guideline’s systematic review are included with the full text of the guideline. The American Psychiatric Association (APA) website features a full description of the guideline development process. Am J Psychiatry 175:1, January 2018 APA OFFICIAL ACTIONS Rating the Strength of Research Evidence and Recommendations Development of guideline statements entails weighing the potential benefits and harms of the statement and then identifying the level of confidence in that determination. This concept of balancing benefits and harms to determine guideline recommendations and strength of recommendations is a hallmark of GRADE (Grading of Recommendations Assessment, Development and Evaluation), which is used by multiple professional organizations around the world to develop practice guideline recommendations (Guyatt et al., 2013). With the GRADE approach, recommendations are rated by assessing the confidence that the benefits of the statement outweigh the harms and burdens of the statement, determining the confidence in estimates of effect as reflected by the quality of evidence, estimating patient values and preferences (including whether they are similar across the patient population), and identifying whether resource expenditures are worth the expected net benefit of following the recommendation (Andrews et al., 2013). In weighing the balance of benefits and harms for each statement in this guideline, our level of confidence is informed by available evidence, which includes evidence from clinical trials as well as expert opinion and patient values and preferences. Evidence for the benefit of a particular intervention within a specific clinical context is identified through systematic review and is then balanced against the evidence for harms. In this regard, harms are broadly defined and might include direct and indirect costs of the intervention (including opportunity costs) as well as potential for adverse events from the intervention. Many topics covered in this guideline have relied on forms of evidence such as consensus opinions of experienced clinicians or indirect findings from observational studies rather than research from randomized trials. It is well recognized that there are guideline topics and clinical circumstances for which high-quality evidence from clinical trials is not possible or is unethical to obtain (Council of Medical Specialty Societies, 2012). The GRADE working group and guidelines developed by other professional organizations have noted that a strong recommendation or “good practice statement” may be appropriate even in the absence of research evidence when sensible alternatives do not exist (Andrews et al., 2013; Brito et al, 2013; Djulbegovic et al., 2009; Hazlehurst et al., 2013). For each guideline statement, we have described the type and strength of the available evidence that was available as well as the factors, including patient preferences, that were used in determining the balance of benefits and harms. The authors of the guideline determined each final rating, as described in the section “Rating the Strength of Research Evidence and Recommendations,” and each statement is endorsed by the APA Board of Trustees. A recommendation (denoted by the numeral 1 after the guideline statement) indicates confidence that the benefits of the intervention clearly outweigh harms. A suggestion (denoted by the numeral 2 after the guideline statement) indicates greater uncertainty. Although the benefits of the statement are still viewed as outweighing the harms, the balance of benefits and Am J Psychiatry 175:1, January 2018 harms is more difficult to judge, or either the benefits or the harms may be less clear. With a suggestion, patient values and preferences may be more variable, and this can influence the clinical decision that is ultimately made. Each guideline statement also has an associated rating for the strength of supporting research evidence. Three ratings are used: high, moderate, or low (denoted by the letters A, B, and C, respectively) and reflect the level of confidence that the evidence for a guideline statement reflects a true effect based on consistency of findings across studies, directness of the effect on a specific health outcome, precision of the estimate of effect, and risk of bias in available studies (AHRQ 2014; Guyatt et al., 2006; Balshem et al., 2011). GUIDELINE STATEMENTS Assessment and Determination of Treatment Goals 1. APA recommends (1C) that the initial psychiatric evaluation of a patient with suspected alcohol use disorder include assessment of current and past use of tobacco and alcohol as well as any misuse of other substances, including prescribed or over-the-counter medications or supplements. 2. APA recommends (1C) that the initial psychiatric evaluation of a patient with suspected alcohol use disorder include a quantitative behavioral measure to detect the presence of alcohol misuse and assess its severity. 3. APA suggests (2C) that physiological biomarkers be used to identify persistently elevated levels of alcohol consumption as part of the initial evaluation of patients with alcohol use disorder or in the treatment of individuals who have an indication for ongoing monitoring of their alcohol use. 4. APA recommends (1C) that patients be assessed for cooccurring conditions (including substance use disorders, other psychiatric disorders, and other medical disorders) that may influence the selection of pharmacotherapy for alcohol use disorder. 5. APA suggests (2C) that the initial goals of treatment of alcohol use disorder (e.g. abstinence from alcohol use, reduction or moderation of alcohol use, other elements of harm reduction) be agreed on between the patient and clinician and that this agreement be documented in the medical record. 6. APA suggests (2C) that the initial goals of treatment of alcohol use disorder include discussion of the patient’s legal obligations (e.g. abstinence from alcohol use, monitoring of abstinence) and that this discussion be documented in the medical record. 7. APA suggests (2C) that the initial goals of treatment of alcohol use disorder include discussion of risks to self (e.g. physical health, occupational functioning, legal involvement) and others (e.g. impaired driving) from continued use of alcohol and that this discussion be documented in the medical record. 8. APA recommends (1C) that patients with alcohol use disorder have a documented comprehensive and personcentered treatment plan that includes evidence-based nonpharmacological and pharmacological treatments. ajp.psychiatryonline.org 87 APA OFFICIAL ACTIONS Selection of a Pharmacotherapy 9. APA recommends (1B) that naltrexone or acamprosate be offered to patients with moderate to severe alcohol use disorder who • have a goal of reducing alcohol consumption or achieving abstinence • prefer pharmacotherapy or have not responded to nonpharmacological treatments alone • have no contraindications to the use of these medications 10. APA suggests (2C) that disulfiram be offered to patients with moderate to severe alcohol use disorder who • have a goal of achieving abstinence • prefer disulfiram or are intolerant to or have not responded to naltrexone and acamprosate • are capable of understanding the risks of alcohol consumption while taking disulfiram • have no contraindications to the use of this medication 11. APA suggests (2C) that topiramate or gabapentin be offered to patients with moderate to severe alcohol use disorder who • have a goal of reducing alcohol consumption or achieving abstinence • prefer topiramate or gabapentin or are intolerant to or have not responded to naltrexone and acamprosate • have no contraindications to the use of these medications. Recommendations Against Use of Specific Medications 12. APA recommends (1B) that antidepressant medications not be used for treatment of alcohol use disorder unless there is evidence of a co-occurring disorder for which an antidepressant is an indicated treatment. 13. APA recommends (1C) that in individuals with alcohol use disorder, benzodiazepines not be used unless treating acute alcohol withdrawal or unless a co-occurring disorder exists for which a benzodiazepine is an indicated treatment. 