Substance abuse and personality disorder comorbidity in adolescent outpatients: Are girls more severely ill than boys?

Background

Substance use disorders (SUDs) constitute a major health problem and are associated with an extensive psychiatric comorbidity. Personality disorders (PDs) and SUDs commonly co-occur. Comorbid PD is characterized by more severe addiction problems and by an unfavorable clinical outcome. The present study investigated the prevalence of SUDs, PDs and common Axis I disorders in a sample of adolescent outpatients. We also investigated the association between PDs and SUDs, and how this association was influenced by adjustment for other Axis I disorders, age and gender.

Methods

The sample consisted of 153 adolescents, aged 14–17 years, who were referred to a non-specialized mental health outpatient clinic with a defined catchment area. SUDs and other Axis I conditions were assessed using the mini international neuropsychiatric interview. PDs were assessed using the structured interview for DSM-IV personality.

Results

18.3 % of the adolescents screened positive for a SUD, with no significant gender difference. There was a highly significant association between number of PD symptoms and having one or more SUDs; this relationship was practically unchanged by adjustment for gender, age and presence of Axis I disorders. For boys, no significant associations between SUDs and specific PDs, conduct disorder (CD) or attention deficit hyperactivity disorder (ADHD) were found. For girls, there were significant associations between SUD and BPD, negativistic PD, more than one PD, CD and ADHD.

Conclusions

We found no significant gender difference in the prevalence of SUD in a sample of adolescents referred to a general mental health outpatient clinic. The association between number of PD symptoms and having one or more SUDs was practically unchanged by adjustment for gender, age and presence of one or more Axis I disorders, which suggested that having an increased number of PD symptoms in itself may constitute a risk factor for developing SUDs in adolescence. The association in girls between SUDs and PDs, CD and ADHD raises the question if adolescent girls suffering from these conditions may be especially at risk for developing SUDs. In clinical settings, they should therefore be monitored with particular diligence with regard to their use of psychoactive substances.

Below:  PD symptoms in adolescents with SUD and other Axis I disorders. PD Symptoms any PD criteria meeting a score of 1,2 or 3 when measured with the SIDP-IV; SUD substance use disorders; alcohol and/or drug abuse or dependence. Alcohol alcohol abuse or dependence; CannabisCannabis abuse or dependence; Anxiety anxiety disorders, simple phobias, generalized anxiety disorder, panic disorder, agoraphobia, social phobia and post-traumatic stress disorder; Mood mood disorders, dysthymia and major depressive episode; CD conduct disorder; ADHDattention deficit hyperactivity disorder. ** p < 0.05

Full article at:   http://goo.gl/szh4O2

By:  Hans Ole Korsgaard, Svenn Torgersen, Tore Wentzel-Larsen, and Randi Ulberg

Department for Child and Adolescent Mental Health (The Nic Waal Institute), Lovisenberg Diakonale Hospital, Oslo, Norway

Department of Psychology, University of Oslo, Oslo, Norway

Centre for Child and Adolescent Mental Health, Eastern and Southern Norway, Oslo, Norway

Norwegian Centre for Violence and Traumatic Stress Studies, Oslo, Norway

Vestfold Hospital Trust, Tønsberg, Norway

Institute of Clinical Medicine, University of Oslo, Oslo, Norway

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Psychiatric Comorbidity and Substance Use Outcomes in an Office-Based Buprenorphine Program Six Months Following Hurricane Sandy

BACKGROUND:

On October 2012, Hurricane Sandy struck New York City, resulting in unprecedented damages, including the temporary closure of Bellevue Hospital Center and its primary care office-based buprenorphine program.

OBJECTIVES:

At 6 months, we assessed factors associated with higher rates of substance use in buprenorphine program participants that completed a baseline survey one month post-Sandy (i.e. shorter length of time in treatment, exposure to storm losses, a pre-storm history of positive opiate urine drug screens, and post-disaster psychiatric symptoms).

METHODOLOGY:

Risk factors of interest extracted from the electronic medical records included pre-disaster diagnosis of Axis I and/or II disorders and length of treatment up to the disaster. Factors collected from the baseline survey conducted approximately one month post-Sandy included self-reported buprenorphine supply disruption, health insurance status, disaster exposure, and post-Sandy screenings for PTSD and depression. Outcome variables reviewed 6 months post-Sandy included missed appointments, urine drug results for opioids, cocaine, and benzodiazepines.

RESULTS:

129 (98%) patients remained in treatment at 6 months, and had no sustained increases in opioid-, cocaine-, and benzodiazepine-positive urine drug tests in any sub-groups with elevated substance use in the baseline survey. Contrary to our initial hypothesis, diagnosis of Axis I and/or II disorders pre-Sandy were associated with significantly less opioid-positive urine drug findings in the 6 months following Sandy compared to the rest of the clinic population.

CONCLUSION:

These findings demonstrate the adaptability of a safety net buprenorphine program to ensure positive treatment outcomes despite disaster-related factors.

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By:  Tofighi B1, Grossman E2, Goldfeld KS1, Williams AR1,3, Rotrosen J3, Lee JD1,2.

