case study adolescent substance use disorder

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This chapter discusses the case study of adolescent with a substance use disorder. Confidentiality is defined as an agreement between patient and provider that information discussed during the encounter will not be shared with other parties without patient permission. A confidentiality statement must be provided to adolescents at every healthcare visit. The confidentiality statement assures adolescents that information provided to the pediatric primary care provider (P-PCP) during the office visit is a standard of care that supports full disclosure and trust between the adolescent and the P-PCP, without punitive consequences for the adolescent. P-PCPs must be knowledgeable about the laws in the state in which they practice to provide accurate information to the adolescents with admitted substance use problems. The key to intercepting these behaviors is effective office-based screenings and an immediate intervention with prompt referral to treatment and interprofessional collaborative initiatives at the national, state, and local community levels.

Original language	English (US)
Title of host publication	Behavioral Pediatric Healthcare for Nurse Practitioners
Subtitle of host publication	A Growth and Developmental Approach to Intercepting Abnormal Behaviors
Publisher
Pages	375-386
Number of pages	12
ISBN (Electronic)	9780826116819
ISBN (Print)	9780826118677
DOIs
State	Published - Jan 1 2018

ASJC Scopus subject areas

General Nursing

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10.1891/9780826116819.0028

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Substance-Related Disorders Medicine & Life Sciences 100%
Confidentiality Medicine & Life Sciences 68%
Primary Health Care Medicine & Life Sciences 29%
Pediatrics Medicine & Life Sciences 24%
Disclosure Medicine & Life Sciences 20%
Standard of Care Medicine & Life Sciences 18%
Referral and Consultation Medicine & Life Sciences 13%
Delivery of Health Care Medicine & Life Sciences 11%

T1 - Case study

T2 - Adolescent with a substance use disorder

AU - Dalina, Katelyn

AU - Katinas, Mary Elizabeth

AU - Ashmawi, Samar Mohsen

AU - Hallas, Donna

PY - 2018/1/1

Y1 - 2018/1/1

N2 - This chapter discusses the case study of adolescent with a substance use disorder. Confidentiality is defined as an agreement between patient and provider that information discussed during the encounter will not be shared with other parties without patient permission. A confidentiality statement must be provided to adolescents at every healthcare visit. The confidentiality statement assures adolescents that information provided to the pediatric primary care provider (P-PCP) during the office visit is a standard of care that supports full disclosure and trust between the adolescent and the P-PCP, without punitive consequences for the adolescent. P-PCPs must be knowledgeable about the laws in the state in which they practice to provide accurate information to the adolescents with admitted substance use problems. The key to intercepting these behaviors is effective office-based screenings and an immediate intervention with prompt referral to treatment and interprofessional collaborative initiatives at the national, state, and local community levels.

AB - This chapter discusses the case study of adolescent with a substance use disorder. Confidentiality is defined as an agreement between patient and provider that information discussed during the encounter will not be shared with other parties without patient permission. A confidentiality statement must be provided to adolescents at every healthcare visit. The confidentiality statement assures adolescents that information provided to the pediatric primary care provider (P-PCP) during the office visit is a standard of care that supports full disclosure and trust between the adolescent and the P-PCP, without punitive consequences for the adolescent. P-PCPs must be knowledgeable about the laws in the state in which they practice to provide accurate information to the adolescents with admitted substance use problems. The key to intercepting these behaviors is effective office-based screenings and an immediate intervention with prompt referral to treatment and interprofessional collaborative initiatives at the national, state, and local community levels.

UR - http://www.scopus.com/inward/record.url?scp=85061149856&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85061149856&partnerID=8YFLogxK

U2 - 10.1891/9780826116819.0028

DO - 10.1891/9780826116819.0028

M3 - Chapter

AN - SCOPUS:85061149856

SN - 9780826118677

BT - Behavioral Pediatric Healthcare for Nurse Practitioners

PB - Springer Publishing Company

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Your saved search, create a file for external citation management software, your rss feed, interventions for substance use disorders in adolescents: a systematic review [internet].

PMID: 32479039
Bookshelf ID: NBK557291

Objectives: This systematic review (SR) synthesizes the literature on behavioral, pharmacologic, and combined interventions for adolescents ages 12 to 20 years with problematic substance use or substance use disorder. We included interventions designed to achieve abstinence, reduce use quantity and frequency, improve functional outcomes, and reduce substance-related harms.

Data sources: We conducted literature searches in MEDLINE, the Cochrane CENTRAL Trials Registry, Embase, CINAHL, and PsycINFO to identify primary studies meeting eligibility criteria through November 1, 2019.

Review methods: Studies were extracted into the Systematic Review Data Repository. We categorized interventions into seven primary intervention components: motivational interviewing (MI), family focused therapy (Fam), cognitive behavioral therapy (CBT), psychoeducation, contingency management (CM), peer group therapy, and intensive case management. We conducted meta-analyses of comparative studies and evaluated the strength of evidence (SoE). The PROSPERO protocol registration number is CRD42018115388 .

Results: The literature search yielded 33,272 citations, of which 118 studies were included. Motivational interviewing reduced heavy alcohol use days by 0.7 days/month, alcohol use days by 1.2 days/month, and overall substance use problems by a standardized mean difference of 0.5, compared with treatment as usual. Brief MI did not reduce cannabis use days (net mean difference of 0). Across multiple intensive interventions, Fam was most effective, reducing alcohol use days by 3.5 days/month compared with treatment as usual. No intensive interventions reduced cannabis use days. Pharmacologic treatment of opioid use disorder led to a more than 4 times greater likelihood of abstinence with extended courses (2 to 3 months) of buprenorphine compared to short courses (14 to 28 days).

Conclusions: Brief interventions : MI reduces heavy alcohol use (low SoE), alcohol use days (moderate SoE), and substance use–related problems (low SoE) but does not reduce cannabis use days (moderate SoE). Nonbrief interventions: Fam may be most effective in reducing alcohol use (low SoE). More research is needed to identify other effective intensive behavioral interventions for alcohol use disorder. Intensive interventions did not appear to decrease cannabis use (low SoE). Some interventions (CBT, CBT+MI, and CBT+MI+CM) were associated with increased cannabis use (low SoE). Both MI and CBT reduce combined alcohol and other drug use (low SoE). Combined CBT+MI reduces illicit drug use (low SoE). Subgroup analyses of interest (male vs. female, racial and ethnic minorities, socioeconomic status, and family characteristics) were sparse, precluding conclusions regarding differential effects. Pharmacological interventions : longer courses of buprenorphine (2–3 months) are more effective than shorter courses (14–28 days) to reduce opioid use and achieve abstinence (low SoE). SRs in the college settings support use of brief interventions for students with any use, heavy or problematic use. More research is needed to identify the most effective combinations of behavioral and pharmacologic treatments for opioid, alcohol, and cannabis use disorders.

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case study adolescent substance use disorder

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Drug use severity in adolescence affects substance use disorder risk in adulthood

NIH-funded study shows screening for substance use disorder in teens may help predict adult prescription drug use and misuse and prevent overdose

People who reported multiple symptoms consistent with severe substance use disorder at age 18 exhibited two or more of these symptoms in adulthood, according to a new analysis of a nationwide survey in the United States. These individuals were also more likely, as adults, to use and misuse prescription medications, as well as self-treat with opioids, sedatives, or tranquillizers. Published today in JAMA Network Open , the study is funded by the National Institute on Drug Abuse (NIDA), part of the National Institutes of Health.

While use of alcohol, cannabis, or other drugs is common among adolescents, previous studies have suggested that most teens reduce or cease drug use as they enter adulthood. However, this study indicates that adolescents with multiple symptoms of substance use disorder – indicating higher severity – do not transition out of symptomatic substance use.

“Screening adolescents for drug use is extremely important for early intervention and prevention of the development of substance use disorder,” said Nora Volkow, M.D., director of NIDA. “This is critical especially as the transition from adolescence to adulthood, when brain development is still in progress, appears to be a period of high risk for drug use initiation.” Dr. Volkow further discusses the findings and implications of this study in a related commentary .

Researchers in this study argue that key knowledge gaps currently hinder the initiation of screening, diagnosis, prevention, and treatment efforts for teens with substance use disorders. For example, previous methods evaluating persistence of substance use disorder tended to treat substance use disorder as one broad category, without looking at severity. They also failed to account for the possibility of polysubstance use, whereby individuals may use multiple drugs or switch the types of drugs they use as they grow older.

The NIDA-funded Monitoring the Future Panel study at the University of Michigan-Ann Arbor helped close this research gap by examining substance use behaviors and related attitudes among 12th graders through their adulthood in the United States. Since 1976, the study has surveyed panels of students for their drug use behaviors across three time periods: lifetime, past year, and past month. In this study, researchers looked primarily at a subgroup of 5,317 12th graders first evaluated between 1976 and 1986, who were followed with additional surveys at two-year, then five-year intervals for up to 32 years, until they reached age 50. Among the respondents, 51% were female and 78% were white.

The research team examined the relationship between substance use disorder symptom severity at age 18 and prescription drug use, prescription drug misuse, and substance use disorder symptoms up to age 50 in these individuals.

To measure severity of substance use disorder symptoms in adolescence, researchers recorded the number of substance use disorder symptoms that participants reported in response to initial survey questions. These questions were based on criteria for alcohol, cannabis, and “other drug” use disorders in the Diagnostic and Statistical Manual of Mental Disorders (DSM). The researchers categorized substance use disorder symptoms into five levels of severity: exhibiting no symptoms, one symptom, two to three symptoms, four to five symptoms, and six or more symptoms. Symptoms included, but were not limited to, substance use resulting in a failure to fulfill major role obligations and repeating substance use even when dangerous to health.

Approximately 12% of surveyed teens indicated “severe” substance use disorder, defined by this study as reporting six or more symptoms. Among this group, more than 60% exhibited at least two symptoms of substance use disorder in adulthood – an association found across alcohol, cannabis, and other drug use disorders. By comparison, roughly 54% of teens reporting two to three symptoms – indicative of “mild” substance use disorder – had two or more substance use disorder symptoms in adulthood. Higher severity of substance use disorder symptoms at age 18 also predicted higher rates of prescription drug misuse in adulthood.

Overall, more than 40% of surveyed 18-year-old individuals reported at least two substance use disorder symptoms (across all substances). More than half of the individuals who were prescribed and used opioids, sedatives, or tranquilizers as adults also reported two or more symptoms at age 18. This finding underlines the importance of strategies to increase safety and properly assess a potential history of substance use disorder symptoms when prescribing controlled medications to adults.

“Teens with substance use disorder will not necessarily mature out of their disorders, and it may be harmful to tell those with severe symptoms that they will,” said Dr. Sean Esteban McCabe, senior author of this study and director of the Center for the Study of Drugs, Alcohol, Smoking and Health at University of Michigan. “Our study shows us that severity matters when it comes to predicting risk decades later, and it’s crucial to educate and ensure that our messaging to teens with the most severe forms of substance use disorder is one that’s realistic. We want to minimize shame and sense of failure for these individuals.”

The authors note that more research is needed to uncover potential neurological mechanisms and other factors behind why adolescents with severe substance use disorder symptoms are at increased risk of drug addiction and misuse in adulthood. Characterizing possible causes of more severe substance use disorder could help improve understanding of vulnerability to chronic substance use and help make prevention and treatment strategies more effective.

References :

SE McCabe, JE Schulenberg, TS Schepis, VV McCabe, PT Veliz. Longitudinal analysis of substance use disorder symptom severity at age 18 and substance use disorder in adulthood . JAMA Network Open. DOI: 10.1001/jamanetworkopen.2022.5324 (2022)
ND Volkow, EM Wargo. Association of Severity of Adolescent Substance Use Disorders and Long-term Outcomes . JAMA Network Open . DOI: 10.1001/jamanetworkopen.2022.5656 (2022)

NIDA Press Office 301-443-6245 [email protected]

About the National Institute on Drug Abuse (NIDA): NIDA is a component of the National Institutes of Health, U.S. Department of Health and Human Services. NIDA supports most of the world’s research on the health aspects of drug use and addiction. The Institute carries out a large variety of programs to inform policy, improve practice, and advance addiction science. For more information about NIDA and its programs, visit www.nida.nih.gov .

About the National Institutes of Health (NIH): NIH, the nation’s medical research agency, includes 27 Institutes and Centers and is a component of the U.S. Department of Health and Human Services. NIH is the primary federal agency conducting and supporting basic, clinical, and translational medical research, and is investigating the causes, treatments, and cures for both common and rare diseases. For more information about NIH and its programs, visit www.nih.gov .

NIH…Turning Discovery Into Health ®

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Jo-Hanna Ivers 1* and Kevin Ducray 2

In October 2012, 83 front-line Irish service providers working in the addiction treatment field received accreditation as trained practitioners in the delivery of a number of evidence-based positive reinforcement approaches that address substance use: 52 received accreditation in the Community Reinforcement Approach (CRA), 19 in the Adolescent Community Reinforcement Approach (ACRA) and 12 in Community Reinforcement and Family Training (CRAFT). This case study presents the treatment of a 17-year-old white male engaging in high-risk substance use. He presented for treatment as part of a court order. Treatment of the substance use involved 20 treatment sessions and was conducted per Adolescent Community Reinforcement Approach (A-CRA). This was a pilot of A-CRA a promising treatment approach adapted from the United States that had never been tried in an Irish context. A post-treatment assessment at 12-week follow-up revealed significant improvements. At both assessment and following treatment, clinician severity ratings on the Maudsley Addiction Profile (MAP) and the Alcohol Smoking and Substance Involvement Screening Test (ASSIST) found decreased score for substance use was the most clinically relevant and suggests that he had made significant changes. Also his MAP scores for parental conflict and drug dealing suggest that he had made significant changes in the relevant domains of personal and social functioning as well as in diminished engagement in criminal behaviour. Results from this case study were quite promising and suggested that A-CRA was culturally sensitive and applicable in an Irish context.

1. Theoretical and Research Basis for Treatment

Substance use disorders (SUDs) are distinct conditions characterized by recurrent maladaptive use of psychoactive substances associated with significant distress. These disorders are highly common with lifetime rates of substance use or dependence estimated at over 30% for alcohol and over 10% for other substances [1 , 2] . Changing substance use patterns and evolving psychosocial and pharmacologic treatments modalities have necessitated the need to substantiate both the efficacy and cost effectiveness of these interventions.

Evidence for the clinical application of cognitive behavioural therapy (CBT) for substance use disorders has grown significantly [3 - 8] . Moreover, CBT for substance use disorders has demonstrated efficacy both as a monotherapy and as part of combination treatment [7] . CBT is a time-limited, problem-focused, intervention that seeks to reduce emotional distress through the modification of maladaptive beliefs, assumptions, attitudes, and behaviours [9] . The underlying assumption of CBT is that learning processes play an imperative function in the development and maintenance of substance misuse. These same learning processes can be used to help patients modify and reduce their drug use [3] .

Drug misuse is viewed by CBT practitioners as learned behaviours acquired through experience [10] . If an individual uses alcohol or a substance to elicit (positively or negatively reinforced) desired states (e.g. euphorigenic, soothing, calming, tension reducing) on a recurrent basis, it may become the preferred way of achieving those effects, particularly in the absence of alternative ways of attaining those desired results. A primary task of treatment for problem substance users is to (1) identify the specific needs that alcohol and substances are being used to meet and (2) develop and reinforce skills that provide alternative ways of meeting those needs [10 , 11] .

CRA is a broad-spectrum cognitive behavioural programme for treating substance use and related problems by identifying the specific needs that alcohol and or other substances are satisfying or meeting. The goal is then to develop and reinforce skills that provide alternative ways of meeting those needs. Consistent with traditional CBT, CRA through exploration, allows the patient to identify negative thoughts, behaviours and beliefs that maintain addiction. By getting the patient to identify, positive non-drug using behaviours, interests, and activities, CRA attempts to provide alternatives to drug use. As therapy progresses the objective is to prevent relapse, increase wellness, and develop skills to promote and sustain well-being. The ultimate aim of CRA, as with CBT is to assist the patient to master a specific set of skills necessary to achieve their goals. Treatment is not complete until those skills are mastered and a reasonable degree of progress has been made toward attaining identified therapy goals. CRA sessions are highly collaborative, requiring the patient to engage in ‘between session tasks’ or homework designed reinforce learning, improve coping skills and enhance self efficacy in relevant domains.

The use of the Community Reinforcement Approach is empirically supported with inpatients [12 , 13] , outpatients [14 - 16] and homeless populations (Smith et al., 1998). In addition, three recent metaanalytic reviews cited CRA as one of the most cost-effective treatment programmes currently available [17 , 18] .

A-CRA is a evidenced based behavioural intervention that is an adapted version of the adult CRA programme [19] . Garner et al [19] modified several of the CRA procedures and accompanying treatment resources to make them more developmentally appropriate for adolescents. The main distinguishing aspect of A-CRA is that it involves caregivers—namely parents or guardians who are ultimately responsible for the adolescent and with whom the adolescent is living.

A-CRA has been tested and found effective in the context of outpatient continuing care following residential treatment [20 - 22] and without the caregiver components as an intervention for drug using, homeless adolescents [23] . More recently, Garner et al [19] collected data from 399 adolescents who participated in one of four randomly controlled trials of the A-CRA intervention, the purpose of which was to examine the extent to which exposure to A-CRA procedures mediated the relationship between treatment retention and outcomes. The authors found adolescents who were exposed to 12 or more A-CRA procedures were significantly more likely to be in recovery at follow-up.

Combining A-CRA with relapse prevention strategies receives strong support as an evidence based, best practice model and is widely employed in addiction treatment programmes. Providing a CBT-ACRA therapeutic approach is imperative as it develops alternative ways of meeting needs and thus altering dependence.

2. Case Introduction

Alan is a 17 year-old male currently living in County Dublin. Alan presented to the agency involuntarily and as a requisite of his Juvenile Liaison Officer who was seeing him on foot of prior drugs arrest for ‘possession with intent to supply’; a more serious charge than a simple ‘drugs possession’ charge. As Alan had no previous charges he was placed on probation for one year. This was Alan’s first contact with the treatment services. A diagnostic assessment was completed upon entry to treatment and included completion of a battery of instruments comprising the Maudsley Addiction Profile (MAP), The World Health Organization Alcohol, Smoking and Substance Involvement Screening Test (ASSIST) and the Beck Youth Inventory (BYI) (see appendices for full description of outcome measures) (Table 1).

3. Diagnostic Criteria

The apparent symptoms of substance dependency were: (1) Loss of Control - Alan had made several attempts at controlling the amounts of cannabis he consumed, but those times when he was able to abstain from cannabis use were when he substituted alcohol and/or other drugs. (2) Family History of Alcohol/Drug Usage - Alan’s eldest sister who is now 23 years old is in recovery from opiate abuse. She was a chronic heroin user during her early adult years [17 - 21] . During this period, which corresponds to Alan’s early adolescent years [12 - 15] she lived in the family home (3) Changes in Tolerance - Alan began per day. At presentation he was smoking six to eight cannabis joints daily through the week, and eight to twelve joints daily on weekends.

4. Psychosocial, Medical and Family History

At time of intake Alan was living with both of his parents and a sister, two years his senior, in the family home. Alan was the youngest and the only boy in his family. He had two other older sisters, 5 and 7 years his senior. He was enrolled in his 5th year of secondary school but at the time of assessment was expelled from all classes. Alan had superior sporting abilities. He played for the junior team of a first division football team and had the prospect of a professional career in football. He reported a family history positive for substance use disorders. An older sister was in recovery for opiate dependence. Apart from his substance use Alan reported no significant psychological difficulties or medical problems. His motives for substance use were cited as boredom, curiosity, peer pressure, and pleasure seeking. His triggers for use were relationship difficulties at home, boredom and peer pressure. Pre-morbid personality traits included thrill seeking and impulsivity (Table 2).

5. Case Conceptualisation

A CBT case formulation is based on the cognitive model, which hypothesizes that "a person’s feelings and emotions are influenced by their perception of events" . It is not the actual event that determines how the person feels, but rather how they construe the event (Beck, 1995 p14). Moreover, cognitive theory posits that the “child learns to construe reality through his or her early experiences with the environment, especially with significant others” and that “sometimes these early experiences lead children to accept attitudes and beliefs that will later prove maladaptive” [24] . A CBT formulation (or case conceptualisation) is one of the key underpinnings of Cognitive Behavioural Therapy (CBT). It is the ‘blueprint’ which aids the therapist to understand and explain the patient’s’ problems.

Formulation driven CBT enables the therapist to develop an individualised understanding of the patient and can help to predict the difficulties that a patient may encounter during therapy. In Alan’s case, exploring his existing negative automatic thoughts about regarding school and his academic competences highlighted the difficulties he could experience with CBT homework completion. Whilst Alan was good at between session therapy assignments, an exploration of what is meant by ‘homework’ in a CBT context was crucial.

A collaborative CBT formulation was done diagrammatically together with Alan (Figure 1). This formulation aimed to describe his presenting problems and using CBT theory, to explore explanatory inferences about the initiating and maintaining factors of his drug use which could practically inform meaningful interventions.

Simmons and Griffiths et al. make the insightful observation that particular group differences need to be specifically considered and suggest that the therapist should be cognizant of the role of both society and culture when developing a formulation. They firstly suggest that the impact played by gender, sexuality and socio-cultural roles in the genesis of a psychological disorder, namely the contribution that being a member of a group may have on predisposing and precipitating factors, be carefully considered. An example they offer is the role of poverty on the development of psychological problems, such as the link evidenced between socio economic group and onset of schizophrenia. This was clearly evident in the case of Alan, who being a member of a deprived socioeconomic group, growing up and living in an area with a high level of economic deprivation, perceived that his choices for success were limited. His thinking, as an adolescent boy, was dichotomous in that he saw himself as having only two fixed and limited choices (a) being good at sport he either pursue a career as a professional sportsman or alternatively (b) he engage in crime and work his way up through the ranks as a ‘career criminal’. Simmons & Griffiths secondly suggest that being a member of a particular group can heavily influence a person’s understanding of the causality of their psychological disorder. A third consideration when developing a formulation is the degree to which being a member of a particular group may influence the acceptance or rejection of a member experiencing a psychological illness. Again this is pertinent in Alan’s case as he was part of a sub-group, a gang engaged in crime. For this cohort, crime and drug use were synonymous. Using drugs was viewed as a rite of passage for Alan.

Drug use, according to CBT models, are socially learned behaviours initiated, maintained and altered through the dynamic interaction of triggers, cues, reinforcers, cognitions and environmental factors. The application of a such a formulation, sensitive to Simmons and Griffiths (2009) aforementioned observations, proved useful in affording insights into the contextual and maintaining factors of Alan’s drug use which was heavily influenced by the availability of drugs ,his peer group (with whom he spent long periods of time) and their petty drug dealing and criminality. Similarly, engaging with his football team mates during the lead up to an important match significantly reduced his drug use and at certain times of the year even lead to abstinence. Sharing this formulation allowed him to note how his drug use patterns were driven, as per the CBT paradigm, by modifiable external, transient, and specific factors (e.g. cues, reinforcements, social networks and related expectations and social pressures).

Employing the A-CRA model allowed for this tailored fit as A-CRA specifically encourages the patient to identify their own need and desire for change. Alan identified the specific needs that were met by using substances and he developed and reinforced skills that provided him with alternative ways of meeting those needs. This model worked extremely well for Alan as he had identified and had ready access to a pro- social ‘alternative group’ or community. As he had had access to an alternative positive peer group and another activity (sport) which he was ‘really good at’, he simply needed to see the evidence of how his context could radically affect his substance use; more specifically how his beliefs, thinking and actions in certain circumstances produced very different drug use consequences and outcomes.

6. Course of Treatment and Assessment of Progress

One focus of CBT treatment is on teaching and practising specific helpful behaviours, whilst trying to limit cognitive demands on clients. Repetition is central to the learning process in order to develop proficiency and to ensure that newly acquired behaviours will be available when needed. Therefore, behavioural using rehearsal will emphasize varied, realistic case examples to enhance generalization to real life settings. During practice periods and exercises, patients are asked to identify signals that indicate high-risk situations, demonstrating their understanding of when to use newly acquired coping skills. CBT is designed to remedy possible deficits in coping skills by better managing those identified antecedents to substance use. Individuals who rely primarily on substances to cope have little choice but to resort to substance use when the need to cope arises. Understanding, anticipating and avoiding high risk drug use scenarios or the “early warning signals” of imminent drug use is a key CBT clinical activity.