14. APA recommends (1C) that for pregnant or breastfeeding women with alcohol use disorder, pharmacological treatments not be used unless treating acute alcohol withdrawal with benzodiazepines or unless a co-occurring disorder exists that warrants pharmacological treatment. 15. APA recommends (1C) that acamprosate not be used by patients who have severe renal impairment. 16. APA recommends (1C) that for individuals with mild to moderate renal impairment, acamprosate not be used as a first-line treatment and, if used, the dose of acamprosate be reduced compared with recommended doses in individuals with normal renal function. 17. APA recommends (1C) that naltrexone not be used by patients who have acute hepatitis or hepatic failure. 18. APA recommends (1C) that naltrexone not be used as a treatment for alcohol use disorder by individuals who use opioids or who have an anticipated need for opioids. 88 ajp.psychiatryonline.org Treatment of Alcohol Use Disorder and Co-occurring Opioid Use Disorder 19. APA recommends (1C) that in patients with alcohol use disorder and co-occurring opioid use disorder, naltrexone be prescribed to individuals who • wish to abstain from opioid use and either abstain from or reduce alcohol use and • are able to abstain from opioid use for a clinically appropriate time prior to naltrexone initiation. GUIDELINE SCOPE The Agency for Healthcare Research and Quality (AHRQ) undertook a systematic review of AUD pharmacotherapy in outpatients (Jonas et al., 2014), which serves as the foundation of the systematic review for this practice guideline. The specific medications that are discussed in the guideline include: acamprosate, naltrexone, disulfiram, gabapentin, and topiramate. The guideline does not apply to the use of these same medications for indications other than AUD. It also does not address the management of individuals who are intoxicated with alcohol, who require pharmacotherapy for the acute treatment of alcohol withdrawal, or who are experiencing other acute medical problems related to alcohol use. Evidence-based psychotherapeutic treatments for AUD, including cognitive-behavioral therapy, twelve-step facilitation, and motivational enhancement therapy (Anton et al., 2006; Martin and Rehm, 2012, Project MATCH Research Group, 1998), also play a major role in the treatment of AUD, but specific recommendations related to these modalities are outside the scope of this guideline. EVIDENCE OF BENEFITS AND HARMS OF PHARMACOTHERAPY FOR AUD Naltrexone and acamprosate have the best available research evidence as pharmacotherapy for patients with AUD. The potential benefit of each medication was viewed as far outweighing the harms of treatment or the harms of continued alcohol use, particularly when nonpharmacological approaches have not produced an effect or when patients prefer to use one of these medications as an initial treatment option. Accordingly, APA recommends (Statement 9) that that these medications be offered to patients with moderate to severe alcohol use disorder in specific clinical circumstances. Both naltrexone and acamprosate have positive effects overall although not all studies or outcomes show a statistically significant benefit from these medications. Acamprosate is associated with a small benefit on the outcomes of returning to any drinking and number of drinking days (moderate strength of research evidence). Naltrexone is associated with a small benefit on the outcomes of returning to any drinking, returning to heavy drinking, frequency of drinking days, and frequency of heavy drinking days (moderate strength of research evidence). In the AHRQ meta-analysis of head-to-head comparisons, Am J Psychiatry 175:1, January 2018 APA OFFICIAL ACTIONS neither acamprosate nor naltrexone showed superiority to the other medication in terms of return to heavy drinking (moderate strength of research evidence), return to any drinking (moderate strength of research evidence), or percentage of drinking days (low strength of research evidence). However, in the U.S. COMBINE study (but not the German PREDICT study), naltrexone was associated with better outcomes than acamprosate. For both acamprosate and naltrexone, the harms of treatment are considered minimal, particularly compared with the harms of continued alcohol use, as long as there is no contraindication to the use of the medication (e.g. pregnancy, renal impairment for acamprosate, acute hepatitis/hepatic failure for naltrexone). Harms of acamprosate are small in magnitude, with slight overall increases in diarrhea and vomiting as compared with placebo (moderate strength of research evidence). Harms of naltrexone are also small in magnitude, with slight overall increases in dizziness, nausea, and vomiting relative to placebo (moderate strength of research evidence). Alterations in hepatic function are also possible with naltrexone. For many other potential harms, including mortality, evidence was not available or was rated by the AHRQ review as insufficient. However, withdrawals from the studies due to adverse events did not differ from placebo for acamprosate (low strength of research evidence) and were only slightly greater than placebo for naltrexone although statistically significant (moderate strength of research evidence). APA suggests (Statement 10) that disulfiram be offered to patients with moderate to severe alcohol use disorder in specific clinical circumstances. Although the bulk of the research evidence for benefits and harms of disulfiram was from randomized open-label studies, the potential benefits of disulfiram were viewed as likely to outweigh the harms for most patients given the medium to large effect size for the benefit of disulfiram and particularly compared with the harms of continued alcohol use. With carefully selected patients in clinical trials, adverse events (e.g. drowsiness, increased levels of hepatic enzymes, drug-drug reactions) were somewhat greater with disulfiram. However, serious adverse events were few and comparable in numbers to serious adverse events in comparison groups consistent with the long history of safe use of disulfiram in clinical practice. Topiramate and gabapentin are also suggested as medications to be offered to patients with moderate to severe alcohol use disorder in specific clinical circumstances (Statement 11). It was noted that even small effect sizes for these medications may be clinically meaningful because of the significant morbidity associated with AUD. A moderate strength of research evidence from multiple randomized controlled trials showed moderate benefit of topiramate on drinks per drinking day, percentage of heavy drinking days, and percentage of drinking days. Despite the benefits, adverse events such as an increased likelihood of cognitive dysfunction, dizziness, taste abnormalities, and decreased Am J Psychiatry 175:1, January 2018 appetite or weight loss were also reported more often with topiramate in placebo-controlled trials in AUD. Gabapentin was associated with moderate benefit on rates of abstinence from drinking and abstinence from heavy drinking (low strength of research evidence). Gabapentin was not associated with an increased likelihood of adverse events relative to placebo (low strength of research evidence); however, in studies that examined side effects of the medication in other conditions, side effects are typically mild and have included dizziness and somnolence. Although gabapentin had a small positive effect, the harm of treatment was seen as being minimal, particularly compared with the harms of continued alcohol use, as long as there was no contraindication to the use of the medication (e.g. pregnancy). The full text of the practice guideline includes a detailed description of research evidence related to effects of medication in individuals with AUD. It also describes aspects of guideline implementation that are relevant to individual patients’ circumstances and preferences. AUTHOR AND ARTICLE INFORMATION From the APA Practice Guideline Writing Group (Victor I. Reus, M.D., Chair) Address correspondence to Jennifer Medicus (jmedicus@psych.org). APA wishes to acknowledge the contributions of APA staff (Jennifer Medicus, Seung-Hee Hong, Samantha Shugarman, Michelle