• 1a Department of Population Health , New York University School of Medicine , New York , New York , USA.
• 2b Division of General Internal Medicine , New York University School of Medicine , New York , New York , USA.
• 3c Department of Psychiatry , New York University School of Medicine , New York , New York , USA. 

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Psychiatric Services and Prescription Fills among Veterans with Serious Mental Illness in Methadone Maintenance Treatment

Comorbidity and co-prescription patterns of people with serious mental illness in methadone maintenance may complicate theirtreatment and have not been studied. The goal of this study was to examine the care and characteristics of people with serious mental illness inmethadone maintenance treatment nationally in the Veterans Health Administration (VHA).

Using national VHA data from FY2012, bivariate and multiple logistic regression analyses were used to compare veterans in methadonemaintenance treatment wo had a serious mental illness (schizophrenia, bipolar disorder, or major affective disorder) to patients in methadonemaintenance treatment without serious mental illness and patients with serious mental illness who were not in methadone maintenance treatment.

Only a small fraction of patients with serious mental illness were receiving methadone maintenance treatment (0.65%), but a relatively large proportion in methadone maintenance treatment had a serious mental illness (33.2%). Compared to patients without serious mental illness, patients with serious mental illness in methadone maintenance treatment were more likely to have been homeless, to have had a recent psychiatric hospitalization, to be over 50% disabled, and to have had more fills for more classes of psychotropic drugs. Compared to other patients with serious mental illness, patients with serious mental illness in methadone maintenance treatment were more likely to have a drug abuse diagnosis and to reside in large urban areas.

One-third of patients in methadone maintenance treatment have serious mental illness and more frequent psychiatric comorbidity, and they are more likely to use psychiatric and general health services and fill more types of psychiatric prescriptions. Further study and clinical awareness of potential drug-drug interactions in this high medication and service using population are needed.

Purchase full article at:  http://goo.gl/raaaew

By:  Marienfeld C, Rosenheck RA.

 

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Crime & Psychiatric Disorders among Youth in the US Population: An Analysis of the National Comorbidity Survey-Adolescent Supplement

Current knowledge regarding psychiatric disorders and crime in youth is limited to juvenile justice and community samples. This study examined relationships between psychiatric disorders and self-reported crime involvement in a sample of youth representative of the US population.

The National Comorbidity Survey-Adolescent Supplement (N=10,123; ages 13–17; 2001–2004) was used to examine the relationship between lifetime DSM-IV-based diagnoses, reported crime (property, violent, other), and arrest history. Logistic regression compared the odds of reported crime involvement with specific psychiatric disorders to those without any diagnoses, and examined the odds of crime by psychiatric comorbidity.

Prevalence of crime was 18.4%. Youth with lifetime psychiatric disorders, compared to no disorders, had significantly greater odds of crime, including violent crime. For violent crime resulting in arrest, conduct disorder, alcohol use disorders, and drug use disorders had the greatest odds with similar findings for violent crime with no arrest. Psychiatric comorbidity increased the odds of crime. Youth with 3 or more diagnoses (16.0% of population) accounted for 54.1% of those reporting arrest for violent crime. Youth with at least 1 diagnosis committed 85.8% of crime, which was reduced to 67.9% by removing those with CD. Importantly, 88.2% of youth with mental illness report never committing any crime.

Our findings highlight the importance of improving access to mental health services for youthful offenders in community settings given the substantial associations found between mental illness and crime in this nationally representative epidemiological sample.

Below:  Percentages of crime accounted for by those with varying numbers of psychiatric diagnoses, relative to population prevalence. Note: Estimates were calculated using logistic regression, accounting for the survey design. Results showed that despite making up a smaller portion of the total population, adolescents with substantial psychiatric comorbidity accounted for a much larger portion of reported crime. For example, those with no psychiatric diagnoses made up over 50% of the population, and accounted for 15.8% of those never arrested who committed violent crime, whereas those with 3 or more diagnoses made up only 16.0% of the population, and accounted for 48.4% of those never arrested who committed violent crime.

Below:  Population attributable fraction (PAF) of those who committed any crime, by number of diagnoses. Note: results are presented both with and without those with conduct disorder (CD) included in the sample. One could expect up to 86% of crime to be reduced if there were no mental illness (68% when those with CD were eliminated from the sample). PAF calculated using the following formula: PAF = Pe(RRe – 1)/[1 + Pe(RRe – 1)], where Pe is the prevalence of the exposure group and RRe is the relative risk associated with the exposure group. To obtain RRe, odds ratios were calculated using logistic regression, accounting for the survey design and adjusting for income, age, gender, and race/ethnicity. These odds ratios (ORe) were then converted to RRe using the following formula: RRe = ORe/[(l — P0) + (P0 * ORe)], where P0 is the prevalence of the outcome in the non-exposed group (0 diagnoses).

Table 1

Frequencies for Specific Disorders, Psychiatric Comorbidity Subgroups, and Crime Outcomes (N = 10,123).