A major goal of a CBT/A-CRA therapeutic approach is to provide a range of basic alternative skills to cope with situations that might otherwise lead to substance use. As ‘skill deficits’ are viewed as fundamental to the drug use trajectory or relapse process, an emphasis is placed on the development and practice of coping skills. A-CRA was manualised in 2001 as part of the Cannabis Youth Treatment Series (CYT) and was tested in that study [21] and more recently with homeless youth [23] . It was also adapted for use in a manual for Assertive Continuing Care following residential treatment [20] .

There are twelve standard and three optional procedures proposed in the A-CRA model. The delivery of the intervention is flexible and based on individual adolescent needs, though the manual provides some general guidelines regarding the general order of procedures. Optional procedures are ‘Dealing with Failure to Attend’, ‘Job-Seeking Skills’, and ‘Anger Management’. Standard procedures are included in table 3 below. For a more detailed description of sessions and procedures please see appendices.

Smith and Myers describe the theoretical underpinnings of CRA as a comprehensive behavioural program for treating substance-abuse problems. It is based on the belief that environmental contingencies can play a powerful role in encouraging or discouraging drinking or drug use. Consequently, it utilizes social, recreational, familial, and vocational reinforcers to assist consumers in the recovery process. Its goal is to essentially make a sober lifestyle more rewarding than the use of substances. Interestingly the authors note: ‘Oddly enough, however, while virtually every review of alcohol and drug treatment outcome research lists CRA among approaches with the strongest scientific evidence of efficacy, very few clinicians who treat consumers with addictions are familiar with it’. ‘The overall philosophy is to promote community based rewarding of non drug-using behaviour so that the patient makes healthy lifestyle changes’ p.3 [25] .

A-CRA procedures use ‘operant techniques and skills training activities’ to educate patients and present alternative ways of dealing with challenges without substances. Traditionally, CRA is provided in an individual, context-specific approach that focuses on the interaction between individuals and those in their environments. A-CRA therapists teach adolescents when and where to use the techniques, given the reality of each individual’s social environment. This tailored approach is facilitated by conducting a ‘functional analysis’ of the adolescent’s behaviour at the beginning of therapy so they can better understand and interrupt the links in the behavioural chain typically leading to episodes of drug use. A-CRA therapists then teach individuals how to improve communication and other skills, build on their reinforcers for abstinence and use existing community resources that will support positive change and constructive support systems.

A-CRA emphasises lapse and relapse prevention. Relapseprevention cognitive behavioural therapy (RP-CBT) is derived from a cognitive model of drug misuse. The emphasis is on identifying and modifying irrational thoughts, managing negative mood and intervening after a lapse to prevent a full-blown relapse [26] . The emphasis is on development of skills to (a) recognize High Risk Situations (HRS) or states where clients are most vulnerable to drug use, (b) avoidance of HRS, and (C) to use a variety of cognitive and behavioural strategies to cope effectively with these situations. RPCBT differs from typical CBT in that the accent is on training people who misuse drugs to develop skills to identify and anticipate situations or states where they are most vulnerable to drug use and to use a range of cognitive and behavioural strategies to cope effectively with these situations [26] .

7. Access and Barriers to Care

Alan engaged with the service for eight months. During this time he received twenty sessions, three of which were assessment focused, the remaining seventeen sessions were A-CRA focused; two of the seventeen involved his mother, the remaining fifteen were individual. As Alan was referred by the probation services, he was initially somewhat ambivalent about drug use focussed interventions. His early motivation for engagement was primarily to avoid the possibility of a custodial sentence.

8. Treatment

My sessions with Alan were guided by the principles of A-CRA [27] which focuses on coping skills training and relapse prevention approaches to the treatment of addictive disorders. Prior to engaging with Alan, I had completed the training course and commenced the A-CRA accreditation process, both under the stewardship of Dr Bob Meyers, whose training and publication offers detailed guidelines on skills training and relapse prevention with young people in a similar context [27] .

During the early part of each session I focused on getting a clear understanding of Alan’s current concerns, his general level of functioning, his substance abuse and pattern of craving during the past week. His experiences with therapy homework, the primary focus being on what insight he gained by completing such exercises was also explored. I spent considerable time engaged in a detailed review of Alan’s experience with the implementation of homework tasks during which the following themes were reviewed:

-Gauging whether drug use cessation was easier or harder than he anticipated? -Which, if any, of the coping strategies worked best? -Which strategies did not work as well as expected. Did he develop any new strategies? -Conveying the importance of skills practice, emphasising how we both gained greater insights into how cognitions influenced his behaviour. After developing a clear sense of Alan’s general functioning, current concerns and progress with homework implementation, I initiated the session topic for that week. I linked the relevance of the session topic to Alan’s current cannabis-related concerns and introduced the topic by using concrete examples from Alan’s recent experience. While reviewing the material, I repeatedly ensured that Alan understood the topic by asking for concrete examples, while also eliciting Alan’s views on how he might use these particular skills in the future.

Godley & Meyers [21] propose a homework exercise to accompany each session. An advantage of using these homework sheets is that they also summarise key points about each topic and therefore serve as a useful reminder to the patient of the material discussed each week. Meyers, et al. (2011) suggests that rather than being bound by the suggested exercises in the manualised approach, they may be used as a starting point for discussing the best way to implement the required skill and to develop individualised variations for new assignments [27] . The final part of each session focused on Alan’s plan for the week ahead and any anticipated high-risk situations. I endeavoured to model the idea that patients can literally ‘plan themselves out of using’ cannabis or other drugs. For each anticipated high-risk situation, we identified appropriate and viable coping skills. Better understanding, anticipating and planning for high-risk situations was difficult in the beginning of treatment as Alan was not particularly used to planning or thinking through his activities. For a patient like Alan, whose home life is often chaotic, this helped promote a growing sense of self efficacy. Similarly, as Alan had been heavily involved with drug use for a long time, he discovered through this process that he had few meaningful activities to fill his time or serve as alternatives to drug use. This provided me with an opportunity to discuss strategies to rebuild an activity schedule and a social network.

During our sessions, several skill topics were covered. I carefully selected skills to match Alan’s needs. I selected coping skills that he has used in the past and introduced one or two more that were consistent with his cognitive style. Alan’s cognitive score indicated a cognitive approach reflecting poor problem solving or planning. Sessions focused on generic skills including interpersonal skills, goal setting, coping with criticism or anger, problem solving and planning. The goal was to teach Alan how to build on his pro- social reinforcers, how to use existing community resources supportive of positive change and how to develop a positive support system.

The sequence in which these topics were presented was based on (a) patient needs and (b) clinician judgment (a full description of individual sessions may be found in appendices).

9. Assessment of Treatment Outcome

A baseline diagnostic assessment of outcomes was completed upon treatment entry. This assessment consisted of a battery of psychological instruments including (see appendices for full a description of assessment measures):

-The Maudsley Addiction Profile (MAP). -The Beck Youth Inventories. -The World Health Organization Alcohol, Smoking and Substance Involvement Screening Test (ASSIST).

In addition to the above, objective feedback on Alan’s clinical and drug use status through urine toxicology screens was an important part of his drug treatment. Urine specimens were collected before each session and available for the following session. The use of toxicology reports throughout treatment are considered a valuable clinical tool. This part of the session presents a good opportunity to review the results of the most recent urine toxicology screen and promote meaningful therapeutic activities in the context of the patient’s treatment goals [28] .

In reporting on substance use since the last session, patients are likely to reveal a great deal about their general level of functioning and the types of issues and problems of most current concern. This allows the clinician to gauge if the patient has made progress in reducing drug use, his current level of motivation, whether there is a reasonable level of support available in efforts to remain abstinent and what is currently bothering him. Functional analyses are opportunistically used throughout treatment as needed. For example, if cannabis use occurs, patients are encouraged to analyse antecedent events so as to determine how to avoid using in similar situations in the future. The purpose is to help the patient understand the trajectory and modifiable contextual factors associated with drug use, challenge unhelpful positive drug use expectancies, identify possible skills deficiencies as well as seeking functionally equivalent non- drug using behaviours so as to reduce the probability of future drug use. The approach I used is based on the work of [28] .

The Functional Analysis was used to identify a number of factors occurring within a relatively brief time frame that influenced the occurrence of problem behaviours. It was used as an initial screening tool as part of a comprehensive functional assessment or analysis of problem behaviour. The results of the functional analysis then served as a basis for conducting direct observations in a number of different contexts to attest to likely behavioural functions, clarify ambiguous functions, and identify other relevant factors that are maintaining the behaviour.

The Happiness Scale rates the adolescent’s feelings about several critical areas of life. It helps therapists and adolescents identify areas of life that adolescents feel happy about and alternatively areas in which they have problems or challenges. Most importantly it identifies potential treatment goals subjectively meaningful to the patient, facilitates positive behaviour change in a range of life domains as well as help clients track their progress during treatment.

Alan’s BYI score (Table 4) indicates that at the time of assessment he was within the average scoring range on ‘self-concept’, and moderately elevated in the areas of ‘depression’, ‘anxiety’, and ‘disruptive behaviour’. His score for ‘anger’ suggested that his anger fell within the extremely elevated range. When this was discussed with Alan he agreed that this was quite accurate. Anger, and in particular controlling his anger, was subjectively identified as a treatment goal.

10. Follow-up

Given that follow-up occurred by telephone it was not feasible to administer the full battery of tests. With Alan’s treatment goals in mind it was decided to administer the MAP and ASSIST. Table 5 below illustrates Alan’s score at baseline and follow-up for the MAP and ASSIST. For summary purposes I have taken areas for concern at baseline for both instruments.

Alan’s score for cannabis was the most clinically relevant as it placed him in the 'high risk’ domain while his alcohol score indicated that he had engaged in binge drinking (6+ drinks) at T1. However, at T2 Alan’s score suggests that he had made considerable reductions in the use of both substances. Also his MAP scores for parental conflict and drug dealing suggest that he had also made major positive changes in the relevant domains of personal and social functioning as well as ceasing criminal behaviour.

At 3 months post-discharge I contacted Alan by phone. He had maintained and continued to further his progress. His drug use was at a minimal level (1 or 2 shared joints per month). He was no longer engaged in crime and his probationary period with the judicial system had passed. He had received a caution for his earlier drugs charge. At the time of follow-up he was enjoying participating in a Sports Coaching course and was excelling with his study assignments. Relationships had improved considerably with his mother and sister and he had re-engaged with a previous, positive, peer group linked to his involvement with the GAA . Overall he felt he was doing extremely well.

11. Complicating Factors with A-CRA Model

There are many challenges that may arise in the treatment of substance use disorders that can serve as barriers to successful treatment. These include acute or chronic cognitive deficits, health problems, social stressors and a lack of social resources [7] . Among individuals presenting with substance use there are often other significant life challenges including early school leaving, family conflicts, legal issues, poor or deviant social networks, etc. A particular challenge with Alan’s case was the social and environmental milieu which he shared with his drug using peers. For Alan, who initially had few skills and resources, engaging in treatment meant not only being asked to change his overall way of life but also to renounce some of those components in which he enjoyed a sense of belonging, particularly as he had invested significantly in these friendships. A sense of ‘belonging to the substance use culture’ can increase ambivalence for change [7] . Alan’s mother strongly disapproved of his drug using peer group and failed to acknowledge Alan’s perceived loss. This resulted in mother- son conflict. The use of the caregiver session allowed an exploration of perceived ‘losses’ relative to the ‘gains’ associated with Alan’s abstinence. It was moreover seen to be critical to establish alternatives for achieving a sense of belonging, including both his social connection and his social effectiveness. Alan’s sports ability allowed for this to be fostered. He is a talented sportsman which often meant his acceptance within a team or group is a given.

Despite the positive effects of A-CRA it is not without its shortcomings. The approach is at times quite American- oriented, particularly around identifying local resources and its focus on culturally specific outlets in promoting social engagement as alternatives to substance use. While this is supported in the literature, it may not necessarily be transferable to certain Irish adolescent contexts or subcultures.

12. Treatment Implications of the Case

A-CRA captures a broad range of behavioural treatments including those targeting operant learning processes, motivational barriers to improvement and other more traditional elements of cognitivebehavioural interventions. Overall, this intervention has demonstrated efficacy. Despite this heterogeneity, core elements emerge based in a conceptual model of SUDs as disorders characterized by learning processes and driven by the strongly reinforcing effects of the substances of abuse. There is rich evidence in the substance use disorders literature that improvement achieved by CBT (7) and indeed A-CRA (Godley et al. and Garner et al. [22 , 20] ) generalizes to all areas of functioning, including social, work, family and marital adjustment domains. The present study’s finding that a reduction in substance-related symptoms was accompanied by improved levels of functioning, social adjustment and enhanced quality of life, provides further support for this point.

In conclusion, there is some preliminary evidence that A-CRA is a promising treatment in the rehabilitation of adolescent substance users in Ireland and culturally similar societies. Clearly, results from a case study have limited generalisability and there is need for larger controlled studies providing robust outcomes to confirm the efficacy of A-CRA in an Irish context. A more systematic study of this issue is in the interest of adolescent substance users and the health services providers faced with the challenge of providing affordable, evidencebased mental health and addiction care to young people.

13. Recommendations to Clinicians and Students

The ACRA model is a structured assemblage of a range of cognitive and behavioural activities (e.g. a rationale and overview of the paradigm, sobriety sampling, functional analyses, communication skills, problem solving skills, refusal skills, jobs counselling, anger management and relapse prevention) which are shared in varying degrees with other CBT approaches. The ACRA model has the advantage of established effectiveness. A foundation in empirical research together with its manual- supported approach results in it being an appropriate “off the shelf ” intervention, highly applicable to many adolescent substance misusers. Such a focussed approach also has the advantage of limiting therapist “drift”. Notwithstanding the accessible manual and other resources available on- line, clinicians and students are strongly encouraged to undergo accredited ACRA training and supervision.

Unfortunately such a structured model, despite its many advantages, does have limitations. This model may not meet the sum of all drug misusing adolescent service user treatment needs, nor is it applicable to all adolescent drug users, particularly highly chaotic individuals with high levels of co- morbidities or multi-morbidities as often found in this population [29 , 30] . Whilst focussing on specifically on drug use, ACRA does not directly address co-existing problem behaviours or challenges such as depression, anxiety, personality disorder, or post traumatic stress disorder (PTSD) synergistically linked to drug use. It is possible that given the high levels of dual diagnoses encountered in this population as well as the compounding effect that drug use exerts on multiple systems, clinicians and practitioners may find a strict application of the ACRA model limiting, necessitating the application of an additional range or layer of psychotherapeutic competencies? Additionally the ACRA model does not focus explicitly on other psychological activities useful in the treatment of drug misuse such as the control and management of unhelpful cognitive styles or habits; breathing or progressive relaxation skills; anger management; imagery, visualisation and mindfulness. That is, as a manual based approach comprising a number of fixed components, a major potential challenge facing clinicians and students is the tension they may experience between maintaining strict fidelity to a pure ACRA approach, versus the flexibility l approved by more formulation driven CBT approaches?

The advantages of a skilled application of a formulation driven approach which are cited and summarised in are multiple and include the collaborative nature of goal setting, the facilitation of problem prioritisation in a meaningful and useful manner; a more immediate direction and structuring of the course of treatment; the provision of a rationale for the most fitting intervention point or spotlight for the treatment; an integration of seemingly unrelated or dissimilar difficulties in a meaningful yet parsimonious fashion; an influence on the choice of procedures and “homework” exercises; theory based mechanisms to understand the dynamics of the therapeutic relationship and a sense of targeted and ‘extra-therapeutic’ issues and how they could be best explained and managed, especially in terms of precipitators or triggers, core beliefs, assumptions and automatic thoughts.

Thus given the above observations and together with the importance placed on engagement and retention, the high variability in the cognitive, emotional, social and developmental domains [4] differences in roles (e.g. teenagers who are also parents) and levels of autonomy as well as high degrees of dual diagnosis or co- morbidities found in this group [29 , 30] practitioners are encouraged to also develop competencies in allied psychological treatment models such as Motivational Interviewing [31] ; familiarity with the core principles of CBT, disorder specific and problem-specific CBT competences, the generic and meta- competences of CBT as well as an advanced knowledge and understanding of mental health problems that will provide practitioners with the confidence and capacity to implement treatment models in a more flexible yet coherent manner,. In addition to seeking supervision and mentorship students and practitioners are directed, as a starting point, to University College London’s excellent resources outlining the competencies required to provide a more comprehensive interventions [11] .

Both authors reported no conflict of interest in the content of this paper.

Author Contributions

Conceived and designed the experiments: JI. Recruitment & assessment and on going treatment t of patient JI. On going supervision of case KD. Contributed reagents/materials/analysis tools: JI, & KD. Wrote the paper: JI. Contributed to final draft paper KD.

Acknowledgments

We thank Adolescent Addiction Services, Health Service Executive.

Compton WM, Thomas YF, Stinson FS, Grant BF (2007) Prevalence, correlates, disability, and comorbidity of DSM-IV drug abuse and dependence in the United States: results from the national epidemiologic survey on alcohol and related conditions. Arch Gen Psychiatry 64: 566-576. View
Hasin DS, Stinson FS, Ogburn E, Grant BF (2007) Prevalence, correlates, disability, and comorbidity of DSM-IV alcohol abuse and dependence in the United States: results from the National Epidemiologic Survey on Alcohol and Related Conditions. Arch Gen Psychiatry 64: 830-842. View
Carroll KM, Nich C, Ball SA, McCance E, Frankforter TL, et al. (2000) Oneyear follow-up of disulfiram and psychotherapy for cocaine-alcohol users: sustained effects of treatment. Addiction 95: 1335-1349. View
Stetler CB, Ritchie J, Rycroft-Malone J, Schultz A, Charns M (2007) Improving quality of care through routine, successful implementation of evidence-based practice at the bedside: an organizational case study protocol using the Pettigrew and Whipp model of strategic change. Imp Sci 2: 1-13. View
Carroll KM, Fenton LR, Ball SA, Nich C, Frankforter TL, et al. (2004) Efficacy of disulfiram and cognitive behavior therapy in cocaine-dependent outpatients: a randomized placebo-controlled trial. Arch Gen Psychiatry 61: 264-272. View
Carroll KM, Ball SA, Martino S, Nich C, Babuscio TA, et al. (2008) Computer-assisted delivery of cognitive-behavioral therapy for addiction: a randomized trial of CBT4CBT. Am J Psychiatry 165: 881-888. View
McHugh RK, Hearon BA, Otto MW (2010) Cognitive behavioral therapy for substance use disorders. Psychiatr Clin North Am 33: 511-525. View
Waldron HB, Kaminer Y (2004) On the learning curve: the emerging evidence supporting cognitive-behavioral therapies for adolescent substance abuse. Addiction 99 Suppl 2: 93-105. View
Freeman A, Reinecke MA (1995) Cognitive therapy. New York: Guilford Press.
Stephens RS, Babor TF, Kadden R, Miller M; Marijuana Treatment Project Research Group (2002) The Marijuana Treatment Project: rationale, design and participant characteristics. Addiction 97 Suppl 1: 109-124. View
Pilling S, Hesketh K, Mitcheson L (2009) Psychosocial interventions in drug misuse: a framework and toolkit for implementing NICE-recommended treatment interventions. London: British Psychological Society, Centre for Outcomes, Research and Effectiveness (CORE) Research Department of Clinical, Educational and Health Psychology, University College. View
Azrin NH (1976) Improvements in the community-reinforcement approach to alcoholism. Behav Res Ther 14: 339-348. View
Hunt GM, Azrin NH (1973) A community-reinforcement approach to alcoholism. Behav Res Ther 11: 91-104. View
Azrin NH, Sisson RW, Meyers R, Godley M (1982) Alcoholism treatment by disulfiram and community reinforcement therapy. J Behav Ther Exp Psychiatry 13: 105-112. View
Meyers RJ, Miller WR (2001) A community reinforcement approach to addiction treatment: (International Research Monographs in the Addictions) Cambridge Univ Press.
Mallams JH, Godley MD, Hall GM, Meyers RJ (1982) A social-systems approach to resocializing alcoholics in the community. J Stud Alcohol 43: 1115-1123.
Finney JW, Monahan SC (1996) The cost-effectiveness of treatment for alcoholism: a second approximation. J Stud Alcohol 57: 229-243. View
Rollnick S, Miller WR (1999) What is motivational interviewing? Behav Cogn Psychotherapy. 23: 325-334. View
Garner BR, Barnes B, Godley SH (2009) Monitoring fidelity in the Adolescent Community Reinforcement Approach (A-CRA): the training process for A-CRA raters. J Behav Anal Health Fit Med 2: 43-54. View
Dennis M, Godley SH, Diamond G, Tims FM, Babor T, et al. (2004) The Cannabis Youth Treatment (CYT) Study: main findings from two randomized trials. J Subst Abuse Treat 27: 197-213. View
Garner BR, Godley MD, Funk RR, Dennis ML, Godley SH (2007) The impact of continuing care adherence on environmental risks, substance use, and substance-related problems following adolescent residential treatment. Psychol Addict Behav. 21: 488-497. View
Slesnick N, Prestopnik JL, Meyers RJ, Glassman M (2007) Treatment outcome for street-living, homeless youth. Addict Behav 32: 1237-1251. View
Beck AT, Hollon SD, Young JE, Bedrosian RC, Budenz D (1985) Treatment of depression with cognitive therapy and amitriptyline. Arch Gen Psychiatry 42: 142-148. View
Smith C (1998) Assessing health needs in women's prisons. PSJ 224.
Maude-Griffin PM1, Hohenstein JM, Humfleet GL, Reilly PM, Tusel DJ, et al. (1998) Superior efficacy of cognitive-behavioral therapy for urban crack cocaine abusers: main and matching effects. J Consult Clin Psychol 66: 832-837. View
Godley SH, Meyers RJ, Smith JE, Karvinen T, Titus JC, et al. (2001) The adolescent community reinforcement approach for adolescent cannabis users: US Department of Health and Human Services. View
Carroll KM (1998) A cognitive-behavioral approach: Treating cocaine addiction. National Institute on Drug Abuse. View
Bukstein OG, Glancy LJ, Kaminer Y (1992) Pattern of affective comorbidity in a clinical population of dually diagonised adolescent substance abusers. J Ame Aca Child Adol Psychiat 31: 1041-1045. View
Kaminer Y, Burleson JA, Goldberger R (2002) Psychotherapies for adolescent substance abusers: Short- and long-term outcomes. J Nerv Ment Dis 190: 737-745.
Miller WR, Rollnick S (2002) Motivational interviewing: Preparing people for change. 2nd ed. New York: Guilford Press. View

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Mental health disorders and teen substance use.

Why it's especially tempting — and risky — for kids with emotional or behavioral challenges

Writer: Caroline Miller

Clinical Expert: Sarper Taskiran, MD

What You'll Learn

Why is substance use more dangerous for teens with mental health disorders?
Why do teens with mental health disorders use substances?
Why does substance use make a mental health disorder worse?

When teenagers are upset or angry, they often turn to alcohol or drug use to help them manage their feelings. While adults do this too, a teenager’s brain is still developing. So if teens use drugs or alcohol to feel better, they’ll run into problems more quickly than adults.

Drinking or smoking marijuana can help make hopelessness , anxiety , irritability and negative thoughts disappear quickly. But over time, they’ll make them worse. Teens also become addicted more quickly than adults. This is especially true if they have mental health disorders .

If they’re not treated, almost half of kids with mental health disorders will end up having a substance use disorder. This can make it harder to treat their mental health. It can also impact their future.

Alcohol affects teenagers differently. Instead of slowing them down, alcohol can make teens feel more energetic, do riskier things and get more aggressive . Drinking is even more dangerous for kids with ADHD because they’re already impulsive. Substance use can also make depressed teenagers more prone to impulsive suicidal behavior .

Substance use is also a problem for teens or young adults with schizophrenia. If a person with schizophrenia uses drugs or alcohol, they’re more likely to have psychotic episodes.

Experts note that to encourage teens to reduce or stop substance use, it’s important give them other coping strategies to manage their problems without turning to substances.

When teenagers are struggling with emotional problems, they often turn to alcohol or drug use to help them manage painful or difficult feelings. In this, they are not different from adults. But because adolescent brains are still developing, the results of teenage “self-medication” can be more immediately problematic.