Dirst, Kristin Kroeger Ptakowski). APA and the Guideline Writing Group especially thank Laura J. Fochtmann, M.D., M.B.I., Jeremy Kidd, M.D., Seung-Hee Hong, and Jennifer Medicus for their outstanding work and effort on developing this guideline. APA also thanks the APA Steering Committee on Practice Guidelines (Michael Vergare, M.D., Chair), liaisons from the APA Assembly for their input and assistance, and APA Councils and others for providing feedback during the comment period. Am J Psychiatry 2018; 175:86–90; doi: 10.1176/appi.ajp.2017.1750101 REFERENCES Agency for Healthcare Research and Quality: Methods Guide for Effectiveness and Comparative Effectiveness Reviews. AHRQ Publication No. 10(14)-EHC063-EF. Rockville, MD, Agency for Healthcare Research and Quality. Jan 2014. Available at: http:// www.effectivehealthcare.ahrq.gov/search-for-guides-reviews-andreports/?pageaction5displayproduct&productid5318. Accessed on Feb 15, 2017 American Psychiatric Association: Diagnostic and Statistical Manual of Mental Disorders, 5th ed. Arlington, VA, American Psychiatric Publishing, 2013 Andrews JC, Schünemann HJ, Oxman AD, et al: GRADE guidelines: 15. Going from evidence to recommendation-determinants of a recommendation’s direction and strength. J Clin Epidemiol 66(7):726–735, 2013 Anton RF, O’Malley SS, Ciraulo DA, et al: Combined pharmacotherapies and behavioral interventions for alcohol dependence: the COMBINE study: a randomized controlled trial. JAMA 2006; 295:2003–2017. Available at doi: https://doi.org/10.1001/jama.295.17.2003 Balshem H, Helfand M, Schünemann HJ, et al: GRADE guidelines: 3. Rating the quality of evidence. J Clin Epidemiol 64(4):401–406, 2011 Bouchery EE, Harwood HJ, Sacks JJ, et al: Economic costs of excessive alcohol consumption in the U.S., 2006. Am J Prev Med 2011; 41: 516–524. Available at doi: https://doi.org/10.1016/j.amepre.2011.06. 045 ajp.psychiatryonline.org 89 APA OFFICIAL ACTIONS Brito JP, Domecq JP, Murad MH, et al: The Endocrine Society guidelines: when the confidence cart goes before the evidence horse. J Clin Endocrinol Metab 98(8):3246–3252, 2013 Council of Medical Specialty Societies (CMSS): Principles for the Development of Specialty Society Clinical Guidelines. Chicago, IL, Council of Medical Specialty Societies, 2012 Dawson DA, Grant BF, Stinson FS, et al: Estimating the effect of helpseeking on achieving recovery from alcohol dependence. Addiction 2006; 101:824–834. Available at doi: https://doi.org/10.1111/j.1360-0443. 2006.01433.x Djulbegovic B, Trikalinos TA, Roback J, et al: Impact of quality of evidence on the strength of recommendations: an empirical study. BMC Health Serv Res 9:120, 2009 https://doi.org/10.1186/1472-6963-9-120 Grant BF, Goldstein RB, Saha TD, et al: Epidemiology of DSM-5 alcohol use disorder: results from the national epidemiologic survey on alcohol and related conditions III. JAMA Psychiatry 2015; 72:757–766. Available at doi: https://doi.org/10.1001/jamapsychiatry.2015.0584 Guyatt G, Gutterman D, Baumann MH, et al: Grading strength of recommendations and quality of evidence in clinical guidelines: report from an American College of Chest Physicians Task Force. Chest 129 (1):174–181, 2006 https://doi.org/10.1378/chest.129.1.174 Guyatt G, Eikelboom JW, Akl EA, et al: A guide to GRADE guidelines for the readers of JTH. J Thromb Haemost 11(8):1603–1608, 2013 Hazlehurst JM, Armstrong MJ, Sherlock M, et al: A comparative quality assessment of evidence-based clinical guidelines in endocrinology. Clin Endocrinol (Oxf ) 78(2):183–190, 2013 https://doi.org/10.1111/ j.1365-2265.2012.04441.x Institute of Medicine: Clinical Practice Guidelines We Can Trust. Washington, DC, National Academies Press, 2011 Jonas DE, Amick HR, Feltner C, et al: Pharmacotherapy for Adults With Alcohol-Use Disorders in Outpatient Settings [Internet]. Rockville, 90 ajp.psychiatryonline.org MD, Agency for Healthcare Research and Quality, 2014. Available from http://www.ncbi.nlm.nih.gov/books/NBK208590/ Kendler KS, Ohlsson H, Sundquist J, et al: Alcohol use disorder and mortality across the life-span: a longitudinal cohort and co-relative analysis. JAMA Psychiatry 2016; 73:575–581. Available at doi: https:// doi.org/10.1001/jamapsychiatry.2016.0360 Mark TL, Kassed CA, Vandivort-Warren R, et al: Alcohol and opioid dependence medications: prescription trends, overall and by physician specialty. Drug Alcohol Depend 2009; 99:345–349. Available at doi: https://doi.org/10.1016/j.drugalcdep.2008.07.018 Martin GW, Rehm J: The effectiveness of psychosocial modalities in the treatment of alcohol problems in adults: a review of the evidence. Can J Psychiatry 2012; 57:350–358. Available at doi: https://doi.org/10.1177/ 070674371205700604 Project MATCH Research Group: Matching alcoholism treatments to client heterogeneity: treatment main effects and matching effects on drinking during treatment. J Stud Alcohol 1998; 59:631–639. Available at doi: https://doi.org/10.15288/jsa.1998.59.631 Slade T, Chiu WT, Glantz M, et al: A cross-national examination of differences in classification of lifetime alcohol use disorder between DSM-IV and DSM-5: findings from the world mental health survey. Alcohol Clin Exp Res 2016; 40:1728–1736. Available at doi: https://doi. org/10.1111/acer.13134 Substance Abuse and Mental Health Services Administration: Results from the 2013 National Survey on Drug Use and Health: Summary of National Findings. Rockville, MD, NSDUH Series H-48, HHS Publication No (SMA) 14-4863, 2014 Timko C, Debenedetti A, Moos BS, et al: Predictors of 16-year mortality among individuals initiating help-seeking for an alcoholic use disorder. Alcohol Clin Exp Res 2006; 30:1711–1720. Available at doi: https://doi. org/10.1111/j.1530-0277.2006.00206.x Am J Psychiatry 175:1, January 2018
PERSPECTIVE published: 29 May 2017 doi: 10.3389/fpsyg.2017.00884 Barking up the Wrong Tree: Why and How We May Need to Revise Alcohol Addiction Therapy Ann-Kathrin Stock * Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany Edited by: Bernhard Hommel, Leiden University, Netherlands Reviewed by: Reinout W. Wiers, University of Amsterdam, Netherlands Thomas Edward Gladwin, Ministry of Defense, Netherlands *Correspondence: Ann-Kathrin Stock Ann-Kathrin.Stock@uniklinikumdresden.de Specialty section: This article was submitted to Cognition, a section of the journal Frontiers in Psychology Received: 28 February 2017 Accepted: 15 May 2017 Published: 29 May 2017 Citation: Stock A-K (2017) Barking up the Wrong Tree: Why and How We May Need to Revise Alcohol Addiction Therapy. Front. Psychol. 8:884. doi: 10.3389/fpsyg.2017.00884 One of the main characteristics of alcohol abuse and addiction is the loss of control over alcohol intake and the continuation of drinking in the face of negative consequences. Mounting evidence strongly suggests that an alcohol-induced imbalance between goaldirected and habitual behavior may be one of the main driving factors of this key feature of addiction and furthermore play a key role in staying abstinent. Current therapies often focus only on deficient inhibitory control (i.e., goal-directed behavior), but largely neglect the potential of the well-functioning habit formation found in patients. Yet, focusing on intact habitual/automatic mechanisms in addition to or maybe even instead of deficient cognitive control might equip us with a more effective tool to battle the current alcohol abuse and addiction epidemic, especially with respect to more severely impacted patients who likely suffer from permanent alcohol-induced brain damage. Against this background, I would like to advocate the application and scientific evaluation of habit reversal therapy (HRT) for alcohol abuse and addiction. Keywords: alcohol, addiction, AUD, control, habit reversal therapy, inhibition, therapy INTRODUCTION In many, if not most parts of the world, alcohol abuse and addiction are problems of epidemic proportions which do not only cause a wide range of health problems for the affected individuals, but also skyrocketing costs for healthcare systems as well as a great number of socioeconomic problems (WHO, 2016). Considerable efforts are made to alleviate the adverse effects of this epidemic, but most countries have not had noteworthy decreases in alcohol consumption per capita within the last 25 years1 . Also, relapse rates among alcohol use disorder (AUD) patients have remained extremely high across different currently used therapeutic approaches, with often more than 50% 2 of patients consuming again after completing therapy (Garbusow et al., 2014; Naqvi and Morgenstern, 2015). Against this background, we are in dire need of better treatment options. 