	Total sample (N = 10,123)
	n	%
Attention/Disruptive
Behavior/Impulse Control
Disorders
ADHD	432	4.15
CD	586	5.44
Without alcohol or Drug abuse	278	2.65
With alcohol or drug Abuse	308	2.79
IED	1,389	14.09
ODD	1,047	10.14
Affective Disorders
Bipolar (I or II)	231	2.27
Dysthymia	335	0.34
MDD	1,123	10.82
Anxiety Disorders
Agoraphobia	293	2.66
GAD	298	3.19
Panic Disorder	238	2.35
PTSD	388	4.01
SAD	772	7.63
Social Phobia	1,434	14.46
Eating Disorders
Any Binge Disorder	532	4.95
SUDsa
Alcohol Use Disorders	678	6.43
Drug Use Disorders	880	8.93
Nicotine Dependence	713	7.04
Number of diagnoses
0	5,402	52.6
1	2,009	20.6
2	1,110	10.9
3+	1,627	15.96
Crime
Arrested
Property/theft/burglary	281	2.89
Violent	175	1.74
Other	420	4.03
Never Arrestedb
Property/theft/burglary	621	6.55
Violent	185	1.71
Other	671	7.29

Note: Percentages accounted for the survey design. The listed diagnoses are lifetime prevalence. ADHD = attention-deficit/hyperactivity disorder; CD = conduct disorder; GAD = Generalized anxiety disorder; IED = intermittent explosive disorder; MDD = major depressive disorder; ODD = oppositional defiant disorder; PTSD = posttraumatic stress disorder, SAD = separation anxiety disorder; SUDs = substance use disorders.

aAlcohol Use Disorders (Abuse+Dependence); Drug Use Disorders (Abuse+Dependence-note the Composite International Diagnostic Interview [CIDI] skip patterns do not assess dependence in the absence of abuse); Nicotine Dependence (without alcohol or drug use disorder).

bn=9,397

Full article at: http://goo.gl/SywIoY

By: Kendell L. Coker, PhD, JD, Philip H. Smith, PhD, Alexander Westphal, MD, PhD, Howard V. Zonana, MD, and Sherry A. McKee, PhD

Drs. Coker, Smith, Westphal, Zonana, and McKee are with Yale University School of Medicine, New Haven, CT. Dr. Smith is also with the School of Public Health at Yale University School of Medicine. Dr. Westphal is also with the Yale Child Study Center

  

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Clinical Needs of Patients with Problem Drug Use

Illicit drug use is a serious public health problem associated with significant co-occurring medical disorders, mental disorders, and social problems. Yet most individuals with drug use disorders have never been treated, though they often seek medical treatment in primary care. The purpose of this study was to examine the baseline characteristics of people presenting in primary care with a range of problem drug use severity to identify their clinical needs.

We examined sociodemographic characteristics, medical and psychiatric comorbidities, drug use severity, social and legal problems, and service utilization for 868 patients with drug problems. These patients were recruited from primary care clinics in a medical safety net setting. Based on Drug Abuse Screening Test results, individuals were categorized as having low, intermediate, or substantial/severe drug use severity.

Patients with substantial/severe drug use severity had serious drug use (opiates, stimulants, sedatives, intravenous drugs); high levels of homelessness (50%), psychiatric comorbidity (69%), and arrests for serious crimes (24%); and frequent use of expensive emergency department and inpatient hospitals. Patients with low drug use severity were primarily users of marijuana, with little reported use of other drugs, less psychiatric comorbidity, and more stable lifestyles. Patients with intermediate drug use severity fell in between the substantial/severe and low drug use severity subgroups on most variables.

Patients with the highest drug use severity are likely to require specialized psychiatric and substance abuse care, in addition to ongoing medical care that is equipped to address the consequences of severe/substantial drug use, including intravenous drug use. Because of their milder symptoms, patients with low drug use severity may benefit from a collaborative care model that integrates psychiatric and substance abuse care in the primary care setting. Patients with intermediate drug use severity may benefit from selective application of interventions suggested for patients with the highest and lowest drug use severity. Primary care safety net clinics are in a key position to serve patients with problem drug use by developing a range of responses that are locally effective and that may also inform national efforts to establish patient-centered medical homes and to implement the Affordable Care Act.

Full article at: http://goo.gl/9PHMze

By: Krupski A1, West II2, Graves MC2, Atkins DC2, Maynard C2, Bumgardner K2, Donovan D2, Ries R2, Roy-Byrne P2.

• 1From the Department of Psychiatry and Behavioral Sciences, School of Medicine, University of Washington at Harborview Medical Center, Seattle (AK, IIW, MCG, DCA, KB, DD, RR, PR-B); the Department of Health Services, University of Washington School of Public Health, Seattle (CM); and the Alcohol & Drug Abuse Institute, University of Washington, Seattle (DD). krupski@uw.edu.
• 2From the Department of Psychiatry and Behavioral Sciences, School of Medicine, University of Washington at Harborview Medical Center, Seattle (AK, IIW, MCG, DCA, KB, DD, RR, PR-B); the Department of Health Services, University of Washington School of Public Health, Seattle (CM); and the Alcohol & Drug Abuse Institute, University of Washington, Seattle (DD). 

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