In the short term, substance use can help alleviate unwanted mental health symptoms like hopelessness , anxiety , irritability, and negative thoughts . But in the longer term, it exacerbates them and often ends in abuse or dependence. Substance use escalates from experimentation to a serious disorder much faster in adolescents than it does in adults, and that progression is more likely to happen in kids with mental health disorders than in other kids.

“The rule of thumb is that almost half of the kids with mental health disorders if they’re not treated, will end up having a substance use disorder,” explains Sarper Taskiran , MD, a child and adolescent psychiatrist at the Child Mind Institute. A 2016 study of 10,000 adolescents found that two-thirds of those who developed alcohol or substance use disorders had experienced at least one mental health disorder.

Substance use also interferes with treatment for mental health disorders and worsens the long-term prognosis for a teenager struggling with one. How can we help these young people avoid the substance use trap when the deck seems to be stacked against them?

Why are kids with mental health disorders prone to substance use?

Kids who are anxious or depressed may feel more emotionally “even” if they drink or smoke marijuana. For socially anxious kids, it can quiet the anxiety enough to allow them to function in peer groups. And since their friends do it, it’s not stigmatized the way taking medication is.

“Pre-gaming is a lot about anxiety,” notes Jeanette Friedman, MSW, who works with families of adolescents with substance use problems. “The kids are saying, ‘Let’s go have some fun before we go to the real party.’ But in fact, most of them feel like they need it to calm down enough so they can walk into a group where they’re going to feel exposed and criticized.”

A teen with anxiety might start by smoking marijuana to calm down before social events and soon find himself smoking every morning just to get to school. “I’ve had very stressed-out kids say, ‘I get high before I go to school because I’m so anxious when I think about the start of the school day,’ says Ms. Friedman. “‘If I smoke a little weed, I don’t feel so anxious.’”

Kids who are depressed may use alcohol or marijuana to cheer themselves up, Dr. Taskiran notes, and blunt the irritability that is a symptom of adolescent depression . “They know there’s something wrong with them,” he says. “They’re not taking pleasure in things, they’re not feeling happy. So if their peers are offering a drug that makes you happy, that’s often the first thing they turn to.” Substance use can quiet negative thoughts that plague depressed kids.

It’s also common for children with mental health or learning disorders to develop self-esteem problems, a sense that there’s something wrong with them or that they’re flawed. When these children reach adolescence, with its focus on fitting in, notes Ms. Friedman, “They really want to be normal, and they don’t feel normal. And that means they’re more vulnerable to somebody passing around a drug because they’re just trying to feel better.”

Why is alcohol use riskier for teenagers?

Alcohol affects teens differently from adults. While adults tend to get more subdued and slowed down by alcohol, in adolescents, it’s the opposite. They tend to become more energetic, engage in more risky behavior and get more aggressive .

Dr. Taskiran uses the example of driving . “When adults drink and drive, you worry about slowing of the reflexes and lapses in attention, like missing a stop sign,” he explains. “But with adolescents, we’re worried that they’re going to get more activated. It’s not that they won’t see the red light, but they might try to run it.”

This is especially dangerous for kids with ADHD , who are already impulsive. And substance use makes depressed teenagers more prone to impulsive suicidal behavior . “The adolescent will still be depressed,” says Dr. Taskiran, “but the things that usually hold him back won’t be there while he’s intoxicated, like love for family or the belief that he’s going to get better.”

Why teenagers get addicted sooner

Adolescent alcohol or drug use accelerates very quickly when an untreated mental health disorder is present. ”Within months, we can see problematic use,” says Dr. Taskiran.

Why are they different than adults? In the adolescent brain, pathways between regions are still developing. This is why teens learn new things quickly. This “plasticity” means the brain easily habituates to drugs and alcohol. “If you start drinking at 30, you don’t get addicted nearly as fast as if you start drinking at 15,” adds Ms. Friedman.

Alcohol and drugs also affect the same brain regions that are at play in behavior disorders like ADHD and ODD , says Dr. Taskiran. Teenagers who have those disorders get more satisfaction from the substance — and are more likely to become addicted. “Biologically they get more from the drug,” he adds, “so that’s why they get more hooked on it.”

It’s important to know that substance use can disrupt a young person’s life even if they are not technically dependent on the drug. This is especially true for youth with mental health disorders. “You might not see withdrawal, you might not see the craving, which are the hallmark symptoms for dependence,” says Dr. Taskiran. “But the impact in their social life and academic life, or in terms of their mental wellbeing, might still be large.”

Why substance use makes depression and anxiety worse

“ Self-medicating” with recreational drugs and alcohol works temporarily to alleviate symptoms of anxiety or depression because they affect the same brain regions that the disorders do. But the result is that teens feel even worse when not using. That’s one reason substance use is a risk factor for suicide in kids with depression, Dr. Taskiran notes.

Another negative effect of substance use is that it undermines treatment. First, it diminishes a teenager’s engagement in therapy , and hence its effectiveness. Second, if they are taking prescription medication, it may lower the effectiveness of that medication. “The drugs and the medications target the same areas of the brain,” explains Dr. Taskiran. When meds have to compete with drugs or alcohol, they are less effective. “Also, it’s not uncommon for kids who are using substances to be non-compliant with their meds.”

Psychosis and substance use

Michael Birnbaum, MD, is a psychiatrist who heads an early treatment program for young people who have had a first psychotic episode , usually signaling the onset of schizophrenia. Dr. Birnbaum estimates that at least 50 percent of his patients have at least some history of drug and alcohol use. Getting a handle on substance use is important for the recovery process, he says. “Folks who are still using are more likely to struggle with ongoing psychotic symptoms and also are more likely to have a relapse.”

Most of the people who come to the early treatment program have just come from a hospitalization , he notes, and they are eager to make sure that doesn’t happen again. “So part of the discussion is how do we prevent a relapse?” he continues. At Dr. Birnbaum’s program, clinicians work to understand what substance use was doing for the patient. “It may seem obvious to us,” he says. “’Okay, you need to stop using now.’ But there may be other reasons for continued use that, to the patient, outweigh the risks.”

Dr. Taskiran echoes that approach. “The last thing I’d say from the get-go to one of my patients is, ‘Marijuana is bad for you,’ because the kid has heard that from teachers, parents, TV, everywhere. So instead, what I say is, ‘What is it doing for you? What are you getting out of it?’”

All behavior serves a purpose, even if it’s self-injurious or risky behavior. “If you’re trying to take something away from a teenager, you need to replace it with something,” says Dr. Taskiran. “So instead of just saying, ‘Don’t do that, it’s bad for you,’ we’re trying to replace the need for substance with a coping strategy, with tools for coping without the substances.”

Frequently Asked Questions

W hen teens are struggling with mental health issues , they often turn to substance use to help them manage painful or difficult feelings — not unlike adults. But because their brains are still developing, the results of teenage “self-medication” can be more immediately problematic.

Almost half of kids with untreated mental health disorders will end up having a substance use disorder. A 2016 study of 10,000 adolescents found that two-thirds of those who developed alcohol or substance use disorders had experienced at least one mental health disorder.

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The independent source for health policy research, polling, and news.

Recent Trends in Mental Health and Substance Use Concerns Among Adolescents

Nirmita Panchal Published: Feb 06, 2024

In light of growing mental health concerns among adolescents in the United States, a National State of Emergency in Child and Adolescent Mental Health was issued in 2021, followed by advisories from the U.S. Surgeon General in 2021 and 2023 . This comes at a time when many adolescents have reported adverse experiences, youth drug overdose deaths have spiked, and gun violence has increased. In 2021, 42% of adolescents reported feelings of sadness and hopelessness – which can be indicative of depressive disorder – up from 28% in 2011. Further, a recent KFF poll found that 55% of the public see youth mental health issues as a crisis in the U.S.; and that many children and teenagers are not able to get the mental health services they need.

Data on youth mental health is limited and when it is available, parents or guardians often complete survey questionnaires on behalf of youth in their household. However, the recently released Teen National Health Interview Survey ( NHIS-Teen ) surveyed adolescents (ages 12-17) directly, which allows for a more direct representation of adolescent mental health. This brief uses the NHIS-Teen data – which was collected for an 18 month period from 2021 to 2022 – to provide an up-to-date analysis of adolescent mental health, utilization of mental health care, and unmet needs and how they vary across demographics, including sex and sexual identity. 1 Other survey data collected directly from adolescent populations, including the Youth Risk Behavior Surveillance System ( YRBSS ) and the National Survey on Drug Use and Health ( NSDUH ), are included to supplement and provide more context.

Key takeaways include:

In 2021 and 2022, 21% of adolescents reported experiencing symptoms of anxiety in the past two weeks and 17% reported experiencing symptoms of depression. Female and LGBT+ adolescents were more likely than their counterparts to report experiencing anxiety or depression.
Deaths due to drug overdose among adolescents more than doubled from 2018 (253 deaths) to 2022 (723 deaths). The largest increases in these deaths were among Hispanic and Black adolescents.
Suicides are the second leading cause of death among adolescents. These deaths peaked in 2018 but have declined in recent years. In 2022, suicide death rates were highest among American Indian and Alaska Native adolescents (22.2 per 100,000) followed by White adolescents (7.2 per 100,000). Adolescent males had higher rates of suicide compared to their female peers (8.1 vs. 3.8 per 100,000) in 2022; however, thoughts of suicide and suicide attempts were higher (and increased faster) for females.
In 2021 and 2022, 20% of adolescents reported receiving mental health therapy and 14% reported taking prescription medication. In general, LGBT+ and female adolescents were more likely to report receiving treatment than their counterparts.
Many adolescents reported adverse experiences, including bullying (34%), emotional abuse by a parent (17%), and neighborhood violence (15%) in 2021 and 2022. Ninety-two percent of adolescents reported extended use of screens, which can also negatively impact mental health and well-being.

What share of adolescents experience poor mental health and how does that vary?

Approximately one in five adolescents reported experiencing symptoms of anxiety or depression (Figure 1). In 2021 and 2022, 21% of adolescents reported experiencing symptoms of anxiety in the past two weeks and 17% reported experiencing symptoms of depression. Anxiety and depression can co-occur with other mental health disorders and are associated with suicide and substance use . Additionally, these conditions can impact school attendance and performance among youth.

Female adolescents were more likely than their male peers to report anxiety (31% vs. 12%) and depression (25% vs. 10%) in 2021 and 2022 (Figure 1). These differences among adolescents by sex are consistent with other historical survey data on experiences of poor mental health. In recent years, other indicators of poor mental health, including self-harm and eating disorders, which commonly co-occur with anxiety , have increased, particularly among adolescent females. Analyses of emergency department visits , hospital admissions , and privately-insured youth found that, compared to prior to the pandemic, the presentation of eating disorders increased sharply for adolescent females. Historically, eating disorders affect females more than males. Eating disorders can be very harmful for physical health and even result in death .

In 2021 and 2022, LGBT+ adolescents were more likely than their non-LGBT+ peers to report anxiety (43% vs. 14%) and depression (37% vs. 11%) (Figure 1). Prior survey data has found similar differences in experiences of poor mental health between LGBT+ and non-LGBT+ adolescents.

While data on racial and ethnic groups from NHIS-Teen is not included in this analysis, data from NSDUH and YRBSS shows little variation in mental health conditions among adolescents by racial and ethnic groups. For example, in 2022, 20% of adolescents experienced a major depressive episode in the past year, with no significant differences across racial and ethnic groups. The 2021 YRBSS survey found that the share of Hispanic high school students that reported persistent feelings of sadness and hopelessness (46%) – which can be indicative of depressive disorder – was slightly higher than the share reported by their White (41%), Black (39%), and Asian peers (35%). However, mental health conditions among adolescents of color may be underreported as a result of underdiagnosis , gaps in culturally sensitive mental health care , structural barriers , and stigma associated with accessing care. Note that the NHIS-Teen survey data does not disaggregate data on non-Hispanic adolescents by racial groups and, therefore, was not included in this analysis.

How have substance use and related deaths among adolescents changed in recent years?

Deaths due to drug overdose among adolescents more than doubled since the onset of the COVID-19 pandemic, largely driven by the synthetic opioid , fentanyl . After remaining stable for several years, KFF analysis of CDC WONDER data found that drug overdose deaths among adolescents increased from 253 deaths in 2018 to 723 deaths in 2022 (Figure 2). During the same period, the share of these overdose deaths involving opioids increased from 57% to 78%.

Although White adolescents continue to account for the largest share of adolescent drug overdose deaths, Black and Hispanic adolescents have experienced the fastest increase in these deaths in recent years. In 2022, White adolescents accounted for 49% of total adolescent drug overdose deaths, down from 63% in 2018. 2 This decrease reflects the rapid increase in drug overdose deaths among adolescents of color since the onset of the pandemic. By 2022, the drug overdose death rate of both Hispanic and Black adolescents (3.3 and 2.8 per 100,000) surpassed the overdose death rate of White adolescents (2.7 per 100,000) (Figure 3). Further, these drug overdose death rates increased more than fourfold among Hispanic and Black adolescents compared to prior to the pandemic.

Since the COVID-19 pandemic began, drug overdose deaths increased for both adolescent males and females, with an initial spike among males. From 2018 to 2022, the drug overdose death rate more than doubled among adolescent males (from 1.1 to 3.0 per 100,000) and females (from 1.0 to 2.5 per 100,000) (Figure 3).

Although drug overdose deaths among adolescents have increased, their use of some substances has declined over time. YRBSS data from 2011 to 2021 shows declines in adolescent use of several substances, including current alcohol use (from 39% to 23%), current marijuana use (from 23% to 16%), and ever used illicit drugs (from 19% to 13%). However, findings on whether substance use has increased among adolescents during the pandemic are mixed. Some research has shown that substance use decreased in 2021 among adolescents and then largely held steady in 2022. Other research found that among high school students who used substances prior to the pandemic, nearly one in three reported increases in substance use in 2021. Early initiation of substance use is associated with increased risk of addiction later in life.

Mental health and substance use issues can often co-occur among adolescents. Data from NSDUH found that adolescents experiencing a past year major depressive episode in 2022 were more likely than peers to have used illicit drugs (26% vs 12%) and marijuana (22% vs 9%) in the past year, misused opioids (3% vs 1%) in the past year, and engaged in binge drinking (6% vs 3%) in the past month. In total, 4% of adolescents reported both a past year major depressive episode and substance use disorder in 2022. A recent analysis found that 41% of youth ages 10-19 that died from a drug overdose between 2019 and 2021 had a documented mental health condition.

How have suicide and self-harm among adolescents changed in recent years?

In the past decade, CDC data show that adolescent deaths due to suicide increased and peaked in 2018 (1,750 deaths) before slowing and declining by 2022 (1,540 deaths). Suicide remains the second leading cause of death among adolescents. 3 However, from 2021 to 2022, the adolescent suicide death rate decreased by 8% (from 6.5 to 6.0 per 100,000) while the total population suicide death rate slightly increased . It is possible that some suicides are misclassified as drug overdose deaths since it can be difficult to determine whether drug overdoses are intentional . Forty-four percent of adolescent suicides were by firearm in 2022, compared to 40% in 2012. 4

The rate of suicide deaths is increasing faster among adolescents of color compared to their White peers. Suicide death rates remain highest among American Indian and Alaska Native (AIAN) adolescents; in 2022, the death rate for AIAN youth was three times higher than White youth (22.2 vs. 7.2 per 100,000, respectively; Figure 4). Although their suicide death rates were lower than White adolescents, Black, Asian, and Hispanic adolescents experienced larger increases in these death rates from 2012 to 2022 (129%, 48%, 30%, respectively; Figure 4) compared to their White peers (26%). Further, in 2021, Black high school students were more likely to report attempting suicide than their Asian, Hispanic, and White peers.

Among adolescents, male suicide rates are more than double the rates among females. Although the suicide death rate among adolescent females has increased faster than their male counterparts over the past decade, the adolescent female suicide death rate remains significantly lower than the death rate of their male peers (3.8 vs. 8.1 per 100,000 in 2022) (Figure 4). However, the share of adolescent females reporting serious thoughts of suicide remains higher and has increased faster over time (from 19% in 2011 to 30% in 2021) compared to adolescent males (from 13% in 2011 to 14% in 2021). Similar trends were seen in suicide attempts: from 10% in 2011 to 13% in 2021 among adolescent females, and from 6% to 7% over the same period for adolescent males. Additionally, as the pandemic progressed, emergency department visits for suicide attempts increased among adolescents, primarily driven by females.

LGBQ+ adolescents are more likely to experience suicidal thoughts compared to their heterosexual peers. Data from YRBSS found that in 2021 , higher shares of LGBQ+ adolescents reported serious thoughts of suicide (45% vs. 15%) and suicide attempts (22% vs. 6%) compared to heterosexual adolescents. 5 Data on suicide deaths by LGBQ+ identity were not available.

What share of adolescents report receiving mental health treatment in the past year and how does that vary?

Access to and sources of mental health services.

Among all adolescents, 20% reported receiving mental health therapy or counseling and 14% reported taking prescription medication for mental health in the past year (Figure 5). LGBT+ adolescents were more likely to report receiving mental health therapy or counseling (35%) and prescription medication (24%) for mental health in the past year than their counterparts (15% and 11%, respectively). Higher shares of female adolescents reported receiving mental health therapy or counseling compared to their male peers (24% vs. 16%).

Among adolescents with a past year major depressive episode, mental health services were most often accessed through outpatient care and telehealth. Data from NSDUH found that in 2022, 19.5% of adolescents (or 4.8 million) had a past year major depressive episode. Major depressive episode refers to a period of at least two weeks when an individual experienced a depressed mood or loss of interest or pleasure in daily activities and had a majority of specified depression symptoms. Among these adolescents with a past year major depressive episode, 48% received mental health services in an outpatient setting, which includes general medical and education settings (Figure 6). Thirty-four percent of adolescents with a past year major depressive episode received mental health care via telehealth (care received via phone or video from a therapist or other health care professional) in 2022. Additionally, 8% of adolescents with a past year major depressive episode accessed mental health care at emergency departments. There has been an uptick in mental health-related emergency visits in recent years; however, emergency departments may have limited capacity to address psychiatric illnesses.

Unmet need for mental health services

Although some adolescents received mental health care, 20% reported not receiving the mental health therapy they needed because of cost, fear of what others would think, and/or they did not know how to get help (Figure 7). This lack of needed therapy or counseling was more pronounced among female (32%) and LGBT+ adolescents (38%). While data on racial and ethnic groups from NHIS-Teen is not included in this analysis, other KFF analyses have found that receipt of mental health treatment is generally lower among people of color compared to their White peers.

Other factors that may contribute to limited mental health care access among adolescents include insurance barriers , a lack of providers , and the absence of culturally competent care . Additionally, in light of the COVID-19 pandemic, access and utilization of mental health care may have worsened . Among Medicaid and CHIP beneficiaries, utilization of mental health services declined by 25% for beneficiaries 18 and younger from March 2020 to July 2022 compared to prior to the pandemic; and utilization of substance use disorder services declined by 31% for beneficiaries ages 15-18 during the same period. Nearly two out of five children under the age of 18 in the U.S. are Medicaid or CHIP beneficiaries.

Although adolescent drug overdose deaths have increased, access to buprenorphine and residential addiction treatment facilities is limited. The dispensing of buprenorphine , a medication approved to treat opioid use disorder, is low among adolescents . Additionally, many residential addiction treatment facilities do not have availability for adolescents and are costly. These facilities often do not provide buprenorphine to adolescents with opioid use disorder.

If untreated, mental health conditions can persist into adulthood and limit quality of life. In 2021 and 2022, just over half of teens ( 55% ) reported discussing their mental or emotional health with their health care provider in the past year; and only 20% reported discussing transitions in their health care services that will go into effect when they turn 18.

What experiences among adolescents may negatively impact their mental health and well-being?

Many adolescents report negative experiences that can impact their mental health and well-being, including bullying (34%) and, specifically, electronic bullying (11%) (Figure 8). Higher shares of LGBT+ adolescents reported experiencing bullying (49%), and electronic bullying (23%), compared to their peers (28% and 8%, respectively). Bullying can increase the risk of mental health conditions, substance use, and self-harm. Electronic bullying may be associated with depression among youth and is more often experienced by female and sexual minority youth compared to their peers.

Adolescents are also spending more time on screens , including social media, which may lead to depression and poor well-being. Ninety-two percent of adolescents reported at least two hours of weekday screentime not associated with schoolwork. Emerging research has found that both smartphone use and social media use may be associated with poor well-being among youth, with a higher risk of depression for female adolescents. Social media use can also lead to difficulties with sleep and maintaining attention.

Many adolescents report adverse experiences which can lead to both mental and physical health concerns. In 2021 and 2022, 21% and 18% of adolescents reported living with a household member experiencing mental illness or substance use issues, respectively; 17% reported emotional abuse by a parent or adult in their household; 15% reported neighborhood violence; and 11% reported having a parent in jail or prison. Adverse childhood experiences are linked to mental illness, substance use, and chronic physical health problems in adolescence and can extend into adulthood. Social supports , including relationships with peers, can be a protective factor among adolescents in the face of adverse experiences. However, only 50% of adolescents reported having peer support “a lot of the time” in 2021 and 2022.

Gun violence continues to rise and may lead to negative mental health impacts among children and adolescents. An increasing number of children and adolescents have been exposed to gun violence in recent years. School shootings have increased and, beginning in 2020, firearms became the leading cause of death among children and teens ages 19 and below. Children and adolescents may experience negative mental health impacts, including symptoms of anxiety, in response to school shootings and gun-related injuries or deaths in their communities . Youth antidepressant use has also been shown to increase following exposures to fatal school shootings.

Looking Ahead

National efforts to address youth mental health concerns include recommendations for mental health screenings and strengthening social media safety protocols, and federal legislation to expand school-based mental health services. The U.S. Preventive Services Task Force put forth recommendations for youth anxiety and depression screenings and the U.S. Surgeon General also issued several advisories, including an advisory on youth mental health and social media that highlights potential solutions for strengthening social media safety protocols and encouraging digital and media literacy. Recently, the U.S. Senate Judiciary Subcommittee held a hearing on the impact of social media on youth mental health and well-being. In light of the increase in mental health-related pediatric ED visits, the American Academy of Pediatrics, the American College of Emergency Physicians and the Emergency Nurses Association released a statement including recommendations to improve care for mental health emergencies. Recent legislation allows for the expansion of school-based mental health care through a number of strategies, including growing the number of school-based mental health providers, leveraging Medicaid to further build out services, and providing trauma care to students.

At the state and local level, initiatives to improve access to youth mental health care include promoting school-based Medicaid behavioral health services and connecting youth to virtual care at no cost. State Medicaid programs have taken a variety of approaches to promote access to Medicaid behavioral health services provided in schools. These include working closely with local education agencies, taking advantage of the reversal of the free care policy, and increasing reimbursements for school-based providers. Local initiatives have also been proposed, including a partnership with New York City’s Department of Health and Mental Hygiene and the mental health app Talkspace to allow for teenagers to connect virtually with licensed therapists at no cost. However, the quality and clinical effectiveness of emerging mental health apps remains unclear . Looking ahead, data on adolescent populations will be pivotal in understanding how to further address and mitigate rising mental health and substance use concerns.

This work was supported in part by the Well Being Trust. KFF maintains full editorial control over all of its policy analysis, polling, and journalism activities.

The NHIS-Teen data does not disaggregate data on non-Hispanic adolescents by racial groups and, therefore, was not included in this analysis.

← Return to text

Drug overdose death distributions by race and ethnicity do not include non-Hispanic individuals of more than one race. KFF analysis of CDC WONDER. Accessed at: https://wonder.cdc.gov/mcd-icd10-provisional.html

KFF analysis of Centers for Disease Control and Prevention, National Center for Injury Prevention and Control. Web-based Injury Statistics Query and Reporting System (WISQARS). Accessed at: https://webappa.cdc.gov/sasweb/ncipc/leadcause.html

The 2021 YRBSS did not include questions on gender identity.

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Roughly 1 in 5 Adolescents Report Experiencing Symptoms of Anxiety or Depression

Also of Interest

The Implications of COVID-19 for Mental Health and Substance Use
The Impact of Gun Violence on Children and Adolescents
Mental Health and Substance Use State Fact Sheets

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Adolescent Motivational Interviewing

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Using Motivational Interviewing to Treat Adolescents and Young Adults with Substance Use Disorders

A case study of hazelden betty ford in plymouth.