1 http://www.who.int/gho/alcohol/consumption_levels/adult_recorded_percapita/en/ For example, Jason et al. (2006) reported that 65% of n = 75 alcohol addicts receiving “usual after-care” relapsed within 24 months after detoxification. Picci et al. (2014) found that 47% of n = 168 alcohol-dependent patients had already relapsed 6 months after their detoxification in a hospital. Kolla et al. (2015) reported relapse rates of 48% in patients undergoing treatment and 66% ITT in alcohol-dependent patients within 12 months after treatment (n = 119). Nalpas et al. (2003) reported that the mean time until the first relapse in n = 267 patients addicted to alcohol was between 3.37 and 5.89 months in four different treatment centers. As relapse rates are cumulative measures, those numbers rise even further when looking at longer follow-up time spans. For example, Garbusow et al. (2014) stated in their review that “on the average, 8 or 9 out of 10 alcohol-dependent patients relapse after detoxification,” but they did not provide a time span. 2 Frontiers in Psychology | www.frontiersin.org 1 May 2017 | Volume 8 | Article 884 Stock Why Revise Alcohol Addiction Therapy? Dickinson, 2009; McKim et al., 2016).3 In practical terms, this behavioral autonomy means that AUD patients tend to maintain their alcohol consumption even in the face of negative consequences, which contributes to the development and maintenance of addictive behavior in AUD (Corbit and Janak, 2016; López et al., 2016). Of note, this effect extends to other behavioral domains as well since alcohol has been shown to shift even consumption-unrelated behavior from goal-directed towards habit-based processes (e.g., Stock et al., 2016) and to generally reduce goal-directed executive control capacities including behavioral inhibition (Brion et al., 2014; Garbusow et al., 2014; Day et al., 2015; Fein and Cardenas, 2015; Trantham-Davidson and Chandler, 2015; Koob and Volkow, 2016).4 Altogether, these changes result in a dysfunctional state where behavioral control is reduced, while the automatisms it should keep in check prevail or may even become enhanced over the course of an AUD (see Figure 1 for illustration). Based on this lack of control capacities, it may seem like the most logical consequence to try to enhance executive functioning/cognitive control in AUD patients (Verdejo-Garcia, 2016), who may present with sometimes severe impairments of this cognitive domain and therefore fail to abstain from drinking (Harper, 2007; Brion et al., 2014). In line with this approach, it has been shown that cognitive control training like goal management training (GMT) may improve executive functions in individuals with substance use disorders (Alfonso et al., 2011). Furthermore, cognitive control training seems to have mildly beneficial effects on the alcohol consumption of non-addicted heavy drinkers (Berg, 1948) and individuals with hazardous drinking behavior who reported relatively strong automatic preferences for alcohol (Houben et al., 2011). Yet still, it is questionable whether more severely impaired AUD patients who already suffer from alcohol-related brain damage and/or Korsakoff ’s syndrome (KS) are able to benefit from cognitive control training. The reason for this assumption is that severe alcohol abuse and the resulting thiamine deficiency often lead to brain damage including thalamic or frontal cortical atrophy (Brun and Andersson, 2001; OscarBerman et al., 2004; Matsumoto, 2009; Oscar-Berman, 2012; Maharasingam et al., 2013; Pitel et al., 2015) as well as functional changes within fronto-striatal loops and the dopaminergic and GABAergic transmitter systems (Everitt and Robbins, 2005; Gremel and Costa, 2013; Sjoerds et al., 2013; Barker et al., 2015; Koob and Volkow, 2016; Gremel and Lovinger, 2017), all of which may which cause severe executive control One of the possible reasons for the low success rates of current AUD treatments is that even though the last decades have seen an unprecedented surge in alcohol abuse and addiction research, many clinical therapeutic approaches do not (yet) consider the latest findings. And while this is not the case for evidencebased treatments, it has recently been noted that even those are currently only modestly effective (Naqvi and Morgenstern, 2015). In order to improve the current situation, effective therapeutic interventions need to be rooted in a mechanistic, not just a correlational, understanding of the behavioral and neurobiological changes that cause harmful consumption and lead to relapse in AUD patients. Based on advances in basic cognitive neuroscience research on the effects of alcohol on the nervous system and behavior, we might now be able to rise to this challenge. In the light of accumulating evidence that alcohol seems to shift the healthy balance between goal-directed and habitual behavior towards the latter, it appears that we might have been barking up the wrong tree all along: Mainly focusing on the cognitive control deficits observed in AUDs, we have utterly neglected cognitive functions such as habits and automatisms, which have lately been proven to remain largely preserved. This is quite unfortunate as preserved cognitive functions provide promising working points to establish alternatives or additions to currently popular therapeutic approaches. As this potential opportunity might benefit millions of patients, current approaches and potential alternatives will be contrasted and discussed in the following. ALCOHOL AND CONTROL DEFICITS One of the key problems contributing to relapses in AUD patients are executive control deficits which result in the inability to control alcohol intake and lead a productive, self-serving life (Fein and Cardenas, 2015; Koob and Volkow, 2016). The term ‘executive control’ subsumes several cognitive functions that help us to adapt to new situations, solve problems, and, perhaps most importantly, counteract impulsive or automatic behavior (for a detailed review, see Diamond, 2013). Among all executive functions, inhibitory control plays a special role in alcohol abuse (Copersino, 2017). It is defined as our ability to control thoughts, emotions, attention and behavior in order to resist temptations, internal predispositions or habits and replace them with more appropriate, goal-directed behavior (Diamond, 2013). Being able to control habits is key to maintaining abstinence as habitual actions substantially contribute to addiction (McKim et al., 2016). In the early stages of substance (ab)use, the consumption of alcohol is usually motivated by the reinforcing hedonic effects of alcohol, but probably due to an interaction of pavlovian and instrumental learning, repeated self-administration gradually shifts the mechanisms driving behavior to stimulus-response (S-R) associations. This eventually leads to the formation of habits and compulsions which are no longer sensitive to outcome devaluation (Everitt and Robbins, 2005; de Wit and Dickinson, 2009; McKim et al., 2016) because alcohol consumption is no longer driven by expected outcomes, but instead triggered by alcohol-associated stimuli (de Wit and Frontiers in Psychology | www.frontiersin.org 3 The underlying mechanisms can be explains within the framework of several dual-process theories. For example, the associative cybernetic model by de Wit and Dickinson (2009) suggests a mechanistic account of how alcohol abuse may lead to behavioral autonomy based on changes in outcome-response associations and response-outcome associations. In contrast to this, the Rł model suggests a continuum of automatic versus reflective processing where response selection depends on how much evaluation precedes selection (Gladwin et al., 2011, 2017). 