Young people receiving treatment for substance use disorders (SUDs) present a unique clinical challenge. Though premature dropout from treatment happens with adults, as many as 50% of teens and young adults with substance use disorders do not complete treatment. In addition, many who do complete treatment do not fully engage with the treatment process (Gogel et al., 2011). Treatment engagement involves more than just being physically present; it involves actively taking part in all aspects of the treatment process and becoming emotionally invested in those processes as well as in peers attending the same services (Szapocznik et al., 2003; Wise et al., 2001). Another factor that may complicate treatment engagement is the fact that many adolescents enter treatment because of external pressures (such as parental insistence) and, as a result, may have low motivation to engage (Battjes et al., 2003). Because both retention and active engagement in treatment are associated with positive outcomes and recovery from substance use disorders (Williams and Chang, 2000; Moos & Moos, 2003; McWhirter, 2008), organizations offering treatment services to youth should focus on approaches that promote engagement and enhance the patient's intrinsic motivation and commitment to change.

The Hazelden Betty Ford Foundation has a facility in Plymouth, Minnesota , that focuses on providing substance abuse treatment to adolescents and young adults. In a recent interview with me, Dr. Joseph Lee, medical director of the Youth Continuum, stressed the importance of empathy in working with adolescent and young adult patients. A key piece of that work involves recognizing that empathy differs from identification. Empathy is the ability to imagine and accurately understand the feelings of another person and respond in a helpful way, and people with strong empathy can do this while maintaining a sense of being separate from that person (Buckman et al., 2011; Amsel, 2015). Identification, on the other hand, can be expressed as either relating to someone else so much that you lose a sense of yourself, or as identifying someone as so similar to yourself that you feel they must do and experience their situation as you do or did.

"We needed to take an honest look at how we were viewing and working with our patients," said Dr. Lee.

five principles of motivational interviewing infographic

"This clinical introspection was especially critical as we began to treat more patients awash in the opioid epidemic . These kids are even more likely to drop out than other kids, and for them, the risk of going back out and using drugs can be fatal." The realization that empathic rapport is critical to helping the patient get better, combined with too many patients leaving treatment prematurely, particularly those with a high degree of clinical severity, prompted Lee and other clinical leaders to improve clinical practice at the therapist level.

"These kids are even more likely to drop out than other kids, and for them, the risk of going back out and using drugs can be fatal."

The Hazelden Betty Ford Foundation had therefore identified an opportunity to strengthen their empathy in working with patients, along with addressing the urgent needs to keep young patients in treatment, increase their engagement in the treatment process and increase their motivation to change. The next step in the process was to decide on a therapeutic approach to meet these objectives. As applied to patients with substance use disorders, motivational interviewing (MI) is a brief psychotherapy aimed at increasing the patient's motivation and ability to change his/her addictive behaviors (Miller, Zweben, DiClemente, & Rychtarik, 1992). It focuses heavily on therapists bringing empathy to the therapeutic process with clients. Figure 1 lists the five elements of the approach, as outlined by Miller et al. (1992). The first element is expressing empathy for the client, which can be done in a number of ways. Empathic communication signals dignity and respect for the client and helps prevent the development of a superior/inferior relationship where the therapist is telling the client what he or she should be feeling. Empathic communication involves reflective listening, communicating an acceptance of where the client is and supporting them in the process of change (Miller & Rollnick, 1991). In addition to its strong focus on empathy, MI was chosen by Plymouth staff because it is an evidence-based practice in treating substance use disorders, with several studies indicating its effectiveness for adolescent and young adult populations (Barnett et al., 2012; Brown et al., 2015).

"Once we identified that we needed to start doing MI in a more formalized, consistent way across our clinicians, we needed to map out and implement a plan for doing it," Dr. Lee observed.

As one might imagine, this plan was fairly complex. Though all staff in patient-facing roles received training, the implementation of Motivational Interviewing was heavily concentrated on two roles: alcohol/drug addiction counselors and addiction technicians. Addiction counselors are a core part of the residential program. They administer assessments, participate in treatment planning and engage in therapy with the patient around his or her unique needs and challenges. The addiction technicians help support the patient, including easing their transition between the medical services unit and the residential treatment unit, helping them get to appointments on time and filling in for other non-clinical aspects of treatment, such as conducting meditation exercises.

Systematic training of staff in these two roles was a vital first step in implementing Motivational Interviewing with patients. Several tactics were used as part of training, including the use of an Motivational Interviewing text, required attendance at several two-day workshops and in-person training by both external MINT-certified specialists and several Plymouth staff well-versed in Motivational Interviewing methods, including Dr. Lee; Travis Vanderbilt, an LADC counselor; and David Wells, a PhD-level psychologist in the mental health clinic. Once counselors and technicians were trained, Lee and other Plymouth Motivational Interviewing experts set up a process to measure how counselors conducted therapy sessions with patients. The process involves periodically taping therapy sessions and auditing them for elements of Motivational Interviewing. The conclusions of these audits are then shared with each counselor in regular supervision meetings with his/ her manager. "The results of the audits and feedback on the clinician's use of Motivational Interviewing are a vital part of the process and happens on an ongoing basis," says Dr. Lee. "But we focus on making these conversations collegial and constructive as opposed to punitive…the idea is to model Motivational Interviewing even in the practitioner/supervisor discussions."

Successful implementation of Motivational Interviewing with Plymouth staff took several months, as is typically the case with clinical programs addressing behavioral health issues. By the middle of 2016, Motivational Interviewing was fully implemented and used consistently with all residential patients. Figure 2 shows atypical discharge rates for patients as a function of when they were discharged from the Plymouth residential program. These rates represent the percentage of patients who left treatment prematurely for various reasons (against staff advice, against medical advice, or occasionally at staff request). Over the last several quarters, the percentage of atypical discharges has been trending downward in a pattern consistent with the timeframe of motivational interviewing implementation. Only 9.9% of patients discharged in Q1 of 2017 left treatment prematurely, as opposed to 13.28% of patients in Q3 of 2015 (a 25% decrease). Though several other factors may have impacted these rates for example, an increase over time in the use of Suboxone for patients with opioid use issues the results are encouraging.

Qualitative feedback from staff members at Plymouth also suggests a positive impact of Motivational Interviewing on both staff and patients. Staff members described it as a "very person centered" approach, in part because it allows the clinician to effectively build rapport through empowerment rather than directives. Young patients are very receptive to the approach because they feel they are being worked with in a collaborative way, not talked down to or ordered to do certain things. Several staff members reported being able to help emotionally distressed patients change their mind about leaving treatment. In a couple of cases, the patient had left the facility, but the counselor was able to convince them to come back. Plymouth staff members directly attributed these outcomes to their use of Motivational Interviewing. "Motivational Interviewing is helping our patients because it reduces many of the impulsive decisions and encourages them to think through their actions before doing them," said one staff member. "It also helps them process through emotions they are not used to experiencing before making important decisions." Several counselors also reported that the therapeutic alliance formed with their patients has been strengthened through the use of Motivational Interviewing, which is quite important given the role of the alliance in predicting positive outcomes after treatment (Connors et al., 1997; Cook et al., 2015).

Behavioral health provider organizations wanting to implement evidence-based clinical practices in a highly accurate, reliable way can do so through an implementation science approach. At its core, implementation science involves the use of research and measurement to ensure that practices are implemented correctly within clinical settings (Proctor et al., 2009). The first step of the approach is to identify a practice that has a strong evidence base, meaning that it has been studied in a scientific manner and found to produce positive outcomes across studies. The second step involves mapping out how to deliver the clinical practice based on the organization's current structure, staffing models, clinical workflows and other processes related to care delivery. A key part of the second step is the training of staff directly administering the program or practice. Hazelden Betty Ford in Plymouth has completed these steps with regard to implementing motivational interviewing with residential patients. Clinical leaders and other staff will focus on subsequent steps over the coming months. This work will focus on evolving and standardizing the processes for measuring how effective each counselor is at implementing Motivational Interviewing with patients. Most importantly, counselors and supervisors will make sure that these assessments are used to continuously improve Motivational Interviewing practice.

"Motivational Interviewing is helping our patients because it reduces many of the impulsive decisions and encourages them to think through their actions before doing them."

This final step, though critical, is often overlooked by organizations implementing new clinical practices. It is one thing to implement something and occasionally measure how things are going. It is another thing to use what is learned and apply it back to care delivery on a continuous, long-term basis. As more behavioral health service providers use this model to bring evidence-based practices to patients, we can expect patient engagement and outcomes to improve.

Case Study October 2017. Download the Adolescent Motivational Interviewing case study .

Acknowledgements

Dr. joseph lee, medical director of the youth continuum.

Joseph Lee, MD, has extensive experience in addiction treatment for youth and families from across the country and abroad, providing him an unparalleled perspective on emerging drug trends, co-occurring mental health conditions and the ever-changing culture of addiction. A triple board certified physician, Lee completed his medical degree at the University of Oklahoma, his adult psychiatry residency at Duke University Hospital and his fellowship in child and adolescent psychiatry at John Hopkins Hospital. He is a diplomat of the American Board of Addiction Medicine and is a member of the American Academy of Child and Adolescent Psychiatry's Substance Abuse Committee. He is also the author of Recovering My Kid: Parenting Young Adults in Treatment and Beyond , which provides a candid, helpful guide for parents in times of crisis.

Amsel, B. (2015). Losing myself in your feelings: Empathy and identification. www.goodtherapy.org/blog/losing-my-self-inyour- feelings-empathy-and-identification-0925154 Barnett, E., Sussman, S., Smith, C., Rohrbach, L. A., & Spruijt-Metz, D. (2012). Motivational interviewing for adolescent substance use: A review of the literature. Addictive Behaviors, 37, 1325-1334.
Battjes, R. J., Gordon, M.S., O'Grady, K. E., Kinlock, T. W., & Carsell, M. A. (2003). Factors that predict adolescent motivation for substance abuse treatment. Journal of Substance Abuse Treatment, 24, 221-32.
Brown, R. A., Abrantes, A. M., Minami, H., Prince, M. A., Bloom, E. L., Apodaca, T. R. et al. (2015). Motivational interviewing to reduce substance use in adolescents with psychiatric comorbidity. Journal of Substance Abuse Treatment, 59, 20-29.
Buckman, R., Tulsky, J. A., & Rodin, G. (2011). Empathic responses in clinical practice: Intuition or tuition? Canadian Medical Association Journal, 183, 569-571. doi:10.1503/ cmaj.090113
Connors, G. J., Carroll, K. M., DiClemente, C. C., Longabaugh, R., & Donovan, D. M. (1997). The therapeutic alliance and its relationship to treatment participation and outcome. Journal of Consulting Clinical Psychology, 65, 588-598.
Cook, S., Heather, N., & McCambridge, J. (2015). The role of the working alliance in treatment for alcohol problems. Psychology of Addictive Behaviors, 29, 371-381.
Gogel, L. P., Cavaleri, M. A., Gardin, J. G. II & Wisdom, J. P. (2011). Retention and ongoing participation in residential substance abuse treatment: Perspectives from adolescents, parents and staff on the treatment process. Journal of Behavioral Health Services & Research, 38, 488-496.
Horvath, A. O., & Bedi, R. P. (2002). The alliance. In J. C. Norcross (Ed.), Psychotherapy relationships that work: Therapist contributions and responsiveness to patients (37-69). New York, NY: Oxford University Press.
McWhirter, P. T. (2008). Enhancing adolescent substance abuse treatment engagement. Journal of Psychoactive Drugs, 40, 173-182.
Miller, W. R., and Rollnick, S. (1991). Motivational interviewing: Preparing people to change addictive behavior. New York: Guilford Press.
Miller, W. R., Zweben, A., DiClemente, C. C., & Rychtarik, R. G. (1992). Motivational enhancement therapy manual: A clinical research guide for therapists treating individuals with alcohol abuse and dependence. National Institute on Alcohol Abuse and Alcoholism; Rockville, MD: NIAAA Project MATCH Monograph Series Volume 2, DHHS Publication No. (ADM) 92-1894.
Moos, R. H., & Moos, B. S. (2003). Long-term influence of duration and intensity of treatment on previously untreated individuals with alcohol use disorders. Addiction, 98, 325-338.
Proctor, E. K., Landsverk, J., Aarons, G., Chambers, D., Glisson, C., & Mittman, B. (2009). Implementation research in mental health services: An emerging science with conceptual, methodological, and training challenges. Administration and Policy in Mental Health, 36, 24-34. doi:10.1007/s10488-008-0197-4
Szapocznik, J., Perez-Vidal, A., & Brickman, A. L., et al. (1988). Engaging adolescent drug abusers and their families in treatment: A strategic structural systems approach. Journal of Consulting and Clinical Psychology, 56(4), 552-557.
Williams, R. J. & Chang, S. Y. (2000). A comprehensive and comparative review of adolescent substance abuse treatment outcome. Clinical Psychology: Science and Practice, 7, 138-166.
Wise, B. K., Cuffe, S. P., & Fischer, T. (2001). Dual diagnosis and successful participation of adolescents in substance abuse treatment. Journal of Substance Abuse Treatment, 21, 161-165.

Harnessing science, love and the wisdom of lived experience, we are a force of healing and hope for individuals, families and communities affected by substance use and mental health conditions.

Guides & Competencies
Try This:® Series
Geriatric Nursing Protocols
ConsultGeri
BHPC: Substance Use Disorders Case Study: Adolescent

By: Michelle Knapp, DNP, PMHNP-BC, FIAAN

Interactive Module

Target Audience

Interprofessionals
Registered Nurses

Description

This Behavioral Health in Primary Care (BHPC) case study aids in assessing and developing a care plan for an adolescent presenting with a substance use disorder in primary care.

About the Series

NURSING IMPROVING MENTAL HEALTH IN THE COMMUNITY is an NYU Meyers Initiative to maximize the potential of the nursing workforce as a vital part of the primary care team to address behavioral health concerns, including substance use and mental health, and mitigate social determinants that serve as barriers to good health.

This project is supported by the New York Community Trust.

Learning Outcomes

Apply your knowledge of the risks of adolescent substance use and utilize this knowledge in the assessment and treatment planning of patients.
Apply a non-judgemental perspective to engage patients, keeping communication open.
Identify substance use disorders and recognize the importance of providing early intervention as these disorders lie on a spectrum and develop over time.
Identify when to use assessment tools such as urine toxicology and NIDA screening in a therapeutic manner during the course of a patient’s treatment.

Michelle Knapp, DNP, PMHNP-BC, FIAAN

Director, Substance Use Sequence

Clinical Assistant Professor

NYU Rory Meyers College of Nursing

Connect with our team

[email protected]

Related Resources

Alcohol Use Screening and Assessment for Older Adults
The 2019 American Geriatrics Society Updated Beers Criteria® for Potentially Inappropriate Medication Use in Older Adults
Behavioral Health in Long-Term Care: Primary Care Providers
Behavioral Health in Long-Term Care: RNs/Interprofessionals
Behavioral Health in Primary Care (BHPC) Series
BHPC: Substance Use Disorders
BHPC: Substance Use Disorders Case Study: Adult
BHPC: Substance Use Disorders Case Study: Older Adult
Care of Older Adults in the Long-Term Care Setting (COA-LTC) Series
COA-LTC (RN-IP): Substance Misuse in Older Adults
Foundations Series: Special Populations of Older Adults

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The Fight Against Teen Vaping: A Pediatrician's Perspective

Substance Use Treatment Center (SUD) Case Study: 14 year old in IOP for alcohol use disorder and vaping

On this page:, patient information.

14 y/o Hispanic female attending IOP for alcohol use disorder

Moderate alcohol use disorder with binge drinking pattern, and depression
Past history of suicidal ideation
Admitted to hospital previously for severe concussion incurred during severe alcohol intoxication
Depression with SI-followed by psychiatrist and pediatrician

ROS (pertinent positives):

Patient reports tobacco use as whatever is available at the time
Parents report grades and mood change and social withdrawal
Parents unaware of tobacco use, aware of alcohol drinking

Other Info:

Heterosexual; pronouns: she/her

Screen for Tobacco Use With Every Youth Age 11+ at Every Clinical Encounter.

* Products used may vary between communities. Visit the Considerations for Clinicians page to view illustrations of common products.

Create a Space for Confidentiality & Trust

Case Study Sample Dialogue: Part 1 of 3

Clinician: Do your friends or family use tobacco or vaping products?

Patient: Yes, when we are partying.

Clinician: Have you ever tried tobacco products, like cigarettes, blunts, or dip?

Patient: Sometimes, but not really.

Clinician: What about e-cigarettes, vapes, or pod devices?

Patient: Yes, sometimes. On weekends at parties, and sometimes during the week.

Clinician: How many times a month do you use vapes at parties on the weekends?

Patient: 3 or 4 times a month.

Clinician: How often during the week do you typically use?

Patient: About 10 or 15 times-it helps clear my mind.

Counsel All Patients Who Use Tobacco About Quitting, Regardless of Amount or Frequency of Use

Case Study Dialogue: Part 2 of 3

Clinician: Thank you for sharing that with me. As your doctor, I care about you and I want to help you stay as healthy as possible. Because your brain is still developing, it's not safe for you to use any tobacco or nicotine product, including e-cigarettes. I want to help you stay as healthy as possible. Quitting smoking and vaping is an important way to keep you healthy. What do you think about quitting?

Patient: I don’t know. I don’t really vape that often. Mainly just when partying with friends.

Clinician: I think you will find that cutting out nicotine, even when just partying, will help with any alcohol cravings too. Quitting is hard, but I believe you can do it. Are you interested in quitting today?

Patient: Not really, but don’t I have to? I’m stuck here.

Link Youth to Appropriate Behavioral Supports

Sample Dialogue: Part 3 of 3

Clinician: Sometimes, I like to give my patients a 2-week challenge. For 2 weeks, I would ask you to completely stop your tobacco use and then we can talk about how you feel. Would you be willing to try that? Patient: Sure.

Clinician: Awesome! Sometimes I find when my patients agree to a 2-week challenge, they can use some support. There are resources I can offer -web, telephone, text. Most of these resources are built for people who are quitting, but I think the information is helpful for anyone who uses tobacco. Would being connected to a resource like that be of interest.

Patient: Ummmm....I guess so.

Clinician: Okay, if you take out your phone, I can show you a place that I think will be helpful. Go to a web browser, and type in teen.smokefree.gov -there is a lot of information there about withdrawal and stress and craving that I think will be helpful.

Patient: Okay thank you.

*Additional behavioral support options can be found at www.aap.org/help2quit

Cessation Support & Additional Follow Up

* Case Study 2 explores the use of NRT in tobacco users < 18 years old. **AAP Recommendations for NRT Prescription can be found at aap.org/NRT

Download Full Case Study View Full Youth Clinical Considerations

Additional case studies were developed to show the variations in clinical settings, common products, screening techniques, motivational interviewing, patient response, and clinician considerations. Please return to the home page to see additional case studies.

Last Updated

American Academy of Pediatrics

Open access
Published: 13 February 2023

Cognitive dysfunction in adolescents with substance use disorder

Ahmed Abdulaal 1 ,
Ashraf El Tantawy 1 ,
Omneya Ibrahim 1 ,
Hytham Elbadry 2 &
Haydy Hassan ORCID: orcid.org/0000-0003-2002-4942 1

Middle East Current Psychiatry volume 30 , Article number: 13 ( 2023 ) Cite this article

3948 Accesses

Metrics details

Substance abuse is a major health problem, associated with multiple clinical correlates. Cognitive dysfunctions were among the most relevant health problems associated with substance abuse among adolescents. The aim of the study is investigate the main cognitive domains affected in a sample of adolescents with substance use disorders. A case-control comparison was performed between 100 substance abusers versus 40 controls. The Mini-International Neuropsychiatric Interview v.5, Addiction Severity Index, Wisconsin Card Sorting Test, socioeconomic scale, and multiple historical variables investigated.

Substance abusers showed higher mean than control as regard all other WCST domains. The difference between two groups was statistically significant. Cannabis substance mostly affects early conceptualization and problem-solving abilities, while inhalants affect predominantly sustained attention, and alcohol mostly affect cognitive flexibility. Polysubstance use is more harmful to most of the executive function domain than mono substance use.

Conclusions

The substance use disorders are a major health problem accompanied cognitive dysfunction among adolescents and associated with increased rates of executive dysfunction. Cognitive flexibility, sustained attention, problem-solving abilities, and early conceptualization are the most domains affected.

Adolescence had a vulnerable developmental time where important changes take place in young cases’s bodies, brains, and sociocultural environments, perhaps making them more susceptible to substance use and psychiatric comorbidity [ 1 ]. According to numerous studies, the majority of individuals who eventually start using drugs or alcohol did it when they were still teenagers [ 2 ].

During youth, early adulthood, cases had most prone to start abusing substances, including alcohol, illegal narcotics, and prescription medicines. By the time they had been seniors, about 70% of high school kids would had tried alcohol, 50% would had used illegal drugs, and more than 20% would had used prescription medications for purposes other than those prescribed [ 3 ].

Biologically, the teenage years are a critical window of vulnerability to drug abuse, because the brain is still developing and malleable (a property known as neuroplasticity), and some brain areas are less mature than others. The parts of the brain that process feelings of reward and pain, crucial drivers of drug use, are the first to mature during childhood. What remains incompletely developed during the teen years are the PFC and its connections to other brain regions. PFC is responsible for assessing situations, making sound decisions, and controlling our emotions and impulses; typically, this circuitry is not mature until a person is in his or her mid-20s [ 4 ].

Substance abuse has a major impact on individuals, families, and communities as its effects are cumulative, contributing to costly social, physical, and mental health problems. Substance use often initiates during adolescence, and over 80% of drug users began using during adolescence [ 5 ].

The substance use disorders are a major health problem among youth, and it is more prevalent in male sex in Egyptian population. Tramadol dependency is at the top of all substances abused in Egypt, followed by polysubstances. Smoking, cannabis, and opiate are the most prevalent types [ 6 ]. Smoking, cannabis, and opiates are more frequent, and polysubstance use is much common worldwide [ 7 ].

Substance exposure during adolescence may adversely affect cognitive functioning, as measured by neuropsychological tests and functional neuroimaging tasks. Adolescent alcohol and marijuana users have poorer performance on working memory, verbal learning and memory, visuospatial functioning, and psychometric motor speed tasks, and poorer performance has been observed with higher doses and an earlier age of onset [ 8 , 9 ]. Moreover, marijuana users appear to suffer additional attentional deficits [ 10 ], and the cognitive deficits may be sustained in chronic users [ 11 ].

There had many different estimates that range from roughly 30–80% of cases with substance use disorders who had cognitive impairment. These deficiencies could be as mild as the brief effects of cannabis use or as severe as the moderate executive control deficits seen in long-term cocaine users, even after several months of abstinence. These estimates include the long-term visuospatial information processing abnormalities found in non-demented individuals with alcohol use disorders, even if they do not include alcoholics who had permanent deficits such Wernicke-Korsakoff syndrome [ 12 ].

Recent studies had found deficiencies in a variety of functions, including learning, memory, executive functioning, problem-solving, visuospatial, verbal ability, speed of information processing, connected to excessive alcohol, and other substance use problems [ 13 , 14 ]. Additionally, polysubstance use disorder had linked to widespread deficits, critical deficiencies on neuropsychological tests of working memory, inhibition, flexibility, self-regulation, and decision-making had been described [ 15 ].

This case-control study had carried out on 100 cases (as case group) who attended the Suez Canal University Hospital, Addiction Centers and Clinics in the Suez Canal region. They had been evaluated, after meeting inclusion criteria between October 2019 and December 2021, with 40 healthy control subjects (as control group) chosen from the healthy blood donors who had matched to the case group in terms of sociodemographic data.

The study included cases between the ages of 13 to 19 who had of either gender and had substance use disorders diagnosed in accordance with the International Classification of Diseases (ICD-10) criteria. Cases with neurodevelopmental disorders, epilepsy, severe head trauma, neurologic deficits, sensory deficits such as hearing or vision loss, low IQ, had intoxicated, or had undergone withdrawal had not been included.

Each participant in the study underwent the following:

A full thorough psychiatric sheet including sociodemographic information

Full physical and neurological testing to rule out organic or neurological comorbidities

A structured interview conducted by a psychiatrist using the Arabic version of the Mini-International Neuropsychiatric Interview v.5 (M.I.N.I. child) [ 16 , 17 ].