4 Importantly, the imbalance of habitual and goal-directed control behavior is not only present in AUD patients (Sebold et al., 2014), but can also be found in animal models (Corbit and Janak, 2016) and healthy humans during high-dose alcohol intoxication (Stock et al., 2014, 2016), which suggests that alcohol is a causal factor for this imbalance. 2 May 2017 | Volume 8 | Article 884 Stock Why Revise Alcohol Addiction Therapy? FIGURE 1 | Alcohol impairs the balance between goal-directed and habitual behavior so that habitual behavior like compulsive drinking can no longer be kept in check by goal-directed control mechanisms such as inhibition. Many conventional therapies primarily aim at improving/augmenting goal-directed cognitive control so that habitual drinking can be overcome. Unfortunately, alcohol abuse may permanently damage frontal brain areas and thus diminish control faculties so that quite a few AUD patients may never develop goal-directed behavior that can effectively keep their drinking habits in check. Against this background, I would like to espouse alternative therapeutic approaches like HRT which aim at modifying or changing habits instead of trying to inhibit them via goal-directed behavior (for details, please see Habit-Based Treatment Options). tendency towards alcohol-related stimuli, which is often done by asking patients to push a lever or joystick towards visual stimuli like pictures of soft drinks (or other non-alcohol stimuli) while pulling it away from alcohol-related stimuli (Wiers et al., 2011; Eberl et al., 2013; Boendermaker et al., 2016; Gladwin et al., 2017). In addition to this response-targeted CBM, the same research group has also investigated attentional bias modification (ABM) procedures aimed at reducing the amount of attention allocated to alcohol-related stimuli, but clinical effects of the latter still remain to be established. Response-based CBM has been shown to generalize to untrained visual stimuli (Wiers et al., 2011) and to reduce relapse rates in AUD patients without KS after 1 year (49.8% of n = 248 with CBM vs. 57.3% of n = 227 without) when used as an add-on to regular AUD therapy (Eberl et al., 2013). But even with CBM, roughly half of the treated patients still experience relapses and not all studies using CBM are able to find a clear-cut beneficial effect on relapse rates (Wiers et al., 2015; Copersino, 2017). While a lack of motivation in some of the participants may have contributed to this (Gladwin et al., 2017), it is also conceivable that the reason for this lies in the specificity of the treatment as CBM targets only one specific aspect of habitual responding out of the wide range of S-R associations and the resulting addiction behavior in AUD. This is why I would like to advocate for a well-known way of altering habits which is more comprehensive, but has so far not been applied to AUDs: the habit reversal therapy (HRT). Habit reversal therapy encompasses several stages designed to alter dysfunctional habits without heavily relying on executive control and has already been proven to be effective in other disorders characterized by unwanted automatisms/habitual behavior, such as Tourette syndrome and chronic tic disorder or trichotillimania (Snorrason et al., 2015; Whittington et al., 2016; Yang et al., 2016). deficits that do not necessarily seem to fully to recover with abstinence (Thomson, 2000; Harper, 2007; Trantham-Davidson and Chandler, 2015). And as consciously controlling habitual drinking heavily strains cognitive control capacities, this means that cognitive control training and related standard addiction treatments may only benefit patients in early stages of AUD who have not yet suffered substantial damage to the brain areas mediating this cognitive faculty (Copersino, 2017; Gladwin et al., 2017). HABIT-BASED TREATMENT OPTIONS At this point, the outlook for patients with marked control deficits may seem bleak, but instead of focusing on potentially irreversibly damaged control capacities, one could also try to find a working point by focusing on relatively preserved cognitive functions. As previously noted, habits and automatisms seem to be rather unimpaired by alcohol abuse. So far, there has been comparatively little research on this therapeutic potential, but a few studies based on a retraining of automatic approach/avoidance tendencies towards alcohol-related stimuli have provided first hints for the efficacy of such interventions (Wiers et al., 2011; Naqvi and Morgenstern, 2015). In this context, the probably best-known approach to altering unwanted AUD behavior via habits and automatisms (instead of addressing cognitive control), is cognitive bias modification (CBM) (for an overview, see Gladwin et al., 2017). Put simply, it is based on the aforementioned finding of S-R-driven alcohol consumption and the observation that untreated AUD patients show an automatic approach bias, which seems to be reduced in patients who benefit from AUD therapy (Gladwin et al., 2017). Based thereon, CBM aims to establish an automatic avoidance Frontiers in Psychology | www.frontiersin.org 3 May 2017 | Volume 8 | Article 884 Stock Why Revise Alcohol Addiction Therapy? During the initial “awareness training,” the patient is made aware of his/her habits and automatisms. In the case of AUD patients, this should probably include several aspects of their S-R association-based habitual alcohol consumption. To my mind, this should include identifying stimuli and situations triggering addictive behavior and also put a major focus on the different (chains of) responses constituting the habit. In a subsequent therapy phase (“development of a competing response”), an effective competing habit or response, which needs to be carried out every time the urge to perform the initial unwanted habit/automatism emerges, is developed (in case specific behavior-eliciting stimuli have been identified during the initial phase, this would, however, also apply to situations where such stimuli are encountered – irrespective of whether they elicit craving). In this phase, the unwanted automatic behavior becomes altered or replaced by another habit/automatism which is established as part of the therapy. This is crucial as the approach requires only little cognitive control to effectively alter behavior in the long run. Importantly, this means that any irreversible cognitive control deficits that may have resulted from former alcohol abuse are not as much of an impediment to the success of this therapeutic intervention as it would have been in many alternative therapeutic approaches. In case of less complex unwanted automatisms such as tics, patients are often trained to develop a habit of performing a counteractive motor movement and the aforementioned CBM training already does something closely related by trying to counteract automatic alcohol approach tendencies by establishing competing avoidance responses. However, it has been recognized as a problem for standardized retraining strategies such as CBM that the range of S-R associations and implicit responses in AUD is far beyond the scope of this approach/avoidance aspect and substantially increases over the course of addiction (Copersino, 2017). In case the individual responses culminating in harmful alcohol consumption are carefully analyzed and dissected during the initial “awareness training” phase, it should, however, become possible to develop specific, individually tailored counteractive habits to several of these responses (like routinely doing sport after every frustrating event or day, screwing the lid of a bottle shut instead of opening it, or pouring alcohol into the sink instead of into a glass, just to make up a few examples5 ). The establishment of competing responses is further promoted by the therapy building block “generalization of new skills” that helps to generalize the competing response to as many relevant contexts as possible/necessary (which is something has mostly been neglected in previous therapeutic approaches). As a consequence, conscious and effortful controls are required less and less over time. Lastly, the block of “building motivation” is designed to motivate the patients to keep up with therapy (again without requiring too much top-down behavioral control). This aspect of HRT is important to maintain the patients’ compliance as the development and generalization of competing responses takes time and therefore does not yield immediate effects/rewards. Also, adequate motivation as well as the development of a positive long-term perspective seem to be a crucial prerequisite to yield positive outcomes when trying to manipulate automatic processes in AUD (Gladwin et al., 2017).6 While the development of competing habits/responses would certainly require an individually tailored approach for each patient, it holds the potential of breaking chains of responses that would otherwise culminate in alcohol consumption. Based thereon, HRT (probably also in combination with CBM) might provide an exciting new therapy option for AUD patients with severe and/or permanent executive control deficits who cannot sufficiently benefit from cognitive control training. 