Testing for urine toxicity both during and after detoxification

Psychometric evaluation

Teen-addiction severity index

It had a structured, objective face-to-face interview that allowed the assessor to comment, including confidence ratings to show whether the information might be misinterpreted as well as severity ratings (indicating how severe the assessor believes had the need for treatment or counseling). Chemical usage, school performance, employment, support, family ties, peer, social interactions, legal status (involvement with criminal justice programme), mental health status, and a list of contacts for more information are among the issues that had been assessed [ 18 ].

It was translated into Arabic by official office. The Arabic draft was translated into English again.

The back-translated version was compared with the original English version to verify that the questions were properly translated. All of the back-translated items were worded similarly to the original ones. Under the direction of the research supervisors, a pilot study with a sample of adolescents had been conducted.

El-Gilany et al. socioeconomic’s scale

The occupation domain, education domain, home sanitation domain, family possessions domain, family domain, economic domain, and healthcare domain made comprised the socioeconomic rating in this study sample [ 19 ].

The Wisconsin Card Sorting Test

The test could be given to individuals between the ages of 6.5 and 89; it makes use of several abilities, such as attention, working memory, and visual processing. It had been used to assess a person’s proficiency in abstract reasoning as well as their capacity to switch up their problem-solving methods when necessary.

The WCST consists of 128 response cards, four stimulus cards with variously shaped shapes (crosses, circles, triangles, or stars), colours (red, blue, green, or yellow), and numbers on them (one, two, three, or four). Before the subject, on the table, are the four stimulus cards [ 20 ].

Following WCST scoring, the following domains had been offered:

The total number of trials conducted

Number of categories completed

Tries to successfully finish first category

Perseverative response

Error numbers

Non-perseverative errors and failure to maintain set

Statistical analysis

The statistical analysis of data was done by using Excel programme for figures, SPSS (SPSS, Inc., Chicago, IL, USA) program, and Statistical Package for Social Science version 16. To test the normality of data distribution, K-S (Kolmogorov-Smirnov) test had been done. Only critical data revealed to be nonparametric. N.B: All tested data revealed to be parametric. One-way analysis of variance (ANOVA) was used to explore group differences in age, years of education, and other variables when appropriate in subsequent analysis. Group differences regarding gender were examined using ×2 analysis.

Group differences on the subscale scores were examined using a multivariate analysis of covariance (MANCOVA), including years of education and economic subscales as covariates. Group differences on the WCST were analysed using univariate ANOVAs. In all these comparisons, we conducted post hoc one-way ANOVAs or Mann-Whitney tests to examine possible differences between subgroups of adolescents with substance use disorder classified according to their primary drug of choice. We conducted three multiple regression analyses to examine the predictive effects of severity of addiction (as measured by the teen addiction severity index subscales) on WCST scales scores. p -value had been critical if ≤ 0.05.

The study, which involved 100 adolescent substance users who attended the psychiatric clinic as previously described and 40 matched healthy adolescents who underwent comprehensive evaluations and psychometric assessments using the aforementioned methods, produced the following findings:

The difference between two groups had no statistical significance regarding father and mothers’ education, daily income, socioeconomic scale, residency, and religion as shown in Table 1 .

There is lower mean than control as regard number of category completed (reflect the overall efficiency in the test performance), while they showed higher mean than control as regard all other WCST domains. The difference between two groups had statistically significance ( p = 0.05) as shown in Table 2 .

The mono and polysubstance case groups had lower mean than control group as regard number of category completed and higher mean than control group as regard other WCST domains, and the difference between mono and polysubstance users’cases and control groups had statistically significance ( p = 0.05). Polysubstance use case group shows higher mean than mono substance use group as regard number of perseverative errors; the difference between two groups had statistically significance ( p = 0.003). Polysubstance use case groups show higher mean than mono substance use group as regard failure to maintain set, but the difference between two groups had no statistically significance as shown in Table 3 .

The combined cannabis use group had lower mean than non-cannabis use group and control group as regard all domains except failure to maintain set, and the difference had statistically significance ( p = 0.05) which means that cannabis use might affect the executive function domains except sustained attention as shown in Table 4 .

The mono alcohol use group showed a higher mean than control, non-alcohol use group as regard number of perseverative errors, and the difference had statistically significance ( p = 0.029) which means that alcohol use might affect flexibility domain critical. Mono alcohol use control groups showed a lower mean than combined alcohol use group and in non-alcohol user group as regard total number of errors; the difference had been statistically critical p = 0. 038. Mono, combined, and non-alcohol use groups show lower mean than control groups as regard number of category completed and higher mean than control as regard numbers of trial to first category, and the difference had statistically significance p = 0.05 as shown in Table 5 .

The combined opioid use group shows a higher mean than control group, non-opioid use group as regard number of perseverative response, total number of errors, and the difference had statistically significance ( p = 0.039) and ( p = 0.05), respectively, which means that opioid use impairs flexibility critical. Combined opioid and non-opioid use groups show a lower mean than control group as regard number of category completed, and the difference had statistically significance p = 0.043. Combined opioid and non-opioid use groups show a higher mean than control group as regard number of perseverative errors, failure to maintain set, and trials to first category, but it had no statistically significance as shown in Table 6 .

The combined benzodiazepine use group shows a lower mean than non-benzodiazepine use group as regard number of perseverative errors, and the difference had statistically significance p = 0.01. Combined benzodiazepine use groups show a lower mean than control group as regard number category completed, higher mean than control group as regard number of non-perseverative errors, trials to first category, and the difference had statistically significance p = 0.05 as shown in Table 7 .

The combined inhalant use group shows lower mean than control group, non-inhalant use group as regard number of category completed, and the difference had statistically significance p = 0.034. Combined inhalant use group shows higher mean than control, non-inhalants use groups as regard trial to first category, failure to maintain set, total number of errors, number of perseverative errors, number of perseverative responses, and the difference had statistically significance p = 0.05, which means that inhalant’s use impairs early conceptualization and sustained attention and flexibility domains. Combined inhalant use group shows higher mean than control and non-inhalants use groups as regard number of non-perseverative errors, but the difference had no statistically significance as shown in Table 8 .

The study, conducted in 2021 at the Faculty of Medicine Suez Canal University Hospital, the psychiatric hospitals, Clinics of the Suez Canal regional area, examined the relationship between substance use and executive function in a sample of adolescents who had been mostly from the same cultural background and who did not had developmental or psychological delays. Toxicology screening, semi-structured interviews (mini-kid), the Teen Addiction Severity Index scale, the WCST, and a social classification scale had all been performed on the 140 cases we evaluated, who had split into two groups of 100 cases and 40 controls.

In this investigation, there had no discernible difference in the sociodemographic characteristics of the cases and the control group.

In all WCST domains, our investigation discovered a substantial difference between the case and control groups. On the basis of our findings, the majority of research conducted to evaluate this issue, including those conducted in Spain [ 21 , 22 , 23 ], the USA [ 24 ], 2011, and Brazil [ 25 ], reached to the same conclusion: there had been a global decline in executive function related to substance use. Adolescent substance use might had a negative impact on performance as determined by neuropsychological testing and functional neuroimaging activities [ 8 ].

This conclusion might be explained by the fact that altered function, including changes in the well-known “executive” domains of attention, inhibition/regulation, working memory, and decision-making, had a defining characteristic of substance use disorders. It had been acknowledged that a fundamental impairment in addiction, a potentially critical target for intervention, had poor (sometimes referred to as “top down”) regulation of downstream motivational processes, whether appetitive (reward, incentive salience) or aversive (stress, negative affect) [ 26 ].

In this study, polydrug use impaired executive functions in the majority of domains more than mono substance use, which had been consistent with research from Meyerhoff [ 27 ], Gustavson [ 28 ], Schmidt et al. [ 29 ], Formiga et al. (2001), and studies from San Francisco, Brazil [ 30 ], while this could be explained biologically and socially, it had been challenging to compare or assess substance use patterns across cases with various backgrounds.

Due to the short sample size, lack of cases who only used cannabis at the time of the trial, there had been no cases who only used cannabis. Cannabis use critically affects all executive function domains, as demonstrated by studies conducted in the Netherlands by Jager et al. [ 31 ], Boston by Dahlgren et al. [ 32 ], and in Israel by Cohen K., Weinstein [ 33 ]; more specifically, it critical affects adolescents’ working memory and flexibility, as demonstrated by Morinet al [ 34 ].. Block et al. [ 35 ] also found this in his study using a sample of Europeans and Americans, respectively. This conclusion had backed by convergent evidence from structural neuroimaging studies, which show that regular cannabis use had linked to neuronal changes in a number of areas of the brain that had been important for working memory, including a shrinkage of the hippocampus and amygdala. These changes also had a relationship with the quantity of cannabis use dependence [ 36 ].

The flexibility and initial conceptualization of the adolescent cases had been critically impacted by alcohol use in our study, which had been consistent with Sanhueza et al. [ 37 ]’s findings in a related study conducted in Madrid. Powell et al. [ 38 ]’s mega study, however, found that alcohol had an impact on every domain of the executive functions; this might be explained by the smaller sample size. Numerous studies revealed that heavy drinkers had lower cerebellar activity in response to a reward processing test (wheel of fortune), frontal, parietal brain activation in response to inhibition, and working memory tasks [ 39 , 40 ].

In line with research conducted in Egypt by Bassiony et al. [ 41 ] and Tehran by Rezapour et al. [ 42 ], opioid usage severely impacted executive functioning in our study, particularly flexibility [ 42 ]. Although other studies conducted in China by Li et al. [ 43 ], Germany by Brand et al. [ 44 ], Florida by Valdes, Lunsford [ 45 ], Finland by Rapeli et al. [ 46 ], and Finland by Brand et al. [ 44 ] did not arrive at the same precise results, this had likely because they used different psychometric tools for evaluating the executive functions. This had been explained by research that shows how opiates cause atrophy, apoptosis by upregulating GDNF, downregulating apoptotic markers including Caspase 3 and 8, and inducing pro-inflammatory indicators.

Additionally, it causes prefrontal cortex neuronal loss, microgliosis, and astrogliosis. Other negative effects of opiate use include behavioural disruption and impairment. Overall, opiate use led to the activation of the neuroinflammatory response, which in turn caused neurodegeneration in the prefrontal cortex [ 47 ].

In line with research conducted in Italy by Federico et al., benzos usage in this study adversely impacted executive functioning, particularly flexibility and problem-solving skills [ 48 ]. Despite the fact that studies conducted in Bangladesh by Chowdhury et al. [ 49 ] and Mexico by Contreras-González et al. [ 49 ], it indicated that benzodiazepines influence all domains of executive function.

This study found that inhalant use considerably affected executive functions, which had been consistent with research from studies conducted in Australia, the USA, Turkey, and Mexico. These investigations found that inhalant use critical affected all domains of executive function. Recent research on the executive functioning of inhalant users had found impairments in information processing speed, self-monitoring, visual, motor speed, working memory, psychomotor function, and spatial problem-solving [ 50 , 51 ].

Adolescents’ substance use problems had a serious health issue that had also accompanied by impairment. Cannabis had the greatest impact on early conceptualization and problem-solving skills, whereas alcohol and inhalants mostly impair flexibility and sustained attention, respectively. Contrary to mono substance use, polysubstance use had been more detrimental to the majority of the executive function area.

Availability of data and materials

Available data and material.

Abbreviations

International Classification of Diseases

The Mini-International Neuropsychiatric Interview v.5

Storr CL, Pacek LR, Martins SS (2012) Substance use disorders, adolescent psychopathology. Public Health Rev 34(2):1–42

Article Google Scholar

Gutierrez A, Sher L (2015) Alcohol, drug use among adolescents: an educational overview. Int J Adolesc Med Health 27(2):207–212

Terry-McElrath YM, O’Malley PM, Johnston LD (2013) Simultaneous alcohol, marijuana use among US high school seniors from 1976 to 2011: trends, reasons, situations. Drug Alcohol Depend 133(1):71–79

Robertson EB, David SL, Rao SA (2003) Preventing drug use among children and adolescents: a research-based guide for parents, educators, and community leaders, 2nd ed. NIH Pub. No. 04-4212(A). National Institute on Drug Abuse, Bethesda Available at: http://www.drugabuse.gov/pdf/prevention/RedBook.pdf

Google Scholar

Das JK, Salam RA, Arshad A, Finkelstein Y, Zulfiqar A (2016) Bhutta. Interventions for adolescent substance abuse: an overview of systematic reviews. J Adolesc Health 59(4 Suppl):S61–S75

El-Awady SA, Elsheshtawy EA, Elbahaey WA, Elboraie OA (2017) Impact of familial risk factors on the severity of addiction in a sample of Egyptian adolescents. Egypt J Psychiatr 38(2):70–78

Jessica A, Kims. (2019) Grisworld. Adolescent substance use and misuse: recognition and management. Am Fam Physician 99(11):689–696

Nguyen-Louie TT, Castro N, Matt GE, Squeglia LM, Brumback T, Tapert SF (2015) Effects of emerging alcohol and marijuana use behaviors on adolescents' neuropsychological functioning over four years. J Stud Alcohol Drugs 76:738–748

Ganzer F, Broning S, Kraft S, Sack PM, Thomasius R (2016) Weighing the evidence: a systematic review on long-term neurocognitive effects of cannabis use in abstinent adolescents and adults. Neuropsychol Rev 26:186–222

Randolph K, Turull P, Margolis A, Tau G (2013) Cannabis and cognitive systems in adolescents. Adolesc Psychiatry 3(2):135–147 Ref Type: Journal (Full)

Renard J, Krebs MO, Le PG, Jay TM (2014) Long-term consequences of adolescent cannabinoid exposure in adult psychopathology. Front Neurosci 8:361

Copersino ML, Fals-Stewart W, Fitzmaurice G, Schretlen DJ, Sokoloff J, Weiss RD (2009) Rapid screening of cases with substance use disorders. Exp Clin Psychopharmacol 17(5):337

Gonzalez R (2007) Acute, non-acute effects of cannabis on brain functioning, neuropsychological performance. Neuropsychol Rev 17(3):347–361

Gruber SA, Silveri MM, Yurgelun-Todd DA (2007) Neuropsychological consequences of opiate use. Neuropsychol Rev 17(3):299–315

Moreno-López L, Stamatakis EA, Fernández-Serrano MJ, Gómez-Rio M, Rodríguez-Fernández A, Pérez-García M, Verdejo-García A (2012) Neural correlates of hot, cold executive functions in polysubstance addiction: association between neuropsychological performance, resting brain metabolism as measured by positron emission tomography. Psychiatry Res Neuroimaging 203(2-3):214–221

Sheehan DV, Lecrubier Y, Sheehan KH, Amorim P, Janavs J, Weiller E, Hergueta T, Baker R, Dunbar GC (1998) The Mini-International Neuropsychiatric Interview (MINI): the development, validation of a structured diagnostic psychiatric interview for DSM-IV, ICD-10. J Clin Psychiatry 59(20):22–33

Ghanem M, Ibrahim M, Elbehery A, Elmerghany H (2000) The translation group of the Arabic version of the Mini International Neuropsychiatric Interview for Children (MINI-Kid) by Sheehan DV et al, 1998. Department of Neuropsychiatry Ain Shams University

Kaminer Y, Wagner E, Plummer B, Seifer R (1993) Validation of the teen addiction severity index (T-ASI): preliminary findings. Am J Addict 2(3):250–254

El-Gilany A, El-Wehady A, El-Wasify M (2012) Updating and validation of the socioeconomic status scale for health research in Egypt. East Mediterr Health J 18(9)

Romine CB, Lee D, Wolfe ME, Homack S, George C, Riccio CA (2004) Wisconsin Card Sorting Test with children: a meta-analytic study of sensitivity, specificity. Arch Clin Neuropsychol 19(8):1027–1041

Verdejo-Garcia AJ, López-Torrecillas F, Aguilar de Arcos F, Perez-Garcia M (2005) Differential effects of MDMA, cocaine, cannabis use severity on distinctive components of the executive functions in polysubstance users: a multiple regression analysis. Addict Behav 30(1):89–101

Verdejo-García A, Rivas-Pérez C, López-Torrecillas F, Pérez-García M (2006) Differential impact of severity of drug use on frontal behavioral symptoms. Addict Behav 31(8):1373–1382

Fernández-Serrano MJ, Pérez-García M, Schmidt Río-Valle J, Verdejo-García A (2009) Neuropsychological consequences of alcohol, drug abuse on different components of executive functions. J Psychopharmacol 24(9):1317–1332

Crean RD, Crane NA, Mason BJ (2011) An evidence based review of acute, long-term effects of cannabis use on executive functions. J Addict Med 5(1):1–8

Formiga MB, Galdino MK, Vasconcelos SC, Neves JW, Lima MD (2021) Executive functions and emotion regulation in substance use disorder. J Bras Psiquiatr 70:236

Tatiana RS, Regier PS (2018) Cognitive impairment in substance use disorders. CNS Spectrums 1–12

Meyerhoff DJ (2016) Multimodal Magnetic Resonance Neuroimaging and Cognition in Polysubstance Users. InNeuropathology of Drug Addictions and Substance Misuse (pp. 872-883). Academic Press.

Gustavson DE, Stallings MC, Corley RP, Miyake A, Hewitt JK, Friedman NP (2017) Executive functions, substance use: relations in late adolescence, early adulthood. J Abnorm Psychol 126(2):257

Schmidt TP, Pennington DL, Cardoos SL, Durazzo TC, Meyerhoff DJ (2017) Neurocognition, inhibitory control in polysubstance use disorders: comparison with alcohol use disorders, changes with abstinence. J Clin Exp Neuropsychol 39(1):22–34

Formiga MB, Galdino MK, Vasconcelos SC, Neves JW, Lima MD (2021) Executive functions, emotion regulation in substance use disorder. J Brasileiro de Psiquiatria 70:236

Jager G, Kahn RS, Van Den Brink W, Van Ree JM, Ramsey NF (2006) Long-term effects of frequent cannabis use on working memory, attention: an fMRI study. Psychopharmacology 185(3):358–368

Article CAS Google Scholar

Dahlgren MK, Sagar KA, Racine MT, Dreman MW, Gruber SA (2016) Marijuana use predicts performance on tasks of executive function. J Stud Alcohol Drugs 77(2):298–308

Cohen K, Weinstein A (2018) The effects of cannabinoids on executive functions: evidence from cannabis, synthetic cannabinoids—a systematic review. Brain Sci 8(3):40

Morin JF, Afzali MH, Bourque J, Stewart SH, Séguin JR, O’Leary-Barrett M, Conrod PJ (2019) A population-based analysis of the relationship between substance use, adolescent development. Am J Psychiatr 176(2):98–106

Block RI, Jager G, Luijten M, Ramsey NF (2022) Associations of regular marijuana use by adolescent boys with verbal memory, perseveration. Psychol Rep 125(2):839–861

Cousijn J, Wiers RW, Ridderinkhof KR, van den Brink W, Veltman DJ, Goudriaan AE (2012) Grey matter alterations associated with cannabis use: results of a VBM study in heavy cannabis users, healthy controls. Neuroimage. 59:3845–3851

Sanhueza C, García-Moreno LM, Expósito J (2011) Weekend alcoholism in youth, neuroaging. Psicothema. 31:209–214

Powell A, Sumnall H, Kullu C, Owens L, Montgomery C (2021) Subjective executive function deficits in hazardous alcohol drinkers. J Psychopharmacol 35(11):1375–1385

Squeglia LM, Pulido C, Wetherill RR, Jacobus J, Brown GG, Tapert SF (2012) Brain response to working memory over three years of adolescence: influence of initiating heavy drinking. J Stud Alcohol Drugs 73:749–760

Cservenka A, Jones SA, Nagel BJ (2015) Reduced cerebellar brain activity during reward processing in adolescent binge drinkers. Dev Cogn Neurosci 16:110–120

Bassiony MM, Youssef UM, Hassan MS, El-Deen GM, El-Gohari H, Abdelghani M, Abdalla A, Ibrahim DH (2017) Impairment, tramadol dependence. J Clin Psychopharmacol 37(1):61–66

Rezapour T, Hatami J, Farhoudian A, Noroozi A, Daneshmand R, Sofuoglu M, Baldacchino A, Ekhtiari H (2021) Baseline executive functions, receiving rehabilitation could predict treatment response in cases with opioid use disorder. J Subst Abus Treat 131:108558

Li J, Weidacker K, Mandali A, Zhang Y, Whiteford S, Ren Q, Zhou Z, Zhou H, Jiang H, Du J, Zhang C (2021) Impulsivity, craving in subjects with opioid use disorder on methadone maintenance treatment. Drug Alcohol Depend 219:108483

Brand M, Roth-Bauer M, Driessen M, Markowitsch HJ (2008) Executive functions, risky decision-making in cases with opiate dependence. Drug Alcohol Depend 97(1-2):64–72

Valdes K, Lunsford D (2021) Executive functioning of individuals with substance use disorder. Ann Int Occup Ther 4(4):e220–e227

Rapeli P, Kivisaari R, Autti T, Kähkönen S, Puuskari V, Jokela O, Kalska H (2006) Cognitive function during early abstinence from opioid dependence: a comparison to age, gender, and verbal intelligence matched controls. Bmc Psychiatry 6(1):1–0

Aghajanpour F, Boroujeni ME, Jahanian A, Soltani R, Ezi S, Khatmi A, Mohammad-Amin et al (2020) Tramadol: a potential neurotoxic agent affecting prefrontal cortices in adult Male Rats, PC-12 Cell Line. Neurotox Res 38:385–397

Federico A, Tamburin S, Maier A, Faccini M, Casari R, Morbioli L, Lugoboni F (2017) Multifocal dysfunction in high-dose benzodiazepine users: a cross-sectional study. Neurol Sci 38(1):137–142

Chowdhury ZS, Morshed MM, Shahriar M, Bhuiyan MA, Islam SM, Bin Sayeed MS (2016) The effect of chronic alprazolam intake on memory, attention, and psychomotor performance in healthy human male volunteers. Behav Neurol 2016

Marín-Navarrete R, Toledo-Fernández A, Villalobos-Gallegos L, Pérez-López A, Medina-Mora ME (2018) Neuropsychiatric characterization of individuals with inhalant use disorder, polysubstance use according to latent profiles of executive functioning. Drug Alcohol Depend 190:104–111

Dingwall KM, Maruff P, Fredrickson A, Cairney S (2011) Recovery during, after treatment for volatile solvent abuse. e. Drug Alcohol Depend 118(2-3):180–185

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Ahmed Abdulaal, Ashraf El Tantawy, Omneya Ibrahim & Haydy Hassan

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Study conception and design, AA, OY, AT, and HH. Data collection, HH, AA, and HE. Data analysis and interpretation, HH, AA, OY, and HE. Drafting of the article, AA, AT, and HH. Critical revision of the article, AA, AT, OY, and HH. The authors read and approved the final manuscript.

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Abdulaal, A., El Tantawy, A., Ibrahim, O. et al. Cognitive dysfunction in adolescents with substance use disorder. Middle East Curr Psychiatry 30 , 13 (2023). https://doi.org/10.1186/s43045-023-00291-8

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Cognitive dysfunction
Substance use disorder
Adolescents
Wisconsin Card

Module 9: Substance-Related and Addictive Disorders

Case studies: substance-abuse disorders, learning objectives.

Identify substance abuse disorders in case studies

Case Study: Benny

The following story comes from Benny, a 28-year-old living in the Metro Detroit area, USA. Read through the interview as he recounts his experiences dealing with addiction and recovery.

Q : How long have you been in recovery?

Benny : I have been in recovery for nine years. My sobriety date is April 21, 2010.

Q: What can you tell us about the last months/years of your drinking before you gave up?

Benny : To sum it up, it was a living hell. Every day I would wake up and promise myself I would not drink that day and by the evening I was intoxicated once again. I was a hardcore drug user and excessively taking ADHD medication such as Adderall, Vyvance, and Ritalin. I would abuse pills throughout the day and take sedatives at night, whether it was alcohol or a benzodiazepine. During the last month of my drinking, I was detached from reality, friends, and family, but also myself. I was isolated in my dark, cold, dorm room and suffered from extreme paranoia for weeks. I gave up going to school and the only person I was in contact with was my drug dealer.

Q : What was the final straw that led you to get sober?

Benny : I had been to drug rehab before and always relapsed afterwards. There were many situations that I can consider the final straw that led me to sobriety. However, the most notable was on an overcast, chilly October day. I was on an Adderall bender. I didn’t rest or sleep for five days. One morning I took a handful of Adderall in an effort to take the pain of addiction away. I knew it wouldn’t, but I was seeking any sort of relief. The damage this dosage caused to my brain led to a drug-induced psychosis. I was having small hallucinations here and there from the chemicals and a lack of sleep, but this time was different. I was in my own reality and my heart was racing. I had an awful reaction. The hallucinations got so real and my heart rate was beyond thumping. That day I ended up in the psych ward with very little recollection of how I ended up there. I had never been so afraid in my life. I could have died and that was enough for me to want to change.

Q : How was it for you in the early days? What was most difficult?

Benny : I had a different experience than most do in early sobriety. I was stuck in a drug-induced psychosis for the first four months of sobriety. My life was consumed by Alcoholics Anonymous meetings every day and sometimes two a day. I found guidance, friendship, and strength through these meetings. To say early sobriety was fun and easy would be a lie. However, I did learn it was possible to live a life without the use of drugs and alcohol. I also learned how to have fun once again. The most difficult part about early sobriety was dealing with my emotions. Since I started using drugs and alcohol that is what I used to deal with my emotions. If I was happy I used, if I was sad I used, if I was anxious I used, and if I couldn’t handle a situation I used. Now that the drinking and drugs were out of my life, I had to find new ways to cope with my emotions. It was also very hard leaving my old friends in the past.

Q : What reaction did you get from family and friends when you started getting sober?

Benny : My family and close friends were very supportive of me while getting sober. Everyone close to me knew I had a problem and were more than grateful when I started recovery. At first they were very skeptical because of my history of relapsing after treatment. But once they realized I was serious this time around, I received nothing but loving support from everyone close to me. My mother was especially helpful as she stopped enabling my behavior and sought help through Alcoholics Anonymous. I have amazing relationships with everyone close to me in my life today.

Q : Have you ever experienced a relapse?

Benny : I experienced many relapses before actually surrendering. I was constantly in trouble as a teenager and tried quitting many times on my own. This always resulted in me going back to the drugs or alcohol. My first experience with trying to become sober, I was 15 years old. I failed and did not get sober until I was 19. Each time I relapsed my addiction got worse and worse. Each time I gave away my sobriety, the alcohol refunded my misery.

Q : How long did it take for things to start to calm down for you emotionally and physically?

Benny : Getting over the physical pain was less of a challenge. It only lasted a few weeks. The emotional pain took a long time to heal from. It wasn’t until at least six months into my sobriety that my emotions calmed down. I was so used to being numb all the time that when I was confronted by my emotions, I often freaked out and didn’t know how to handle it. However, after working through the 12 steps of AA, I quickly learned how to deal with my emotions without the aid of drugs or alcohol.

Q : How hard was it getting used to socializing sober?

Benny : It was very hard in the beginning. I had very low self-esteem and had an extremely hard time looking anyone in the eyes. But after practice, building up my self-esteem and going to AA meetings, I quickly learned how to socialize. I have always been a social person, so after building some confidence I had no issue at all. I went back to school right after I left drug rehab and got a degree in communications. Upon taking many communication classes, I became very comfortable socializing in any situation.

Q : Was there anything surprising that you learned about yourself when you stopped drinking?

Benny : There are surprises all the time. At first it was simple things, such as the ability to make people smile. Simple gifts in life such as cracking a joke to make someone laugh when they are having a bad day. I was surprised at the fact that people actually liked me when I wasn’t intoxicated. I used to think people only liked being around me because I was the life of the party or someone they could go to and score drugs from. But after gaining experience in sobriety, I learned that people actually enjoyed my company and I wasn’t the “prick” I thought I was. The most surprising thing I learned about myself is that I can do anything as long as I am sober and I have sufficient reason to do it.

Q : How did your life change?

Benny : I could write a book to fully answer this question. My life is 100 times different than it was nine years ago. I went from being a lonely drug addict with virtually no goals, no aspirations, no friends, and no family to a productive member of society. When I was using drugs, I honestly didn’t think I would make it past the age of 21. Now, I am 28, working a dream job sharing my experience to inspire others, and constantly growing. Nine years ago I was a hopeless, miserable human being. Now, I consider myself an inspiration to others who are struggling with addiction.

Q : What are the main benefits that emerged for you from getting sober?

Benny : There are so many benefits of being sober. The most important one is the fact that no matter what happens, I am experiencing everything with a clear mind. I live every day to the fullest and understand that every day I am sober is a miracle. The benefits of sobriety are endless. People respect me today and can count on me today. I grew up in sobriety and learned a level of maturity that I would have never experienced while using. I don’t have to rely on anyone or anything to make me happy. One of the greatest benefits from sobriety is that I no longer live in fear.

Case Study: Lorrie

Lorrie, image of a smiling woman wearing glasses.

Figure 1. Lorrie.

Lorrie Wiley grew up in a neighborhood on the west side of Baltimore, surrounded by family and friends struggling with drug issues. She started using marijuana and “popping pills” at the age of 13, and within the following decade, someone introduced her to cocaine and heroin. She lived with family and occasional boyfriends, and as she puts it, “I had no real home or belongings of my own.”

Before the age of 30, she was trying to survive as a heroin addict. She roamed from job to job, using whatever money she made to buy drugs. She occasionally tried support groups, but they did not work for her. By the time she was in her mid-forties, she was severely depressed and felt trapped and hopeless. “I was really tired.” About that time, she fell in love with a man who also struggled with drugs.

They both knew they needed help, but weren’t sure what to do. Her boyfriend was a military veteran so he courageously sought help with the VA. It was a stroke of luck that then connected Lorrie to friends who showed her an ad in the city paper, highlighting a research study at the National Institute of Drug Abuse (NIDA), part of the National Institutes of Health (NIH.) Lorrie made the call, visited the treatment intake center adjacent to the Johns Hopkins Bayview Medical Center, and qualified for the study.

“On the first day, they gave me some medication. I went home and did what addicts do—I tried to find a bag of heroin. I took it, but felt no effect.” The medication had stopped her from feeling it. “I thought—well that was a waste of money.” Lorrie says she has never taken another drug since. Drug treatment, of course is not quite that simple, but for Lorrie, the medication helped her resist drugs during a nine-month treatment cycle that included weekly counseling as well as small cash incentives for clean urine samples.

To help with heroin cravings, every day Lorrie was given the medication buprenorphine in addition to a new drug. The experimental part of the study was to test if a medication called clonidine, sometimes prescribed to help withdrawal symptoms, would also help prevent stress-induced relapse. Half of the patients received daily buprenorphine plus daily clonidine, and half received daily buprenorphine plus a daily placebo. To this day, Lorrie does not know which one she received, but she is deeply grateful that her involvement in the study worked for her.

The study results? Clonidine worked as the NIDA investigators had hoped.

“Before I was clean, I was so uncertain of myself and I was always depressed about things. Now I am confident in life, I speak my opinion, and I am productive. I cry tears of joy, not tears of sadness,” she says. Lorrie is now eight years drug free. And her boyfriend? His treatment at the VA was also effective, and they are now married. “I now feel joy at little things, like spending time with my husband or my niece, or I look around and see that I have my own apartment, my own car, even my own pots and pans. Sounds silly, but I never thought that would be possible. I feel so happy and so blessed, thanks to the wonderful research team at NIDA.”

Liquor store. Authored by : Fletcher6. Located at : https://commons.wikimedia.org/wiki/File:The_Bunghole_Liquor_Store.jpg . License : CC BY-SA: Attribution-ShareAlike
Benny Story. Provided by : Living Sober. Located at : https://livingsober.org.nz/sober-story-benny/ . License : CC BY: Attribution
One patientu2019s story: NIDA clinical trials bring a new life to a woman struggling with opioid addiction. Provided by : NIH. Located at : https://www.drugabuse.gov/drug-topics/treatment/one-patients-story-nida-clinical-trials-bring-new-life-to-woman-struggling-opioid-addiction . License : Public Domain: No Known Copyright

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Peer and Community Influences on Adolescent Substance Use in the Context of Adverse Childhood Experiences

Haley stritzel.

1 The University of Texas at Austin, Austin, TX, USA

Both adverse childhood experiences (ACEs) and peer influences consistently predict early tobacco, alcohol, and illicit drug use. However, less research considers how peer and community influences contribute to or modify the association between ACEs and early substance use. This study addresses these gaps in the literature by analyzing multilevel, longitudinal data from the Project on Human Development in Chicago Neighborhoods (PHDCN; N = 1,912). Unstructured socializing and peer substance use largely explained the association between ACEs and drinking, smoking cigarettes, and illicit drug use in the past month. A history of ACEs magnified the association between peer substance use and the number of cigarettes smoked. Collective efficacy also shaped the associations between peer influences, ACEs, and substance use, but in different ways depending on the substance use outcome analyzed.

Introduction and Theoretical Background

Adverse childhood experiences (ACEs) are consistently associated with a wide range of poor mental ( Chapman et al. 2004 ; Dube et al. 2001 ; Edwards et al. 2003 ) and physical health outcomes ( Anda et al. 2008 ; Danese and McEwen 2012 ; Dong et al. 2003 ) across the life course. The definition of “adverse child experiences” typically encompasses exposure to psychological, physical, and sexual abuse as well as several dimensions of household stressors, including living with someone with a mental illness or substance use disorder, having a household member incarcerated, and witnessing violence toward one’s mother before age 18 ( Felitti et al. 1998 ). One mechanism through which ACEs affect later health is the development of problematic health behaviors such as smoking, drinking alcohol, and illicit drug use ( Hostinar et al. 2015 ). In particular, ACEs are associated with an earlier onset of substance use during adolescence ( Anda et al. 1999 ; Dube et al. 2003 ; Dube et al. 2006 ; Rothman et al. 2008 ), a critical period in which initiation of long-term use often occurs ( Chassin et al. 2004 ). Prior research clearly demonstrates the consequences of peer and community contexts for adolescents’ substance use, but most studies linking ACEs to youth’s substance use conceptualize these behaviors as an individualistic coping mechanism in response to adversity or as an expression of a genetic influence. Few studies address how the combination of peer influences, neighborhood context, and a history of ACEs shape youth’s substance use. Using multilevel data from the Project on Human Development in Chicago Neighborhoods (PHDCN), this study addresses (1) the extent to which peer influences explain the association between ACEs and substance use, (2) whether youth with a history of ACEs are more or less vulnerable to peer influences, and (3) how neighborhood context further shapes this vulnerability.

Mediating Role of Peer Influences in Substance Use

Given that peer influence is one of the strongest and most consistent predictors of adolescent substance use ( Kobus 2003 ; Wills and Cleary 1999 ), it is plausible that the association between ACEs and substance use operates in part through peer settings. Two dimensions of peer influences commonly studied are the direct normative influence of peers who engage in substance use and the opportunities for substance use that peer groups provide. In particular, spending time with friends without a structured activity and in the absence of authority figures, that is, unstructured socializing, increases the likelihood of substance use independently of the peers’ own substance use ( Osgood et al. 1996 ). Prior research supports both influence-based and opportunity-based theories of the mechanisms linking peers to adolescent substance use ( Haynie and Osgood 2005 ). ACEs may increase the likelihood that youth spend time in such settings for several reasons.

First and most simply, youth tend to seek out friends with similar characteristics and experiences ( Brechwald and Prinstein 2011 ; Poulin and Boivin 2000 ). Youth who have more ACEs tend to smoke, drink, and use drugs more on average. They may then select peers who also engage in these behaviors ( Ennett and Bauman 1994 ; B. R. Hoffman et al. 2007 ). In addition, they may bond with friends with similar adverse experiences who also turn to substance use as a coping mechanism. Either way, greater exposure to friends who use substances can increase the opportunities and enhance the social rewards of substance use. In addition, adolescents with more ACEs express greater admiration and desire to imitate antisocial peers ( Perez, Jennings, and Baglivio 2018 ).

Second, children growing up in unstable household environments are more likely to experience school and neighborhood mobility, exposing youth to new, potentially delinquent, peer networks ( Fomby and Sennott 2013 ). Adolescents with more ACEs may not only be more likely to select substance-using peers, but also may spend more unstructured time with their friends. Unstructured socializing without parental monitoring, such as going to parties, in turn is associated with greater substance use.

Third, adolescents with more ACEs by definition experience greater levels of household stress and dysfunction, reflecting greater strain in the parent-child relationship. Greater family instability ( Cavanagh et al. 2018 ), lower levels of relationship warmth ( Hair et al. 2008 ), and less disclosure from the adolescent ( Stattin and Kerr 2000 ) constrain parents’ ability to monitor adolescents’ behavior ( Osgood and Anderson 2004 ). Lastly, according to conventional social control and attachment theories (e.g., Hirschi 1969 ), adolescents who feel alienated from their families may spend more time with delinquent peers outside of a supervised setting. Thus, the first aim of this study is to test the hypothesis that peer substance use and unstructured socializing mediate the association between ACEs and substance use (the path labeled “a” in Figure 1 ).

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Object name is nihms-1796580-f0001.jpg

Conceptual model.

Moderation of Peer Influences by ACEs

In addition to being more likely to spend time in peer contexts conducive to substance use, youth with ACEs may also be more vulnerable to certain peer influences. In statistical terms, ACEs may moderate the association between peer influences and substance use; this hypothesis is represented by the path labeled “b” in Figure 1 . One reason for this may be that youth who have experienced ACEs are more likely to live with parents who use drugs or drink alcohol and thus have greater access to these substances. Children may also inherit a genetic predisposition toward substance use, which makes them more vulnerable to peer influences ( Chassin et al. 2013 ).

In addition, the stress and trauma of ACEs can set in motion the development of maladaptive behavioral patterns and personality traits which can create difficulties in regulating peer influences. For example, in one sample of youth involved in the juvenile justice system, a greater number of ACEs was associated with higher impulsivity, aggression, and admiration of deviant peers ( Perez et al. 2018 ). Experiencing maltreatment also increases the likelihood of developing intrusive or disorganized attachment styles ( Cyr et al. 2010 ; Kendall-Tackett 2002 ). In two prior studies, for example, Susan D. Hillis and colleagues (2004) hypothesized that the established association between ACEs and risky sexual behavior could be due in part to a desire to seek interpersonal intimacy in the absence of emotional warmth and support at home. Consequently, these youth may experience greater difficulties in saying no to peers and situations that encourage substance use ( Agnew 1991 ). In addition to disrupting the development of healthy attachment styles, negative family experiences and strained household relationships contribute to a lower sense of self-control ( Wills and Dishion 2004 ), lower self-efficacy ( Schulenberg et al. 1996 ), and less autonomy ( Allen et al. 2012 ), all of which limit youth’s ability to set healthy boundaries with peers ( Jaccard, Blanton, and Dodge 2005 ). In other words, at the same time that youth with a greater number of ACEs are more likely to spend times in peer situations conducive to substance use, they may also be more vulnerable to these influences ( O’Donnell, Schwab-Stone, and Muyeed 2002 ). Accordingly, the second aim of this study is to examine whether ACEs magnify youth’s vulnerability to substance use promoting peer influences.

Moderation by Neighborhood Collective Efficacy

Following routine activity theory ( Osgood et al. 1996 ), the larger community context also shapes the opportunities and perceived risks and rewards of substance use. Beyond the sociodemographic composition of a community, contextual characteristic like the aggregate level of parental monitoring ( Osgood and Anderson 2004 ), number of bars and liquor stores ( Maimon and Browning 2012 ), neighborhood norms ( Zimmerman and Farrell 2017 ), and the strength of community ties ( Tobler, Komro, and Maldonado-Molina 2009 ) matter for substance use. To build on prior research examining the contextual effects on delinquency broadly, this study considers how the neighborhood environment shapes the effects of peer influence on youth differentially exposed to ACEs ( J. P. Hoffmann 2003 ).

Collective efficacy, defined as the extent to which the members of the community can monitor and supervise youth and intervene in the presence of risk or physical threat ( Sampson, Raudenbush, and Earls 1997 ), is one such neighborhood characteristic that may impact the strength of peer influences on adolescent substance use (see the pathway labeled “c” in Figure 1 ). The combination of greater adult supervision in addition to a willingness to intervene can mitigate other environmental or individual risks. For example, David Maimon and Christopher R. Browning (2012) found that the association between alcohol retail outlets and underage drinking was stronger in neighborhoods with low collective efficacy, suggesting that in these environments alcohol is easier and less “expensive” to obtain because youth are less concerned that neighbors will observe and report them. Furthermore, collective efficacy may be particularly protective for youth with a greater number of ACEs: prior research demonstrates that greater collective efficacy attenuated associations between childhood neglect and later externalizing behaviors ( Yonas et al. 2010 ) and between violent victimization and adolescent substance use ( Fagan, Wright, and Pinchevsky 2014 ). In addition to reflecting greater adult supervision and willingness to intervene, collective efficacy is also associated with increased self-efficacy ( Dupéré, Leventhal, and Vitaro 2012 ) and emotional resilience ( Jain et al. 2012 ). Thus, neighborhood collective efficacy may be a source of resilience for youth affected by ACEs, such as improving youth’s ability to decline substance use. In other words, the difference in the strength of the effects of peer influences on substance use by ACE status may not be as large in neighborhoods with greater collective efficacy.

On the other hand, as Christopher R. Browning (2009) points out, most research on social capital (including collective efficacy) tends to assume that social capital facilitates unambiguously positive outcomes. While one aspect of collective efficacy emphasizes the willingness of neighbors to intervene on behalf of the well-being of one another (i.e., informal social control), the other captures trust, cohesion, and reciprocal exchange (i.e., social cohesion). Although both aspects are seemingly positive, the social cohesion aspect may facilitate youth’s unstructured socializing, which in turn increases youth’s risk of substance use. In other words, the social integration inherent in collective efficacy may have countervailing effects on informal social control ( Pattillo 1998 ). If parents perceive the neighborhood to be a protective environment or trust other parents to appropriately supervise, then they may feel comfortable letting their children spend time with peers in unstructured settings ( Maimon and Browning 2010 ), which in turn promotes substance use. In highly integrated social networks, parents may be more accepting of older adolescents in the neighborhood spending time with younger peers, which could provide younger adolescents with models and opportunities for substance use ( Harding 2009 ). For reasons described in the previous section, peer influences may be particularly pronounced for youth who feel alienated from their families. Thus, in higher collective efficacy neighborhoods, the difference in the strength of the relationship between peer influences and substance use by ACE history may be greater. The third aim of this study is to adjudicate between these two hypotheses concerning how collective efficacy shapes the association between peer influences and substance use for youth with varying levels of ACEs.

Data and Method

Data and analytic sample.

This study used two components of the PHDCN: neighborhood cluster-level data from the Community Survey collected in 1994–1995 and individual data from the Longitudinal Cohort Study collected over three waves in 1994–1997, 1997–1999, and 2000–2001.

Neighborhood clusters consisted of geographically contiguous and sociodemographically homogenous census tracts made up of approximately 8,000 individuals each. For the Community Survey, sampling took place in three stages: city blocks were sampled within each neighborhood cluster, dwelling units were sampled within each block, and then one adult resident was randomly interviewed within each dwelling unit. A total of 8,782 individuals in 343 neighborhood clusters completed interviews regarding several aspects of their neighborhood social environment, with answers aggregated to the neighborhood cluster level.

For the Longitudinal Cohort Study, 80 of the 343 neighborhood clusters were selected for sampling so as to maximize variability in racial/ethnic composition and socioeconomic status. Again, a three-stage sampling design randomly selected block groups within these neighborhood clusters, dwelling units within blocks, and residents with children in seven age groups (within six months of birth, ages 3, 6, 9, 12, 15, and 18). At Wave 1, 6,226 children and their families completed interviews. The sample was limited to youth who were 9, 12, and 15 years old at Wave 1 and approximately 12, 15, and 18 years old at Wave 2 ( n = 2,345) to capture the ages when the onset of substance use generally occurs and frequency of use tends to escalate ( Chassin et al. 2004 ). Approximately 85 percent of youth in these cohorts completed Wave 2 interviews. Youth who did not complete the substance use section of the interview, due to attrition or otherwise, were not included in the analysis, resulting in a final analytic sample of 1,912 youth.

Measurement

The independent variable of interest was modeled after Vincent J. Felitti and colleagues’ (1998) ACE Study questionnaire. Based on the availability of questions in the PHDCN, an index of adverse childhood experiences included seven categories derived from primary caregiver reports at Wave 1. First, verbal abuse referred to whether or not the primary caregiver ever insulted or swore at the child, threatened to hit or throw something at the child, or threw, smashed, hit, or kicked something during an argument with the child. Second, physical abuse represented whether or not they ever threw something at the child; pushed, grabbed, or shoved the child; slapped or spanked the child; kicked, bit, or hit child with a fist; hit or tried to hit the child with an object; beat the child up; or burned or scalded the child. Third, maternal abuse was defined as whether the primary caregiver reported that her partner or spouse ever threatened her with or actually enacted physical violence toward her. Fourth, household substance abuse was based on reports of anyone in the household experiencing legal, family, or health problems due to substance use. Fifth, household member mental illness referred to whether anyone in the household ever suffered from severe depression, had frequent legal or disciplinary problems, had problems with their nerves or suffered a nervous breakdown, or ever attempted or committed suicide. The last two items included incarceration of a household member and experiencing parental divorce or separation.

Note that the resulting index ranges from 0 to 7 and represents distinct types of adversity as in past studies ( Dong et al. 2004 ), rather than chronicity or severity. Missingness for each item was generally low (less than 5 percent; see Table 1 ), except for parental divorce, which was unavailable or children who lived with a primary caregiver other than a parent. Youth with missing data on some but not all items were still included in the analysis, thus their ACE score was a conservative estimate of their actual number of ACEs.

Sample Description ( N = 1,912).

Variables	Frequency or ( )	Percent missing
Individual variables
Adverse childhood experiences
Any verbal abuse	70.14%	0.00
Any physical abuse	67.47%	0.00
Family/household member substance use	44.71%	1.15
Divorce	17.63%	16.63
Family/household mental illness	43.70%	1.26
Any maternal abuse	32.11%	0.00
Family/household member incarceration	7.96%	4.03
Overall adverse childhood experience index	2.79 (1.55)	0.00
Peer substance use	0.0004 (0.79)	3.40
Unstructured socializing	0.002 (0.87)	0.31
Number of days drank in past month	0.57 (2.08)	7.17
Number of days used drugs in past month	0.67 (3.18)	5.70
Number of cigarettes smoked in past month	10.85 (57.28)	7.64
Female	49.53%	0.00
Race
White	14.91%	0.05
Hispanic	46.94%	0.05
Black	34.43%	0.05
Other	3.72%	0.05
Age cohort
Cohort 9	35.30%	0.00
Cohort 12	35.67%	0.00
Cohort 15	29.03%	0.00
Age of primary caregiver	37.51 (6.32)	6.54
Household socioeconomic status	−0.10 (1.42)	0.78
Family size	5.35 (2.02)	2.30
Neighborhood variables
Collective efficacy	0.02 (1.00)	0.00
Concentrated disadvantage	−0.02 (1.00)	0.00
Immigrant concentration	−0.005 (1.00)	0.00
Residential stability	0.02 (1.00)	0.00
Crime	−0.03 (1.00)	0.00
	1,912

Youth reported their substance use at Wave 2. Number of cigarettes smoked in the past month ranged from 0 to 638 cigarettes and was created by multiplying the average number of cigarettes smoked per day (less than one, 1–5, 6–15, 16–25, or more than 25) and the number of days smoked in the past month (never, 1–2, 3–5, 6–9, 10–14, 15–20, or more than 20). Before multiplying, categories were recoded to their midpoint (less than one cigarette was recoded to zero cigarettes), and the resulting product was rounded to the nearest integer. Number of days drank in the past month was based on two questions asking if youth ever had a drink of beer, wine, wine cooler, or hard liquor (not including sips or tastes), and if so, how many days have they drank in the past 30 days (never, 1–2, 3–5, 6–9, 10–14, 15–20, or more than 20). As with number of cigarettes, categories were recoded to their midpoint and rounded up; the final variable ranged from 0 to 21. Number of illicit drug use days in the past month was created by summing the responses to how frequently (never, 1–2, 3–5, 6–9, 10–14, 15–20, or more than 20) youth used each of 11 drugs in the past month (marijuana, cocaine, crack, inhalants, hallucinogens, heroin, barbiturates, tranquilizers, amphetamines, steroids, or intravenous drugs). Again, categories were recoded to their midpoint and the sum was rounded to the nearest integer; the final variable ranged from 0 to 33.