5 6 CONCLUSION Since HRT has not yet been applied and evaluated in the context of alcohol addiction, more research is needed to establish whether it provides an effective addition to or even replacement of control-focused AUD therapy. Also, we need to put further consideration into how potent competing responses can be developed. Importantly, this perspective does in no way intend to disregard the fact that the imbalance between habitual and goal-directed behavior is by far not the only mechanism at work in AUD, or that pharmacological interventions may provide valuable support for therapeutic advances. Yet, accumulating evidence strongly suggests that the alcohol-induced imbalance between goal-directed and habitual behavior may play a key role in staying abstinent. Hence, focusing on intact habitual/automatic mechanisms in addition to or maybe even instead of deficient cognitive control might equip us with a more effective tool to battle the current alcohol abuse and addiction epidemic, especially with respect to more severely impacted patients who likely suffer from permanent alcohol-induced brain damage. Against this background, I would like to advocate the application and scientific evaluation of HRT or similar therapies for alcohol abuse and addiction. AUTHOR CONTRIBUTIONS The author confirms being the sole contributor of this work and approved it for publication. FUNDING This work was funded by a grant from the “Deutsche Forschungsgemeinschaft” (DFG) SFB940-B8 to A-KS. These examples are just meant to provide a rough mechanistic idea as the development of effective competing responses likely requires an individually tailored approach (which cannot be illustrated in detail without a case study) and extensive therapeutic experience. Frontiers in Psychology | www.frontiersin.org One of the reasons for this could be that non-conscious cognitive and motivational processes are responsible for the effects of mental contrasting, which may produce either active goal pursuit or active goal disengagement, depending on the expectation of success (Oettingen, 2012). 4 May 2017 | Volume 8 | Article 884 Stock Why Revise Alcohol Addiction Therapy? REFERENCES Kolla, B. P., Schneekloth, T., Mansukhani, M. P., Biernacka, J. M., Hall-Flavin, D., Karpyak, V., et al. (2015). The association between sleep disturbances and alcohol relapse: a 12-month observational cohort study. Am. J. Addict. 24, 362–367. doi: 10.1111/ajad.12199 Koob, G. F., and Volkow, N. D. (2016). Neurobiology of addiction: a neurocircuitry analysis. Lancet Psychiatry 3, 760–773. doi: 10.1016/S2215-0366(16)00104-8 López, M., Soto, A., and Bura, S. (2016). Alcohol seeking by rats becomes habitual after prolonged training. Psicothema 28, 421–427. doi: 10.7334/ psicothema2016.114 Maharasingam, M., Macniven, J. A. B., and Mason, O. J. (2013). Executive functioning in chronic alcoholism and Korsakoff syndrome. J. Clin. Exp. Neuropsychol. 35, 501–508. doi: 10.1080/13803395.2013.795527 Matsumoto, I. (2009). Proteomics approach in the study of the pathophysiology of alcohol-related brain damage. Alcohol Alcohol. 44, 171–176. doi: 10.1093/alcalc/ agn104 McKim, T. H., Shnitko, T. A., Robinson, D. L., and Boettiger, C. A. (2016). Translational research on habit and alcohol. Curr. Addict. Rep. 3, 37–49. doi: 10.1007/s40429-016-0089-8 Nalpas, B., Combescure, C., Pierre, B., Ledent, T., Gillet, C., Playoust, D., et al. (2003). Financial costs of alcoholism treatment programs: a longitudinal and comparative evaluation among four specialized centers. Alcohol. Clin. Exp. Res. 27, 51–56. doi: 10.1097/01.ALC.0000047301.72437.10 Naqvi, N. H., and Morgenstern, J. (2015). Cognitive neuroscience approaches to understanding behavior change in alcohol use disorder treatments. Alcohol Res. Curr. Rev. 37, 29–38. Oettingen, G. (2012). Future thought and behaviour change. Eur. Rev. Soc. Psychol. 23, 1–63. doi: 10.1080/10463283.2011.643698 Oscar-Berman, M. (2012). Function and dysfunction of prefrontal brain circuitry in alcoholic Korsakoff ’s syndrome. Neuropsychol. Rev. 22, 154–169. doi: 10.1007/s11065-012-9198-x Oscar-Berman, M., Kirkley, S. M., Gansler, D. A., and Couture, A. (2004). Comparisons of Korsakoff and non-Korsakoff alcoholics on neuropsychological tests of prefrontal brain functioning. Alcohol. Clin. Exp. Res. 28, 667–675. Picci, R. L., Oliva, F., Zuffranieri, M., Vizzuso, P., Ostacoli, L., Sodano, A. J., et al. (2014). Quality of life, alcohol detoxification and relapse: is quality of life a predictor of relapse or only a secondary outcome measure? Qual. Life Res. Int. J. Qual. Life Asp. Treat. Care Rehabil. 23, 2757–2767. doi: 10.1007/s11136-0140735-3 Pitel, A. L., Segobin, S. H., Ritz, L., Eustache, F., and Beaunieux, H. (2015). Thalamic abnormalities are a cardinal feature of alcohol-related brain dysfunction. Neurosci. Biobehav. Rev. 54, 38–45. doi: 10.1016/j.neubiorev.2014. 07.023 Sebold, M., Deserno, L., Nebe, S., Nebe, S., Schad, D. J., Garbusow, M., et al. (2014). Model-based and model-free decisions in alcohol dependence. Neuropsychobiology 70, 122–131. doi: 10.1159/000362840 Sjoerds, Z., de Wit, S., van den Brink, W., Robbins, T. W., Beekman, A. T. F., Penninx, B. W. J. H., et al. (2013). Behavioral and neuroimaging evidence for overreliance on habit learning in alcohol-dependent patients. Transl. Psychiatry 3, e337. doi: 10.1038/tp.2013.107 Snorrason, I., Berlin, G. S., and Lee, H.-J. (2015). Optimizing psychological interventions for trichotillomania (hair-pulling disorder): an update on current empirical status. Psychol. Res. Behav. Manag. 8, 105–113. doi: 10.2147/PRBM. S53977 Stock, A.-K., Riegler, L., Chmielewski, W. X., and Beste, C. (2016). Paradox effects of binge drinking on response inhibition processes depending on mental workload. Arch. Toxicol. 90, 1429–1436. doi: 10.1007/s00204-0151565-y Stock, A.-K., Schulz, T., Lenhardt, M., Blaszkewicz, M., and Beste, C. (2014). High-dose alcohol intoxication differentially modulates cognitive subprocesses involved in response inhibition. Addict. Biol. 21, 136–145. doi: 10.1111/adb. 12170 Thomson, A. D. (2000). Mechanisms of vitamin deficiency in chronic alcohol misusers and the development of the Wernicke-Korsakoff syndrome. Alcohol Alcohol. Suppl. 35, 2–7. Trantham-Davidson, H., and Chandler, L. J. (2015). Alcohol-induced alterations in dopamine modulation of prefrontal activity. Alcohol 49, 773–779. doi: 10.1016/ j.alcohol.2015.09.001 Alfonso, J. P., Caracuel, A., Delgado-Pastor, L. C., and Verdejo-García, A. (2011). Combined goal management training and mindfulness meditation improve executive functions and decision-making performance in abstinent polysubstance abusers. Drug Alcohol Depend. 117, 78–81. doi: 10.1016/j. drugalcdep.2010.12.025 Barker, J. M., Corbit, L. H., Robinson, D. L., Gremel, C. M., Gonzales, R. A., and Chandler, L. J. (2015). Corticostriatal circuitry and habitual ethanol seeking. Alcohol 49, 817–824. doi: 10.1016/j.alcohol.2015.03.003 Berg, E. A. (1948). A simple objective technique for measuring flexibility in thinking. J. Gen. Psychol. 39, 15–22. doi: 10.1080/00221309.1948.9918159 Boendermaker, W. J., Sanchez Maceiras, S., Boffo, M., and Wiers, R. W. (2016). Attentional bias modification with serious game elements: evaluating the shots game. JMIR Serious Games 4:e20. doi: 10.2196/games.6464 Brion, M., Pitel, A.