Unstructured socializing at Wave 2 was based on the mean response gauging the frequency of five activities ( Maimon and Browning 2010 ): riding in a car or motorcycle for fun, hanging out with friends, going to parties, going out after school or at night, and going on a date. Responses were then standardized. Peer substance use represented the mean number of friends a respondent reported using marijuana, drinking, and smoking. Both of these variables were mean-centered.

The PHDCN Scientific Directors created the neighborhood collective efficacy scale by adding and standardizing responses to two five-item Likert scales measuring social cohesion and informal social control ( Sampson et al. 1997 ). For social cohesion, respondents answered the extent to which they agreed (“very strongly agreed, agreed, neither agreed nor disagreed, disagreed, strongly disagreed”) that they lived in a close-knit neighborhood, neighbors were willing to help each other, neighbors got along with each other, neighbors shared the same values, and neighbors could be trusted. To measure informal social control, respondents reported the likelihood (“very likely, likely, neither likely nor unlikely, unlikely, or very unlikely”) that neighbors would intervene if a group of children skipped school and hung out on the street corner, spray-painted graffiti on a local building, was disrespectful to an adult, if a fight broke outside, or if the city threatened to close down a fire station. These two scales were closely associated across neighborhoods ( r = .80; p < .001) and thus combined into a single construct.

Individual-level covariates, all measured at Wave 1, included gender, age, race/ethnicity, family size, primary caregiver’s age, and socioeconomic status constructed from the principal component of the highest education level of the parent or partner, household income, and the highest socioeconomic index of the parent or partner’s occupation ( Sampson, Morenoff, and Earls 1999 ). Neighborhood-level covariates included four variables created by the PHDCN Scientific Directors using data from the 1990 Census as well as from the Chicago Police Department: violent crime, concentrated disadvantage (the first principal component of the percentage of families receiving public assistance, unemployed individuals, female-headed families with children, and percentage of Black residents; Sampson et al. 1997 ), residential instability, and immigrant population concentration ( Kirk and Papachristos 2011 ; Molnar et al. 2003 ).

All models were estimated using negative binomial regression. This type of regression is appropriate when the outcome is an over-dispersed count variable in which its variance exceeds its mean. To address the first two aims (mediation and moderation by peer influences), path analysis conducted in Mplus estimated the associations between ACEs, the two peer influence variables, and three substance use outcomes. These models employed robust standard errors to adjust for neighborhood clustering. The Mplus command MODEL INDIRECT tested mediation effects, and interaction terms between peer influences and ACEs tested moderation. For the third aim concerning further moderation by collective efficacy, multilevel random intercepts models were used with individual-level variables and interaction terms specified at the “within” level and neighborhood variables at the “between” level. Iteratively, these models estimated the three-way interaction between the two peer influence variables, collective efficacy, and ACEs for each outcome. All models were estimated in Mplus version 7.31 ( Muthén and Muthén 1998–2012 ) using full-information maximum likelihood procedures to account for the minimal missing data that remained after sample restrictions were applied ( Enders and Bandalos 2001 ).

Sample Description

As displayed in Table 1 , ACEs were common among this sample of youth, with youth experiencing an average of about three different types of adversity. The most commonly experienced adverse experiences were verbal and physical abuse, with approximately two-thirds of the sample reporting such experiences. Over two-fifths of the sample reported living with someone with a substance abuse problem or mental illness. About a third of the sample reported abuse of their primary caregiver, 18 percent of youth experienced parental divorce, and about 8 percent experienced the incarceration of a household or family member. For substance use, youth reported a mean of 0.57 days in which they drank and 0.67 days in which they used illicit drugs. Youth smoked an average of 10.85 cigarettes in the past month. However, use varied considerably and the standard deviations of all three variables were quite large relative to their means, thus necessitating the use of negative binomial regression models.

Mediation by Peer Influences

The results displayed in Table 2 address the question, “To what extent do peer influences mediate the association between ACEs and substance use?” All tables show the parameter estimates and can be interpreted as the difference in the logs of expected counts of the outcome variable; exponentiating the coefficients gives the incidence rate ratios. As shown in columns 1, 3, and 5 of Table 2 , ACEs were significantly and positively associated with all three substance use outcomes. Each additional ACE was associated with an average increase of 1.12 days drank, 1.21 days used drugs, and 1.51 cigarettes smoked in the past month. Including peer substance use and unstructured socializing as mediators, shown in columns 2, 4, and 6 of Table 2 , completely accounted for the observed associations between ACEs and each substance use outcome. These results supported the hypothesis that youth with a higher number of ACEs are more likely to engage in peer groups and settings that increase the frequency of substance use.

Direct and Indirect Paths between Peer Influences, ACEs, and Substance Use ( N = 1,912).

	Negative Binomial Regression Coefficients
	Days drank in past month		Days used drugs in past month		Cigarettes smoked in past month
Variables	(1)	(2)	(3)	(4)	(5)	(6)
ACEs	0.116 (0.056)	−0.002 (0.052)	0.194 (0.095)	0.003 (0.102)	0.414 (0.088)	0.083 (0.092)
Peer substance use		1.049 (0.145)		1.791 (0.234)		2.090 (0.230)
Unstructured socializing		0.816 (0.102)		1.156 (0.168)		1.298 (0.203)
Female	−0.260 (0.190)	0.119 (0.179)	−0.497 (0.311)	−0.003 (0.224)	0.042 (0.345)	−0.002 (0.343)
Race (ref: White)
Hispanic	−0.327 (0.271)	0.023 (0.277)	−0.862 (0.485)	−0.158 (0.408)	−0.930 (0.501)	−0.864 (0.398)
Black	−0.877 (0.268)	−0.460 (0.233)	−0.999 (0.438)	−0.096 (0.396)	−1.847 (0.459)	−1.675 (0.482)
Other	−1.279 (0.436)	−0.627 (0.342)	−1.861 (0.614)	−0.828 (0.553)	−1.011 (0.389)	−0.811 (0.565)
Age cohort (ref: Cohort 9)
Cohort 12	2.964 (0.602)	1.742 (0.600)	12.699 (0.332)	10.495 (0.340)	4.402 (0.535)	2.021 (0.589)
Cohort 15	4.902 (0.588)	2.817 (0.650)	15.152 (0.228)	11.943 (0.399)	7.322 (0.525)	4.840 (0.627)
Age of primary caregiver	0.390 (0.203)	0.239 (0.187)	−0.406 (0.215)	−0.462 (0.245)	0.829 (0.314)	0.069 (0.236)
Household SES	0.023 (0.075)	0.065 (0.076)	−0.095 (0.096)	−0.090 (0.117)	−0.292 (0.124)	−0.349 (0.142)
Family size	−0.006 (0.062)	0.032 (0.060)	−0.091 (0.074)	−0.066 (0.074)	−0.240 (0.075)	−0.194 (0.070)
Indirect effects
Peer substance use		0.059 (0.015)		0.101 (0.025)		0.116 (0.026)
Unstructured socializing		0.041 (0.011)		0.058 (0.017)		0.066 (0.019)
	1,912	1,912	1,912	1,912	1,912	1,912

Note . ACEs = adverse childhood experiences; SES = socioeconomic status.

two-tailed significance tests.

Moderation of Peer Influences by ACE History

Table 3 shows results for the second analytical aim, which was to determine whether and how ACEs moderate the association between peer influences and substance use. Looking at the first four columns of Table 3 , ACEs did not moderate the associations between either of the peer influence variables and number of days drank or used drugs in the past month. ACEs also did not moderate the association between unstructured socializing and number of cigarettes smoked in the past month. However, ACEs did moderate the association between peer substance use and number of cigarettes smoked in the past month. For ease of interpretation, Figure 2 shows the predicted number of cigarettes as a function of the number of ACEs and level of peer substance use. At average and low levels of peer substance use (defined as the mean level of peer substance use and one standard deviation below the mean, respectively), the predicted number of cigarettes smoked did not differ as a function of ACEs history. At high levels of peer substance use (one standard deviation above the mean), the predicted number of cigarettes smoked in the past month increased tenfold from 3.18 cigarettes at zero ACEs to 31.4 cigarettes at the maximum number of seven ACEs. These results partially supported the hypothesis that ACEs magnify youth’s vulnerability to substance use promoting peer influences—specifically, youth with a greater number of ACEs smoked more when they were around substance-using peers than youth without a history of ACEs.

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Predicted number of cigarettes smoked in past month, by number of adverse childhood experiences and level of peer substance use.

Moderation of Peer Influences on Substance Use by ACEs ( N = 1,912).

	Negative Binomial Regression Coefficients
	Days drank in past month		Days used drugs in past month		Cigarettes smoked in past month
Variables	(1)	(2)	(3)	(4)	(5)	(6)
ACEs	0.062 (0.063)	−0.002 (0.067)	0.053 (0.159)	−0.080 −0.097	0.033 (0.107)	0.079 (0.101)
Peer substance use	1.390 (0.201)	1.054 (0.149)	2.032 (0.405)	1.801 (0.229)	1.094 (0.514)	2.222 (0.211)
Unstructured socializing	0.809 (0.099)	0.818 (0.219)	1.160 (0.169)	0.623 (0.303)	1.321 (0.183)	0.889 (0.485)
Peer substance use × ACEs	−0.109 (0.063)		−0.075 (0.587)		0.374 (0.155)
Unstructured socializing × ACEs		−0.001 (0.061)		0.186 −0.105		0.125 (0.146)
	1,912	1,912	1,912	1,912	1,912	1,912

Note . Models control for child gender, child race/ethnicity, child age, primary caregiver’s age, household socioeconomic status, and family size. ACEs = adverse childhood experiences.

Moderation of Peer Influences by Neighborhood Collective Efficacy and ACE History

The third aim was to examine how neighborhood collective efficacy shapes the associations between ACEs, peer influences, and substance use. The three-way interactions between ACEs, collective efficacy, and each of the peer influences were not significant for drug use, thus only the results for number of cigarettes and number of days drank are shown. The results for drug use are available from the author upon request.

Table 4 displays the three-way interactions between ACEs, the two peer influence variables, and neighborhood collective efficacy for number of cigarettes smoked and number of days drank in the past month. For ease of interpretation, Figure 3 shows how the associations (expressed as incidence rate ratios) between the two peer influence variables and number of cigarettes smoked changed based on the number of ACEs and level of neighborhood collective efficacy. In Figure 3 , “low” and “high” refer to one standard deviation below and above, respectively, the mean neighborhood collective efficacy score. In low collective efficacy neighborhoods, the association between peer substance use and smoking varied considerably by a youth’s number of ACEs. For youth with no ACEs in these neighborhoods, a one point increase in peer substance use was associated with an average expected increase of 1.3 cigarettes smoked in the past month. For an adolescent with five ACEs in the same neighborhood, a one point increase in peer substance use was associated with an increase of 717 cigarettes smoked in the past month (i.e., a little more than a pack a day). This moderation of peer substance use by ACEs was also present in high collective efficacy neighborhoods, but to a smaller extent. The average expected increase of cigarettes smoked in the past month associated with a one point increase in peer substance use ranged from 5.6 cigarettes for youth with no ACEs to 30.7 cigarettes for youth with five ACEs. The moderation of unstructured socializing by ACEs on smoking did not differ by level of neighborhood collective efficacy.

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Associations between peer influences and number of cigarettes smoked in past month by number of ACEs and neighborhood CE.

Note . ACEs = adverse childhood experiences; CE = collective efficacy.

Moderation of Peer Influences on Smoking and Drinking by ACEs and Neighborhood Collective Efficacy ( N = 1,912).

	Negative Binomial Regression Coefficients
	Cigarettes smoked in past month			Days drank in past month
Variables	(1)	(2)	(3)	(1)	(2)	(3)
Adverse childhood experiences	0.083 (0.139)	−0.232 (0.218)	−0.073 (0.147)	−0.004 0.051	0.012 (0.065)	−0.015 (0.062)
Peer substance use	3.206 (0.341)	1.013 (0.665)	2.818 (0.620)	1.092 (0.150)	1.335 (0.208)	1.158 (0.138)
Unstructured socializing	1.481 (0.259)	1.470 (0.245)	0.359 (0.744)	0.798 (0.102)	0.818 (0.105)	0.902 (0.224)
Collective efficacy	0.284 (0.440)	0.292 (0.425)	0.107 (0.476)	0.075 (0.143)	0.024 (0.240)	0.057 (0.186)
Collective efficacy × ACEs		0.175 (0.124)	0.069 (0.052)		0.069 (0.035)	−0.005 (0.042)
Peer substance use × ACEs		0.787 (0.210)			−0.053 (0.061)
Peer substance use × collective efficacy		0.738 (0.493)			−0.136 (0.128)
Peer substance use × collective efficacy × ACEs		−0.465 (0.150)			−0.047 (0.048)
Unstructured socializing × ACEs			0.334 (0.275)			−0.020 (0.062)
Unstructured socializing × collective efficacy			0.232 (0.445)			−0.400 (0.145)
Unstructured socializing × collective efficacy × ACEs			−0.041 (0.198)			0.149 (0.053)

Note . Models control for child gender, child race/ethnicity, child age, primary caregiver’s age, household socioeconomic status, and family size at the individual level; and concentrated disadvantage, immigrant concentration, residential stability, and violent crime at the neighborhood level. ACEs = adverse childhood experiences.

Turning to number of days drank in Figure 4 , the moderation of peer substance use by ACEs did not differ by level of neighborhood collective efficacy. However, ACEs did moderate the effect of unstructured socializing on number of days drank differently based on the level of collective efficacy in the youth’s neighborhood. In low collective efficacy neighborhoods, ACEs moderated the effect of unstructured socializing in an opposite direction than in previous models—an increase in ACEs attenuated the association between unstructured socializing and number of days drank. However, the pattern was reversed in high collective efficacy neighborhoods such that ACEs intensified the association between unstructured socializing and drinking. A one point increase in unstructured socializing was associated with an average expected increase of 1.7 days drank in the past month for youth with no ACEs. For youth with five ACEs, this expected increase per one point in unstructured socializing rose to 3.1 days drank in the past month. These results provided partial support to both hypotheses regarding the interactive effects between ACEs, neighborhood collective efficacy, and peer influences, depending on the specific type of peer influence and substance use outcome examined.

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Associations between peer influences and number of days drank in past month by number of ACEs and neighborhood CE.

Discussion and Conclusion

ACEs consistently predict worse mental and physical health outcomes throughout the life course, in part through the development of risky health behaviors. In particular, youth with adverse experiences are more likely to ever drink, smoke, and use drugs ( Dube et al. 2006 ), use these substances in greater quantities ( Anda et al. 1999 ) and to initiate use at earlier ages ( Dube et al. 2003 ). Peer influences, particularly the increased availability and perceived rewards of substance use within certain peer groups, also consistently predict adolescents’ own substance use. Given these findings, more research needs to consider how peers shape substance use behaviors for youth affected by ACEs in particular. This study fills in these gaps in the literature by considering the extent to which peer influences explain the association between ACEs and substance use as well as how ACEs moderate the established relationship between peer influences and substance use. Community contexts also shape opportunities for substance use and the extent to which peer groups can facilitate substance use. Thus, this study also considers how peer influences operate differentially for youth depending on both their history of ACEs and neighborhood context.

As hypothesized, peer substance use and unstructured socializing mediated the association between ACEs and all three substance use outcomes examined. Furthermore, ACEs strengthened the association between peer substance use and number of cigarettes smoked in the past month. Lastly, two three-way interactions between neighborhood collective efficacy, peer influences, and ACEs revealed that the strength of the link between peer influences and substance use differed not only by a history of ACEs but also by community context and the type of substance use examined. Specifically, the strengthening effect of ACEs on the association between peer substance use and smoking was more pronounced in low collective efficacy neighborhoods. The opposite pattern emerged for drinking and unstructured socializing—ACEs attenuated the association between unstructured socializing and drinking in low collective efficacy neighborhoods but strengthened it in high collective efficacy neighborhoods. These results point to three main themes.

First, youth with a history of ACEs were more likely to spend time in peer settings that are associated with increased substance use; specifically, they are more likely to engage in unstructured socializing and have friends who drink, smoke, and use drugs. A major methodological concern in the literature on peer effects on adolescent substance use is the distinction between peer selection and peer influence. In other words, do youth who already drink or smoke seek out peers who engage in similar behaviors (i.e., selection) or do substance-using peers encourage their friends to partake (i.e., influence)? This study cannot precisely disentangle whether youth with a greater number of ACEs simply gravitate toward friends who have similar substance use habits or if their friends’ influence exerts a causal effect. Both mechanisms likely shape adolescents’ risk of substance use ( Haynie 2001 ; Kandel 1978 ; Krohn et al. 1996 ; Matsueda and Anderson 1998 ; Thornberry 1987 ); a better understanding of both how youth with ACEs select peer groups and how these peers affect them can aid in prevention and outreach efforts. Another limitation of estimating peer effects is that respondents may overestimate how much their friends actually use substances. However, this study supports prior research (e.g., Haynie and Osgood 2005 ) indicating that both unstructured socializing and peer substance are independently associated with youth’s own substance use, particularly for youth with a history of ACEs. New methodological advances such as the use of intensive longitudinal modeling (e.g., Weerman, Wilcox, and Sullivan 2018 ) can help to better elucidate the proximate processes linking peer settings and influences to youth’s substance use.

Second, a history of ACEs was associated with greater vulnerability to peer substance use regarding number of cigarettes smoked. This result corresponds to Julie S. Olson and Robert Crosnoe’s (2018) finding that the association between peer drinking and youth’s own drinking was stronger for youth who had binge-drinking parent(s) compared with youth whose parents who had not recently binge drank. Going beyond parental substance use, this study considers multiple types of family experiences that may set the stage for adolescent substance use. The intensification may apply to cigarette smoking in particular because cigarettes are easier to procure (especially among older adolescents who can buy them legally) compared with alcohol or illicit drugs. Smoking may be an attractive and easily available tool for emotional regulation among youth with ACEs who spend time with peers who also smoke.

One limitation of these data is that the ACE index was derived from primary caregiver reports of their own behaviors and household experiences; thus, this measurement likely represents an underreport of ACEs if primary caregivers are reluctant to disclose abusive behavior. In this case, estimates reported in this study are conservative. Another limitation is that this measure of ACEs only captures whether or not youth ever experienced each type of adversity, rather than chronicity and/or severity. Nevertheless, this measure of ACEs improves over many past studies by capturing more temporally proximate experiences, rather than relying on adults’ retrospective reports of their childhoods. Next steps for this line of research include more sophisticated measurement of ACEs, such as explicit consideration of the severity and chronicity of maltreatment, as well as the cumulative and interactive effects of multiple types of trauma. Other research also expands the definition of ACEs to consider exposure to violence in the community, foster care involvement, and other adverse experiences at school and in the neighborhood (e.g., Cronholm et al. 2015 ; Fagan et al. 2014 ).

Third, the extent to which peer influences matter for youth’s substance use depended not only on their history of ACEs but also their neighborhood context. Not only do neighborhood characteristics moderate parental influences on adolescent substance use ( Zimmerman and Farrell 2017 ), but this study suggests that they also shape the influence of peers on adolescent substance use, and in nuanced ways depending on an adolescent’s ACEs history and type of substance. Collective efficacy limited the interactive effect between peer substance use and ACEs on smoking. In other words, youth with a high number of ACEs and substance-using peers smoked less in neighborhoods with high collective efficacy compared with their peers with the same number of ACEs and same level of peer substance use but in low collective efficacy neighborhoods. This finding aligns with the hypothesis that collective efficacy can have a protective effect for youth in vulnerable situations and is consistent with Abigail A. Fagan and colleagues’ (2014) finding that violence-exposed youth are less likely to turn to substance use in high collective efficacy neighborhoods compared with their peers in lower collective efficacy neighborhoods. This could be because adults in high collective efficacy neighborhoods are more likely to monitor adolescents and intervene to prevent negative health behaviors like smoking.

However, this pattern did not hold true in the models for drinking. Collective efficacy intensified the interactive effect between ACEs and unstructured socializing on number of days drank in the past month. The magnification of the interaction between ACEs and unstructured socializing on drinking in the context of high neighborhood collective efficacy observed in this study stands in contrast to David Maimon and Christopher R. Browning’s (2010) findings, in which greater collective efficacy attenuated the association between unstructured socializing and violent behavior. Consistent with routine activity and negotiated coexistence theory ( Browning 2009 ), these findings reflect the idea that the deviant behavior may flourish in what may typically be thought of as prosocial environments ( Osgood et al. 1996 ). Specifically, the greater trust among adults and youth may actually facilitate adolescent drinking.

These findings raise the question as to why collective efficacy operated so differently for the two different substance use outcomes. The effect of collective efficacy on youth’s substance use may depend on local norms among adults ( Ahern et al. 2009 ) whose intervention can either facilitate or prevent adolescent substance use. Most parents, regardless of socioeconomic status, likely do not approve of adolescent smoking. However, many parents may approve of youth drinking in moderation, particularly in wealthier communities ( Snedker, Herting, and Walton 2009 ). One study found that collective efficacy was associated with more drinking for older adolescents (ages 16–19), but less drinking for young adolescents (under age 16) ( Jackson et al. 2016 ). Adults may see it as appropriate for older adolescents to drink and may facilitate this drinking. Furthermore, collective efficacy may reflect the trust among adults in the community that facilitates opportunities for vulnerable youth to engage with substance-using peers in unstructured, unsupervised settings. A previous study demonstrates that youth’s own perception of safety is also positively associated with binge-drinking initiation ( Tucker et al. 2013 ). The combination of high collective efficacy and high unstructured socializing which predicts more drinking among youth with ACEs may also reflect youth’s own perceptions of neighborhood safety. As the authors of the study note, youth’s perception of safety likely refers to safety from being punished or stopped by adults, rather than from neighborhood crime or violence. In high collective efficacy neighborhoods, adults are willing to intervene to stop adolescents from engaging in unhealthy or unsafe behavior—but they might not perceive drinking as such.

Another limitation of this study is that unstructured socializing and substance use may not necessarily occur within the bounds of an adolescent’s neighborhood. Collective efficacy may be confounded with some other characteristic of a youth’s school or home environment; however, the analysis controls for several neighborhood-level covariates such as crime, concentrated disadvantage, and residential instability to minimize this possibility. In addition, collective efficacy was measured three years prior to youth’s substance use and peer settings and the data do not capture the characteristics of a different neighborhood if youth moved between waves. However, prior research indicates that the social environment of a neighborhood generally does not change drastically in a short period of time, nor do families usually move into significantly different types of neighborhoods ( Sampson 2011 ). Future research using new methodological tools such as ecological momentary assessment (e.g., Roberts et al. 2019 ) and theoretical concepts such as activity spaces and ecological networks (e.g., Browning, Soller, and Jackson 2015 ) can clarify how place matters for youth’s substance use.

It is important to note that the PHDCN sample is not nationally representative and is disproportionately made up of non-white adolescents living in urban areas. Although this group may experience more ACEs than the general population, ACEs are common across all socioeconomic groups ( Merrick et al. 2018 ). Black and Hispanic adolescents are less likely to smoke cigarettes, use illicit drugs, and drink heavily compared with white adolescents (although these differences have narrowed in recent years; Johnston et al. 2017 ). Furthermore, the association between economic deprivation and substance use is weaker for Black and Hispanic youth ( Bachman et al. 2011 ). Thus, these results may be an underestimate of the associations between ACEs, peer influences, neighborhood context, and substance use. A major advantage of using the PHDCN is its rich data on neighborhood contexts that is not available with nationally representative data sets like Monitoring the Future. Nevertheless, future national data collection efforts regarding adolescent substance use may benefit from questions regarding youth’s neighborhood context. More research should also pay attention to adolescents in rural areas, particularly those most affected by the opioid epidemic.

Linking insights from criminology, sociology, and developmental psychology, this study demonstrates the relevance of examining family and peer contexts as crucial influences on adolescent health behaviors that may persist into adulthood. It also offers a start to the examination of the neighborhood contexts of substance use trajectories for youth affected by ACEs. In sum, peer settings explained a large portion of the association between ACEs and adolescent substance use; additionally, youth with a history of ACEs were more vulnerable to problematic peer influences with regard to cigarette smoking. Last, collective efficacy further modified the interactive effect of ACEs with peer influences, but in different ways depending on the type of substance use examined. These results invite further investigation into the proximate school, peer, and community factors influencing the substance use behaviors of youth affected by ACEs.