-L., Beaunieux, H., and Maurage, P. (2014). Revisiting the continuum hypothesis: toward an in-depth exploration of executive functions in korsakoff syndrome. Front. Hum. Neurosci. 8:498. doi: 10.3389/fnhum.2014. 00498 Brun, A., and Andersson, J. (2001). Frontal dysfunction and frontal cortical synapse loss in alcoholism–the main cause of alcohol dementia? Dement. Geriatr. Cogn. Disord. 12, 289–294. Copersino, M. L. (2017). Cognitive mechanisms and therapeutic targets of addiction. Curr. Opin. Behav. Sci. 13, 91–98. Corbit, L. H., and Janak, P. H. (2016). Habitual alcohol seeking: neural bases and possible relations to alcohol use disorders. Alcohol. Clin. Exp. Res. 40, 1380–1389. doi: 10.1111/acer.13094 Day, A. M., Kahler, C. W., Ahern, D. C., and Clark, U. S. (2015). Executive functioning in alcohol use studies: a brief review of findings and challenges in assessment. Curr. Drug Abuse Rev. 8, 26–40. de Wit, S., and Dickinson, A. (2009). Associative theories of goal-directed behaviour: a case for animal-human translational models. Psychol. Res. 73, 463–476. doi: 10.1007/s00426-009-0230-6 Diamond, A. (2013). Executive functions. Annu. Rev. Psychol. 64, 135–168. doi: 10.1146/annurev-psych-113011-143750 Eberl, C., Wiers, R. W., Pawelczack, S., Rinck, M., Becker, E. S., and Lindenmeyer, J. (2013). Approach bias modification in alcohol dependence: do clinical effects replicate and for whom does it work best? Dev. Cogn. Neurosci. 4, 38–51. doi: 10.1016/j.dcn.2012.11.002 Everitt, B. J., and Robbins, T. W. (2005). Neural systems of reinforcement for drug addiction: from actions to habits to compulsion. Nat. Neurosci. 8, 1481–1489. doi: 10.1038/nn1579 Fein, G., and Cardenas, V. A. (2015). Neuroplasticity in human alcoholism: studies of extended abstinence with potential treatment implications. Alcohol Res. Curr. Rev. 37, 125–141. Garbusow, M., Sebold, M., Beck, A., and Heinz, A. (2014). Too difficult to stop: mechanisms facilitating relapse in alcohol dependence. Neuropsychobiology 70, 103–110. doi: 10.1159/000362838 Gladwin, T. E., Figner, B., Crone, E. A., and Wiers, R. W. (2011). Addiction, adolescence, and the integration of control and motivation. Dev. Cogn. Neurosci. 1, 364–376. doi: 10.1016/j.dcn.2011.06.008 Gladwin, T. E., Wiers, C. E., and Wiers, R. W. (2017). Interventions aimed at automatic processes in addiction: considering necessary conditions for efficacy. Curr. Opin. Behav. Sci. 13, 19–24. doi: 10.1016/j.cobeha.2016.08.001 Gremel, C. M., and Costa, R. M. (2013). Orbitofrontal and striatal circuits dynamically encode the shift between goal-directed and habitual actions. Nat. Commun. 4, 2264. doi: 10.1038/ncomms3264 Gremel, C. M., and Lovinger, D. M. (2017). Associative and sensorimotor corticobasal ganglia circuit roles in effects of abused drugs. Genes Brain Behav. 16, 71–85. doi: 10.1111/gbb.12309 Harper, C. (2007). The neurotoxicity of alcohol. Hum. Exp. Toxicol. 26, 251–257. doi: 10.1177/0960327107070499 Houben, K., Wiers, R. W., and Jansen, A. (2011). Getting a grip on drinking behavior: training working memory to reduce alcohol abuse. Psychol. Sci. 22, 968–975. doi: 10.1177/0956797611412392 Jason, L. A., Olson, B. D., Ferrari, J. R., and Lo Sasso, A. T. (2006). Communal housing settings enhance substance abuse recovery. Am. J. Public Health 96, 1727–1729. doi: 10.2105/AJPH.2005.070839 Frontiers in Psychology | www.frontiersin.org 5 May 2017 | Volume 8 | Article 884 Stock Why Revise Alcohol Addiction Therapy? drinkers over the web. Addict. Behav. 40, 21–26. doi: 10.1016/j.addbeh.2014. 08.010 Yang, C., Hao, Z., Zhu, C., Guo, Q., Mu, D., and Zhang, L. (2016). Interventions for tic disorders: an overview of systematic reviews and meta analyses. Neurosci. Biobehav. Rev. 63, 239–255. doi: 10.1016/j.neubiorev.2015.12.013 Verdejo-Garcia, A. (2016). Cognitive training for substance use disorders: neuroscientific mechanisms. Neurosci. Biobehav. Rev. 68, 270–281. doi: 10.1016/ j.neubiorev.2016.05.018 Whittington, C., Pennant, M., Kendall, T., Glazebrook, C., Trayner, P., Groom, M., et al. (2016). Practitioner review: treatments for tourette syndrome in children and young people - a systematic review. J. Child Psychol. Psychiatry 57, 988–1004. doi: 10.1111/jcpp.12556 WHO (2016). Global Status Report on Alcohol and Health 2014 WHO. Available at: http://www.who.int/substance_abuse/publications/global_alcohol_report/en/ [accessed November 13, 2016]. Wiers, R. W., Eberl, C., Rinck, M., Becker, E. S., and Lindenmeyer, J. (2011). Retraining automatic action tendencies changes alcoholic patients’ approach bias for alcohol and improves treatment outcome. Psychol. Sci. 22, 490–497. doi: 10.1177/0956797611400615 Wiers, R. W., Houben, K., Fadardi, J. S., van Beek, P., Rhemtulla, M., and Cox, W. M. (2015). Alcohol cognitive bias modification training for problem Frontiers in Psychology | www.frontiersin.org Conflict of Interest Statement: The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Copyright © 2017 Stock. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. 6 May 2017 | Volume 8 | Article 884
EDITORIALS The Importance of Identifying Characteristics Underlying the Vulnerability to Develop Alcohol Use Disorder Ismene L. Petrakis, M.D. The article by Gowin et al. (1), in this issue of the Journal, describes a study evaluating characteristics that represent risk factors for the development of alcohol use disorder. The authors carefully examined a large sample of healthy social drinkers using an innovative laboratory paradigm and found that family history of alcoholism, male sex, and impulsivity—when combined—were associated with high rates of binge drinking during the laboratory paradigm. This study documents different patterns of alcohol exposure within individual drinking sessions. The findings suggest that those with characteristics associated with a higher risk of developing alcohol use disorder experienced higher alcohol exposure within sessions. The authors conclude that young social drinkers at risk for alcohol use disorder have markedly different patterns of drinking and encourage the evaluation of binge drinking in clinical settings as a potential indicator of vulnerability to alcohol use disorder. This study is quite timely, and its publication for a wide psychiatric audience is important given the recently documented increase in the prevalence of alcohol use disorder. Over the past decade, there has been a dramatic increase in rates of alcohol use, high-risk drinking, and alcohol-related conditions (2). This despite scientific advances in the understanding of the underlying neurobiology (3), genetics (4), and treatment of alcohol use disorder (5). The high rates of consumption with the resultant medical consequences and socioeconomic cost represent a public health crisis. The finding that high-risk drinking is on the rise suggests that understanding factors that influence problematic drinking patterns and targeting high-risk groups should be a priority for health care professionals. The present study does exactly that. It extends the existing literature about risk factors associated with vulnerability for alcohol use disorder. The risks of developing alcohol use disorder include a strong family history of alcoholism (6), sex (6), and impulsivity (7). Research on understanding the mechanisms of these underlying risks has focused on altered subjective response to alcohol in the laboratory, for example, based on family history (8), impulsivity (9), and genotype (10). Nevertheless, conflicting results have been reported (11), and discrepant hypotheses on the relationship of subjective response to family history have been proposed (8). In addition, it is hard to control for other factors, and some studies have suggested that drinking history, rather than family history, is the important factor determining alcohol response and subsequent risk (12). Furthermore, subjective response may 1034 ajp.psychiatryonline.org or may not lead to differences in drinking patterns outside of laboratory settings. This study extends previous findings in two important ways. First of all, the authors have simultaneously evaluated several different previously identified factors. Results from this study suggest that the risk factors are additive, which is both intuitive and important when considering a complex disorder such as alcohol use disorder. More importantly, however, the authors have evaluated these risk factors pertaining to actual patterns of drinking or alcohol exposure within a controlled laboratory setting. These data would be hard to collect outside a laboratory setting, given the limitations of selfreport of alcohol use and the introduction of other factors that might complicate results. Given the recent epidemiologic findings, results suggesting that family history is The high rates of related to alcohol exposure consumption with the and binge drinking, rather resultant medical than subjective response, consequences and indicate that binging may socioeconomic cost be a more meaningful clinrepresent a public ical phenomenon. The methodology of health crisis. this study deserves to be highlighted. There are many paradigms used in research that provide a platform for understanding factors thought to be important to vulnerability and relapse; these include subjective effects of alcohol, alcohol consumption patterns, and related phenomenon such as craving. Paradigms using oral alcohol administration, including self-administration, while ecologically valid cannot control for factors such as variable absorption and metabolism (13). The novel paradigm used in this study is a self-administration intravenous paradigm, which controls for interindividual differences, and eliminates cues (which may be both a strength and a weakness) and is thought to reflect pure pharmacologic effects. This paradigm has been used to test both medication and genetic effects (14, 15). It has the potential to be an important tool in medication development, as a way to test novel compounds before conducting larger clinical trials. There are several limitations of this study that should be acknowledged. The elimination of factors that influence drinking are also a weakness, since expectancy, taste, and environmental cues influence drinking in a “real-world” environment. As such, Am J Psychiatry 174:11, November 2017 EDITORIALS this paradigm loses some ecological validity. As the authors point out, the cross-sectional design is a limitation, and they suggest that longitudinal work would be an important next step when evaluating whether the drinking patterns noted in the laboratory are associated with long-term risk of the development of alcohol use disorder. Despite these limitations, the undertaking of well-designed, laboratory studies identifying risk factors for the development of alcohol use disorder is sorely needed. Despite decades of work in the alcohol-related field, findings have not influenced actual behavior as measured by the prevalence of alcohol use disorder or of alcohol-related medical consequences. As the treatments developed have modest efficacy and poor uptake (16), efforts toward preventing the development of alcohol use disorder could have important benefits. The authors make the clinical point that binge drinking may be an indicator of vulnerability and should be assessed. This perhaps should be taken a step further, since evidence is mounting that there are clinical characteristics that influence risk. There is sufficient evidence to support a good clinical evaluation that includes the assessment of other factors such as family history and impulsivity as possible risk factors, which may lead to binge drinking and increase the risk. It is important for the scientific community to develop assessments and interventions that can be disseminated widely to evaluate and address alcohol use-related disorders. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. AUTHOR AND ARTICLE INFORMATION From the VA Connecticut Healthcare System, West Haven, Conn., and the Department of Psychiatry, Yale University School of Medicine, New Haven, Conn. 13. Address correspondence to Dr. Petrakis (ismene.petrakis@yale.edu). Dr. Petrakis has served as a consultant to Alkermes. Dr. Freedman has reviewed this editorial and found no evidence of influence from this relationship. 14. Accepted August 2017. Am J Psychiatry 2017; 174:1034–1035; doi: 10.1176/appi.ajp.2017.17080915 REFERENCES 1. Gowin JL, Sloan ME, Stangl BL, et al: Vulnerability for alcohol use disorder and rate of alcohol consumption. Am J Psychiatry 2017; 174:1094–1101 2. Grant BF, Chou SP, Saha TD, et al: Prevalence of 12-month alcohol use, high-risk drinking, and DSM-IV alcohol use disorder in the Am J Psychiatry 174:11, November 2017 15. 16. United States, 2001–2002 to 2012–2013: results from the National Epidemiologic Survey on Alcohol and Related Conditions. JAMA Psychiatry 2017; 74:911–923 Mason BJ: Emerging pharmacotherapies for alcohol use disorder. Neuropharmacology 2017; 122:244–253 Reilly MT, Noronha A, Goldman D, et al: Genetic studies of alcohol dependence in the context of the addiction cycle. Neuropharmacology 2017; 122:3–21 Litten RZ, Falk DE, Ryan ML, et al: Discovery, development, and adoption of medications to treat alcohol use disorder: goals for the phases of medications development. Alcohol Clin Exp Res 2016; 40: 1368–1379 Grant BF: The impact of a family history of alcoholism on the relationship between age at onset of alcohol use and DSM-IV alcohol dependence: results from the National Longitudinal Alcohol Epidemiologic Survey. Alcohol Health Res World 1998; 22:144–147 Sanchez-Roige S, Stephens DN, Duka T: Heightened impulsivity: associated with family history of alcohol misuse, and a consequence of alcohol intake. Alcohol Clin Exp Res 2016; 40:2208–2217 Morean ME, Corbin WR: Subjective response to alcohol: a critical review of the literature. Alcohol Clin Exp Res 2010; 34:385–395 Leeman RF, Ralevski E, Limoncelli D, et al: Relationships between impulsivity and subjective response in an IV ethanol paradigm. Psychopharmacology (Berl) 2014; 231:2867–2876 Boyd SJ, Schacht JP, Prisciandaro JJ, et al: Alcohol-induced stimulation mediates the effect of a GABRA2 SNP on alcohol self-administrated among alcohol-dependent individuals. Alcohol Alcohol 2016; 51: 549–554 Kerfoot K, Pittman B, Ralevski E, et al: Effects of family history of alcohol dependence on the subjective response to alcohol using the intravenous alcohol clamp. Alcohol Clin Exp Res 2013; 37:2011–2018 King AC, Hasin D, O’Connor SJ, et al: A prospective 5-year reexamination of alcohol response in heavy drinkers progressing in alcohol use disorder. Biol Psychiatry 2016; 79:489–498 Ramchandani VA, Plawecki M, Li TK, et al: Intravenous ethanol infusions can mimic the time course of breath alcohol concentrations following oral alcohol administration in healthy volunteers. Alcohol Clin Exp Res 2009; 33:938–944 Suchankova P, Yan J, Schwandt ML, et al: The glucagon-like peptide1 receptor as a potential treatment target in alcohol use disorder: evidence from human genetic association studies and a mouse model of alcohol dependence. Transl Psychiatry 2015; 5:e583 Vatsalya V, Gowin JL, Schwandt ML, et al: Effects of varenicline on neural correlates of alcohol salience in heavy drinkers. Int J Neuropsychopharmacol 2015; 18:pyv068 Harris AH, Ellerbe L, Reeder RN, et al: Pharmacotherapy for alcohol dependence: perceived treatment barriers and action strategies among Veterans Health Administration service providers. Psychol Serv 2013; 10:410–419 ajp.psychiatryonline.org 1035

Tutor Answer

masterjoe
School: Carnegie Mellon University

At...

flag Report DMCA
Review

Anonymous
Wow this is really good.... didn't expect it. Sweet!!!!

Similar Questions
Hot Questions
Related Tags

Brown University





1271 Tutors

California Institute of Technology




2131 Tutors

Carnegie Mellon University




982 Tutors

Columbia University





1256 Tutors

Dartmouth University





2113 Tutors

Emory University





2279 Tutors

Harvard University





599 Tutors

Massachusetts Institute of Technology



2319 Tutors

New York University





1645 Tutors

Notre Dam University





1911 Tutors

Oklahoma University





2122 Tutors

Pennsylvania State University





932 Tutors

Princeton University





1211 Tutors

Stanford University





983 Tutors

University of California





1282 Tutors

Oxford University





123 Tutors

Yale University





2325 Tutors