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by grant P2CHD042849, Population Research Center, and grant T32HD007081, Training Program in Population Studies, awarded to the Population Research Center at The University of Texas at Austin by the Eunice Kennedy Shriver National Institute of Child Health and Human Development. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Author Biography

Haley Stritzel received her PhD in Sociology from the University of Texas at Austin and is a current post-doctoral scholar at the University of North Carolina at Chapel Hill. She studies the family and community contexts of youth well-being, with a particular emphasis on children involved with the child welfare system. Her current research focuses on foster children affected by parental substance use and kinship caregivers.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Agnew Robert. 1991. “ The Interactive Effects of Peer Variables on Delinquency .” Criminology 29 ( 1 ):47–72. [ Google Scholar ]
Ahern Jennifer, Galea Sandro, Hubbard Alan, and Syme S. Leonard. 2009. “ Neighborhood Smoking Norms Modify the Relation between Collective Efficacy and Smoking Behavior .” Drug and Alcohol Dependence 100 ( 1–2 ):138–45. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Allen Joseph P., Chango Joanna, Szwedo David, Schad Megan, and Marston Emily. 2012. “ Predictors of Susceptibility to Peer Influence Regarding Substance Use in Adolescence .” Child Development 83 ( 1 ):337–50. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Anda Robert F., Brown David W., Dube Shanta R., Bremner J. Douglas, Felitti Vincent J., and Giles Wayne H.. 2008. “ Adverse Childhood Experiences and Chronic Obstructive Pulmonary Disease in Adults .” American Journal of Preventive Medicine 34 ( 5 ):396–403. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Anda Robert F., Croft Janet B., Felitti Vincent J., Nordenberg Dale, Giles Wayne H., Williamson David E., and Giovino Gary A.. 1999. “ Adverse Childhood Experiences and Smoking during Adolescence and Adulthood .” Journal of the American Medical Association 282 ( 17 ):1652–58. [ PubMed ] [ Google Scholar ]
Bachman Jerald G., O’Malley Patrick M., Johnston Lloyd D., Schulenberg John E., and Wallace John M. Jr. 2011. “ Racial/Ethnic Differences in the Relationship between Parental Education and Substance Use among US 8th-, 10th-, and 12th-grade Students: Findings from the Monitoring the Future Project .” Journal of Studies on Alcohol and Drugs 72 ( 2 ):279–85. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Brechwald Whitney A. and Prinstein Mitchell J.. 2011. “ Beyond Homophily: A Decade of Advances in Understanding Peer Influence Processes .” Journal of Research on Adolescence 21 ( 1 ):166–79. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Browning Christopher R. 2009. “ Illuminating the Downside of Social Capital: Negotiated Coexistence, Property Crime, and Disorder in Urban Neighborhoods .” American Behavioral Scientist 52 ( 11 ):1556–78. [ Google Scholar ]
Browning Christopher R., Soller Brian, and Jackson Aubrey L.. 2015. “ Neighborhoods and Adolescent Health-risk Behavior: An Ecological Network Approach .” Social Science & Medicine 125 :163–72. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Cavanagh Shannon E., Stritzel Haley, Smith Chelsea, and Crosnoe Robert. 2018. “ Family Instability and Exposure to Violence in the Early Life Course .” Journal of Research on Adolescence 28 ( 2 ):456–72. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Chapman Daniel P., Whitfield Charles L., Felitti Vincent J., Dube Shanta R., Edwards Valerie J., and Anda Robert F.. 2004. “ Adverse Childhood Experiences and the Risk of Depressive Disorders in Adulthood .” Journal of Affective Disorders 82 ( 2 ):217–25. [ PubMed ] [ Google Scholar ]
Chassin Laurie, Hussong Andrea, Barrera Manuel, Molina Brooke S. G., Trim Ryan, and Ritter Jennifer. 2004. “Adolescent Substance Use.” Pp. 665–96 in Handbook of Adolescent Psychology , edited by Lerner Richard M. and Steinberg Laurence. Hoboken, NJ: John Wiley. [ Google Scholar ]
Chassin Laurie, Sher Kenneth J., Hussong Andrea, and Curran Patrick. 2013. “ The Developmental Psychopathology of Alcohol Use and Alcohol Disorders: Research Achievements and Future Directions .” Development and Psychopathology 25 ( 4, Pt. 2 ):1567–84. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Cronholm Peter F., Forke Christine M., Wade Roy, Bair-Merritt Megan H., Davis Martha, Harkins-Schwarz Mary, Pachter Lee M., and Fein Joel A.. 2015. “ Adverse Childhood Experiences: Expanding the Concept of Adversity .” American Journal of Preventive Medicine 49 ( 3 ):354–61. [ PubMed ] [ Google Scholar ]
Cyr Chantal, Euser Eveline M., Bakersman-Kranenburg Marian J., and Van Ijzendoorn Marinus H.. 2010. “ Attachment Security and Disorganization in Maltreating and High-risk Families: A Series of Meta-analyses .” Development and Psychopathology 22 ( 1 ):87–108. [ PubMed ] [ Google Scholar ]
Danese Andrea and McEwen Brue S.. 2012. “ Adverse Childhood Experiences, Allostasis, Allostatic Load, and Age-related Disease .” Physiology & Behavior 106 ( 1 ):29–39. [ PubMed ] [ Google Scholar ]
Dong Maxia, Anda Robert F., Felitti Vincent J., Dube Shanta R., Williamson David F., Thompson Theodore J., Loo Clifton M., and Giles Wayne H.. 2004. “ The Interrelatedness of Multiple Forms of Childhood Abuse, Neglect, and Household Dysfunction .” Child Abuse & Neglect 28 ( 7 ):771–84. [ PubMed ] [ Google Scholar ]
Dong Maxia, Dube Shanta R., Felitti Vincent J., Giles Wayne H., and Anda Robert F.. 2003. “ Adverse Childhood Experiences and Self-reported Liver Disease: New Insights into the Causal Pathway .” Archives of Internal Medicine 163 ( 16 ):1949–56. [ PubMed ] [ Google Scholar ]
Dube Shanta R., Anda Robert F., Felitti Vincent J., Chapman Daniel P., Williamson David F., and Giles Wayne H.. 2001. “ Childhood Abuse, Household Dysfunction, and the Risk of Attempted Suicide throughout the Life Span: Findings from the Adverse Childhood Experiences Study .” Journal of the American Medical Association 286 ( 24 ):3089–96. [ PubMed ] [ Google Scholar ]
Dube Shanta R., Felitti Vincent J., Dong Maxia, Chapman Daniel P., Giles Wayne H., and Anda Robert F.. 2003. “ Childhood Abuse, Neglect, and Household Dysfunction and the Risk of Illicit Drug Use: The Adverse Childhood Experiences Study .” Pediatrics 111 ( 3 ):564–72. [ PubMed ] [ Google Scholar ]
Dube Shanta R., Miller Jacqueline W., Brown David W., Giles Wayne H., Felitti Vincent J., Dong Maxia, and Anda Robert F.. 2006. “ Adverse Childhood Experiences and the Association with Ever Using Alcohol and Initiating Alcohol Use during Adolescence .” Journal of Adolescent Health 38 ( 4 ):444. e1–10. [ PubMed ] [ Google Scholar ]
Dupéré Véronique, Leventhal Tama, and Vitaro Frank. 2012. “ Neighborhood Processes, Self-efficacy, and Adolescent Mental Health .” Journal of Health and Social Behavior 53 ( 2 ):183–98. [ PubMed ] [ Google Scholar ]
Edwards Valerie J., Holden George W., Felitti Vincent J., and Anda Robert F.. 2003. “ Relationship between Multiple Forms of Childhood Maltreatment and Adult Mental Health in Community Respondents: Results from the Adverse Childhood Experiences Study .” American Journal of Psychiatry 160 ( 8 ):1453–60. [ PubMed ] [ Google Scholar ]
Enders Craig K. and Bandalos Deborah L.. 2001. “ The Relative Performance of Full Information Maximum Likelihood Estimation for Missing Data in Structural Equation Models .” Structural Equation Modeling 8 ( 3 ):430–57. [ Google Scholar ]
Ennett Susan T. and Bauman Karl E.. 1994. “ The Contribution of Influence and Selection to Adolescent Peer Group Homogeneity: The Case of Adolescent Cigarette Smoking .” Journal of Personality and Social Psychology 67 ( 4 ):653–63. [ PubMed ] [ Google Scholar ]
Fagan Abigail A., Wright Emily M., and Pinchevsky Gillian M.. 2014. “ The Protective Effects of Neighborhood Collective Efficacy on Adolescent Substance Use and Violence Following Exposure to Violence .” Journal of Youth and Adolescence 43 ( 9 ):1498–512. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Felitti Vincent J., Anda Robert F., Nordenberg Dale, Williamson David F., Spitz Alison M., Edwards Valerie, Koss Mary P., and Marks James S.. 1998. “ Relationship of Childhood Abuse and Household Dysfunction to Many of the Leading Causes of Death in Adults: The Adverse Childhood Experiences (ACE) Study .” Journal of Preventive Medicine 14 ( 4 ):245–58. [ PubMed ] [ Google Scholar ]
Fomby Paula and Sennott Christie A.. 2013. “ Family Structure Instability and Mobility: The Consequences for Adolescents’ Problem Behavior .” Social Science Research 42 ( 1 ):186–201. [ PubMed ] [ Google Scholar ]
Hair Elizabeth C., Moore Kristin A., Garrett Sarah B., Ling Thomson, and Cleveland Kevin. 2008. “ The Continued Importance of Quality Parent: Adolescent Relationships during Late Adolescence .” Journal of Research on Adolescence 18 ( 1 ):187–200. [ Google Scholar ]
Harding David J. 2009. “ Violence, Older Peers, and the Socialization of Adolescent Boys in Disadvantaged Neighborhoods .” American Sociological Review 74 ( 3 ):445–64. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Haynie Dana L. 2001. “ Delinquent Peers Revisited: Does Network Structure Matter? ” American Journal of Sociology 106 ( 4 ):1013–57. [ Google Scholar ]
Haynie Dana L. and Osgood D. Wayne. 2005. “ Reconsidering Peers and Delinquency: How Do Peers Matter? ” Social Forces 84 ( 2 ):1109–30. [ Google Scholar ]
Hillis Susan D., Anda Robert F., Dube Shanta R., Felitti Vincent J., Marchbanks Polly A., and Marks James S.. 2004. “ The Association between Adverse Childhood Experiences and Adolescent Pregnancy, Long-term Psychosocial Consequences, and Fetal Death .” Pediatrics 113 ( 2 ):320–27. [ PubMed ] [ Google Scholar ]
Hirschi Travis. 1969. Causes of Delinquency . Berkeley, CA: University of California Press. [ Google Scholar ]
Hoffman Beth R., Monge Peter R., Chou Chih-Ping, and Valente Thomas W.. 2007. “ Perceived Peer Influence and Peer Selection on Adolescent Smoking .” Addictive Behaviors 32 ( 8 ):1546–54. [ PubMed ] [ Google Scholar ]
Hoffmann John P. 2003. “ A Contextual Analysis of Differential Association, Social Control, and Strain Theories of Delinquency .” Social Forces 81 ( 3 ):753–85. [ Google Scholar ]
Hostinar Camelia E., Lachman Margie E., Mroczek Daniel K., Seeman Teresa E., and Miller Gregory E.. 2015. “ Additive Contributions of Childhood Adversity and Recent Stressors to Inflammation at Midlife: Findings from the MIDUS Study .” Developmental Psychology 51 ( 11 ):1630–44. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Jaccard James, Blanton Hart, and Dodge Tonya. 2005. “ Peer Influences on Risk Behavior: An Analysis of the Effects of a Close Friend .” Developmental Psychology 41 ( 1 ):135–47. [ PubMed ] [ Google Scholar ]
Jackson Nicki, Denny Simon, Sheridan Janie, Zhao Jinfeng, and Ameratunga Shanthi. 2016. “ The Role of Neighborhood Disadvantage, Physical Disorder, and Collective Efficacy in Adolescent Alcohol Use: A Multilevel Path Analysis .” Health & Place 41 :24–33. [ PubMed ] [ Google Scholar ]
Jain Sonia, Buka Stephen L., Subramanian SV, and Molnar Beth E.. 2012. “ Protective Factors for Youth Exposed to Violence: Role of Developmental Assets in Building Emotional Resilience .” Youth Violence and Juvenile Justice 10 ( 1 ):107–29. [ Google Scholar ]
Johnston Lloyd D., O’Malley Patrick M., Miech Richard A., Bachman Jerald G., and Schulenberg John E.. 2017. “ Demographic Subgroup Trends among Adolescents in the Use of Various Licit and Illicit Drugs, 1975–2016 .” Monitoring the Future Occasional Paper No. 88 . http://www.monitoringthefuture.org/pubs/occpapers/mtf-occ88.pdf [ Google Scholar ]
Kandel Denise B. 1978. “ Homophily, Selection, and Socialization in Adolescent Friendships .” American Journal of Sociology 84 ( 2 ):427–36. [ Google Scholar ]
Kendall-Tackett Kathleen. 2002. “ The Health Effects of Childhood Abuse: Four Pathways by which Abuse Can Influence Health .” Child Abuse & Neglect 26 ( 6–7 ):715–29. [ PubMed ] [ Google Scholar ]
Kirk David S. and Papachristos Andrew V.. 2011. “ Cultural Mechanisms and the Persistence of Neighborhood Violence .” American Journal of Sociology 116 ( 4 ):1190–233. [ PubMed ] [ Google Scholar ]
Kobus Kimberley. 2003. “ Peers and Adolescent Smoking .” Addiction 98 :37–55. [ PubMed ] [ Google Scholar ]
Krohn Marvin D., Lizotte Alan J., Thornberry Terence P., Smith Carolyn, and McDowall David. 1996. “ Reciprocal Causal Relationships among Drug Use, Peers, and Beliefs: A Five-way Panel Model .” Journal of Drug Issues 26 ( 2 ):405–28. [ Google Scholar ]
Maimon David and Browning Christopher R.. 2010. “ Unstructured Socializing, Collective Efficacy, and Violent Behavior among Urban Youth .” Criminology 48 ( 2 ):443–74. [ Google Scholar ]
Maimon David and Browning Christopher R.. 2012. “ Underage Drinking, Alcohol Sales and Collective Efficacy: Informal Control and Opportunity in the Study of Alcohol Use .” Social Science Research 41 ( 4 ):977–90. [ PubMed ] [ Google Scholar ]
Matsueda Ross L. and Anderson Kathleen. 1998. “ The Dynamics of Delinquent Peers and Delinquent Behavior .” Criminology 36 ( 2 ):269–308. [ Google Scholar ]
Merrick Melissa T., Ford Derek C., Ports Katie A., and Guinn Angie S.. 2018. “ Prevalence of Adverse Childhood Experiences from the 2011–2014 Behavioral Risk Factor Surveillance System in 23 States .” JAMA Pediatrics 172 ( 11 ):1038–44. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Molnar Beth E., Buka Stephen L., Brennan Robert T., Holton John K., and Earls Felton. 2003. “ A Multilevel Study of Neighborhoods and Parent-to-child Physical Aggression: Results from the Project on Human Development in Chicago Neighborhoods .” Child Maltreatment 8 ( 2 ):84–97. [ PubMed ] [ Google Scholar ]
Muthén Linda K. and Muthén Bengt O.. 1998–2012. Mplus User’s Guide . Los Angeles, CA: Muthén & Muthén. [ Google Scholar ]
O’Donnell Deborah A., Schwab-Stone Mary E., and Muyeed Adaline Z.. 2002. “ Multidimensional Resilience in Urban Children Exposed to Community Violence .” Child Development 73 ( 4 ):1265–82. [ PubMed ] [ Google Scholar ]
Olson Julie S. and Crosnoe Robert. 2018. “ The Interplay of Peer, Parent, and Adolescent Drinking .” Social Science Quarterly 99 ( 4 ):1349–62. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Osgood D. Wayne and Anderson Amy L.. 2004. “ Unstructured Socializing and Rates of Delinquency .” Criminology 42 ( 3 ):519–50. [ Google Scholar ]
Osgood D. Wayne, Wilson Janet K., O’Malley Patrick M., Bachman Jerald G., and Johnston Lloyd D.. 1996. “ Routine Activities and Individual Deviant Behavior .” American Sociological Review 61 ( 4 ):635–55. [ Google Scholar ]
Pattillo Mary E. 1998. “ Sweet Mothers and Gangbangers: Managing Crime in a Black Middle-class Neighborhood .” Social Forces 76 ( 3 ):747–74. [ Google Scholar ]
Perez Nicholas M., Jennings Wesley G., and Baglivio Michael T.. 2018. “ A Path to Serious, Violent, Chronic Delinquency: The Harmful Aftermath of Adverse Childhood Experiences .” Crime & Delinquency 64 ( 1 ):3–25. [ Google Scholar ]
Poulin François and Boivin Michel. 2000. “ The Role of Proactive and Reactive Aggression in the Formation and Development of Boys’ Friendships .” Developmental Psychology 36 ( 2 ):233–40. [ PubMed ] [ Google Scholar ]
Roberts Megan E., Brittney Keller-Hamilton Alice Hinton, Browning Christopher R., Slater Michael D., Xi Wenna, and Ferketich Amy K.. 2019. “ The Magnitude and Impact of Tobacco Marketing Exposure in Adolescents’ Day-to-day Lives: An Ecological Momentary Assessment (EMA) Study .” Addictive Behaviors 88 :144–49. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Rothman Emily F., Edwards Erika M., Heeren Timothy, and Hingson Ralph W.. 2008. “ Adverse Childhood Experiences Predict Earlier Age of Drinking Onset: Results from a Representative US Sample of Current or Former Drinkers .” Pediatrics 122 ( 2 ):e298–304. [ PubMed ] [ Google Scholar ]
Sampson Robert J. 2011. Great American City: Chicago and the Enduring Neighborhood Effect . Chicago, IL: University of Chicago Press. [ Google Scholar ]
Sampson Robert J., Morenoff Jeffrey D., and Earls Felton. 1999. “ Beyond Social Capital: Spatial Dynamics of Collective Efficacy for Children .” American Sociological Review 64 ( 5 ):633–60. [ Google Scholar ]
Sampson Robert J., Raudenbush Stephen W., and Earls Felton. 1997. “ Neighborhoods and Violent Crime: A Multilevel Study of Collective Efficacy .” Science 277 ( 5328 ):918–24. [ PubMed ] [ Google Scholar ]
Schulenberg John, Wadsworth Katherine N., O’Malley Patrick M., Bachman Jerald G., and Johnston Lloyd D.. 1996. “ Adolescent Risk Factors for Binge Drinking during the Transition to Young Adulthood: Variable- and Pattern-centered Approaches to Change .” Developmental Psychology 32 ( 4 ):659–74. [ Google Scholar ]
Snedker Karen A., Herting Jerald R., and Walton Emily. 2009. “ Contextual Effects and Adolescent Substance Use: Exploring the Role of Neighborhoods .” Social Science Quarterly 90 ( 5 ):1272–97. [ Google Scholar ]
Stattin Häkan and Kerr Margaret. 2000. “ Parental Monitoring: A Reinterpretation .” Child Development 71 ( 4 ):1072–85. [ PubMed ] [ Google Scholar ]
Thornberry Terence P. 1987. “ Toward an Interactional Theory of Delinquency .” Criminology 25 ( 4 ):863–92. [ Google Scholar ]
Tobler Amy L., Komro Kelli A., and Maldonado-Molina Mildred M.. 2009. “ Early Adolescent, Multi-ethnic, Urban Youth’s Exposure to Patterns of Alcohol-related Neighborhood Characteristics .” Journal of Community Health 34 ( 5 ):361–69. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Tucker Joan S., Pollard Michael S., de la Haye Kayla, Kennedy David P., and Green Harold D. Jr. 2013. “ Neighborhood Characteristics and the Initiation of Marijuana Use and Binge Drinking .” Drug and Alcohol Dependence 128 ( 1–2 ):83–89. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Weerman Frank M., Wilcox Pamela, and Sullivan Christopher J.. 2018. “ The Short-term Dynamics of Peers and Delinquent Behavior: An Analysis of Bi-weekly Changes within a High School Student Network .” Journal of Quantitative Criminology 34 ( 2 ):431–63. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Wills Thomas A. and Cleary Sean D.. 1999. “ Peer and Adolescent Substance Use among 6th–9th Graders: Latent Growth Analyses of Influence versus Selection Mechanisms .” Health Psychology 18 ( 5 ):453–63. [ PubMed ] [ Google Scholar ]
Wills Thomas A. and Dishion Thomas J.. 2004. “ Temperament and Adolescent Substance Use: A Transactional Analysis of Emerging Self-control .” Journal of Clinical Child and Adolescent Psychology 33 ( 1 ):69–81. [ PubMed ] [ Google Scholar ]
Yonas Michael A., Lewis Terri, Hussey Jon M., Thompson Richard, Newton Rae, English Diana, and Dubowitz Howard. 2010. “ Perceptions of Neighborhood Collective Efficacy Moderate the Impact of Maltreatment on Aggression .” Child Maltreatment 15 ( 1 ):37–47. [ PubMed ] [ Google Scholar ]
Zimmerman Gregory M. and Farrell Chelsea. 2017. “ Parents, Peers, Perceived Risk of Harm, and the Neighborhood: Contextualizing Key Influences on Adolescent Substance Use .” Journal of Youth and Adolescence 46 ( 1 ):228–47. [ PubMed ] [ Google Scholar ]

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This case study is a publication of the National Academy for State Health Policy (NASHP). This project is supported by the Health Resources and Services Administration ... "Principles of Adolescent Substance Use Disorder Treatment: A Research -Based Guide" 6 "Teen Substance Use and Risks," Department of Health and Human Services ...
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Family-based Treatments for Adolescent Substance Use
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The researchers used a qualitative case study design which focused on an issue of concern (such as adolescent substance abuse) and thus selected one case to elucidate the issue, i.e., a single case study. ... The impact of substance use disorder on families and children: From theory to practice. Social Work in Public Health, 28, 194-205 ...
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Price. $20.00. Format. Interactive Module. Target Audience. Interprofessionals. Registered Nurses. Description. This Behavioral Health in Primary Care (BHPC) case study aids in assessing and developing a care plan for an adolescent presenting with a substance use disorder in primary care.
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Substance use disorders are among the commonest mental disorders in childhood and adolescence. In Germany, approximately 10% of adolescents have tried cannabis at least once. The prognosis is negatively affected by individual (bio-)psychological traits, mental comorbidities, laws that facilitate consumption, socioeconomic disadvantage ...
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Peer and Community Influences on Adolescent Substance Use in the
Mediating Role of Peer Influences in Substance Use. Given that peer influence is one of the strongest and most consistent predictors of adolescent substance use (Kobus 2003; Wills and Cleary 1999), it is plausible that the association between ACEs and substance use operates in part through peer settings.Two dimensions of peer influences commonly studied are the direct normative influence of ...

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1. Theoretical and Research Basis for Treatment

2. Case Introduction

3. Diagnostic Criteria

4. Psychosocial, Medical and Family History

5. Case Conceptualisation

6. Course of Treatment and Assessment of Progress

7. Access and Barriers to Care

8. Treatment

9. Assessment of Treatment Outcome

10. Follow-up

11. Complicating Factors with A-CRA Model

12. Treatment Implications of the Case

13. Recommendations to Clinicians and Students

Author Contributions

Acknowledgments

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What You'll Learn

Why are kids with mental health disorders prone to substance use?

Why is alcohol use riskier for teenagers?

Why teenagers get addicted sooner

Why substance use makes depression and anxiety worse

Psychosis and substance use

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Recent Trends in Mental Health and Substance Use Concerns Among Adolescents

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How have suicide and self-harm among adolescents changed in recent years?

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Unmet need for mental health services

What experiences among adolescents may negatively impact their mental health and well-being?

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Also of Interest

Adolescent Motivational Interviewing

Using Motivational Interviewing to Treat Adolescents and Young Adults with Substance Use Disorders

"These kids are even more likely to drop out than other kids, and for them, the risk of going back out and using drugs can be fatal."

"Motivational Interviewing is helping our patients because it reduces many of the impulsive decisions and encourages them to think through their actions before doing them."

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Substance Use Treatment Center (SUD) Case Study: 14 year old in IOP for alcohol use disorder and vaping

Screen for Tobacco Use With Every Youth Age 11+ at Every Clinical Encounter.

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Harnessing science, love and the wisdom of lived experience, we are a force of healing and hope for individuals, families and communities affected by substance use and mental health conditions.