DSM-IV Axis-I comorbidity among illicit drug users seeking treatment for substance use disorders: results from the Multi-centre Study of Psychiatric Comorbidity in Drug Addicts (MUPCDA)

2010 ◽  
Vol 3 (3) ◽  
pp. 182-192 ◽  
Author(s):  
Rainer Thomasius ◽  
Peter-Michael Sack ◽  
Kay Uwe Petersen
Keyword(s):  
Substance Use ◽  
Substance Use Disorders ◽  
Drug Users ◽  
Psychiatric Comorbidity ◽  
Illicit Drug ◽  
Drug Addicts ◽  
Multi Centre Study ◽  
Centre Study ◽  
Axis I ◽  
Dsm Iv

Comparative study of psychiatric comorbidity differences in patients with ADHD and cocaine substance use disorders and patients ADHD and cannabis use disorders

2017 ◽  
Vol 41 (S1) ◽  
pp. S477-S477
Author(s):  
N. Martínez-Luna ◽  
L. Rodriguez-Cintas ◽  
C. Daigre ◽  
L. Grau-Lopez ◽  
R.F. Palma-Alvarez ◽  
...  
Keyword(s):  
Substance Use ◽  
Substance Use Disorders ◽  
Drug Users ◽  
Psychiatric Comorbidity ◽  
Psychotic Disorders ◽  
Neuronal Damage ◽  
Total Sample ◽  
Cocaine Users ◽  
The Difference ◽  
Competing Interest

Substance Use Disorders (SUD) and Attention Deficit Hyperactive Disorder (ADHD) are frequent conditions in out drug treatment centers. There are evidences about the high prevalence of ADHD in SUD patients (20%) compared with just ADHD in general population (1–7.3%). Both disorders and psychiatric comorbidity are important in the diagnosis proceeding. The objective of this study is search the difference in psychiatric comorbidity conditions between patients with ADHD and Cocaine SUD and ADHD and Cannabis SUD. ADHD was present in 158 patients of a total sample in which 46,8% used cocaine, 17.1% cannabis and 36.1% used both. Mood disorders were 26.8% in cocaine users, 21.7% in cannabis and 18.9% in both. Anxiety disorders were 20.3% in cocaine users, 37.5 in cannabis and 13% in both users. Primary psychotic disorders were 2.9% in cocaine users, none in cannabis and 11,1% in both drug users. Personality disorders by cluster were, Cluster A: 11.3% in cocaine group, 36% in cannabis group and 24.5 in cannabis and cocaine group. Cluster B: 33.8% in cocaine group, 44% in cannabis group and 51.9% in cannabis and cocaine group. Cluster C: 9.9% in cocaine group, 28% in cannabis group and 19.2% in cannabis and cocaine group. There could be common pathways of neuronal damage related to psychiatric comorbidity depending of used drug, the differences in comorbidity found in this study could explain a little part of it. It is important to manage SUD-ADHD and other psychiatric comorbidity in order to improve the outcomes of these patients.Disclosure of interestThe authors have not supplied their declaration of competing interest.

scholarly journals Diagnosing comorbidity: concepts, criteria, and methods

2004 ◽  
Vol 16 (1) ◽  
pp. 9-18 ◽  
Author(s):  
S. Samet ◽  
E. V. Nunes ◽  
D. Hasin
Keyword(s):  
Substance Use ◽  
Substance Use Disorders ◽  
Drug Users ◽  
Psychiatric Symptoms ◽  
Alcohol Intoxication ◽  
Psychiatric Research ◽  
Diagnostic Instruments ◽  
Substance Disorders ◽  
Research Interview ◽  
Dsm Iv

Background:The clinical and etiologic implications of comorbid psychiatric and substance-use disorders are relevant across countries and cultures. The DSM-IV now places greater emphasis on the clinical and research utility of the substance-induced disorders classification, and clarifies several important diagnostic issues specific to primary and substance-induced disorders. However, no research consensus exists over the core problem of identifying and differentiating the drug and alcohol intoxication and withdrawal symptoms that can mimic psychiatric symptoms in heavy drinkers and drug users.Objective:To investigate how various diagnostic instruments have measured comorbid psychiatric and substance-use disorders and how each instrument operationalizes the DSM-IV classification.Method:We review the evolution of the concept of comorbidity beginning with its formalization as the ‘primary–secondary’ distinction in the Feighner Criteria. We address the ‘organic–non-organic’ distinction found in the RDC, DSM-III, and DSM-III-R; and finally, review the ‘primary’ and ‘substance-induced’ categories of DSM-IV, DSM-IV-TR and ICD-10. We describe how these distinctions have been operationalized in widely used diagnostic instruments.Conclusion:Further understanding of these classifications and the relationship of co-occurring psychiatric and substance disorders can be accomplished with the range of available measures, particularly the Psychiatric Research Interview for Substance and Mental Disorders (PRISM), which reliably utilizes and refines DSM-IV classification distinctions.

scholarly journals Prospective relationships of ADHD symptoms with developing substance use in a population-derived sample

2011 ◽  
Vol 41 (12) ◽  
pp. 2615-2623 ◽  
Author(s):  
E. Sihvola ◽  
R. J. Rose ◽  
D. M. Dick ◽  
T. Korhonen ◽  
L. Pulkkinen ◽  
...  
Keyword(s):  
Substance Use ◽  
Alcohol Use ◽  
Drug Use ◽  
Rating Scales ◽  
Illicit Drug ◽  
Illicit Drug Use ◽  
Adhd Symptoms ◽  
Co Morbidity ◽  
Axis I ◽  
Dsm Iv

BackgroundClinically ascertained reports suggest that boys and girls with attention deficit hyperactivity disorder (ADHD) may differ from each other in their vulnerability to substance use problems.MethodA total of 1545 Finnish adolescents were assessed for DSM-IV-based ADHD symptoms by their parents and classroom teachers using standardized rating scales at age 11–12 years. At age 14, substance use disorders and psychiatric co-morbidity were assessed with the Semi-Structured Assessment for the Genetics of Alcoholism, providing DSM-III-R/DSM-IV diagnoses for Axis I disorders. At age 17.5, substance use was assessed by multi-item questionnaire.ResultsAlthough baseline ADHD symptoms were less common among females, they were more predictive of adverse substance use outcomes once conduct disorder and previous substance use were controlled for. Only in females were baseline ADHD symptoms significant predictors of alcohol abuse and dependence and illicit drug use at age 14. At the age of 17.5, parents' reports of inattentiveness and hyperactivity were significant predictors for frequent alcohol use in both sexes, but they were more predictive of frequent alcohol and illicit drug use in girls. Impulsivity in teachers' ratings predicted frequent alcohol use and illicit drug use in boys. Parental reports of inattentiveness in their 11-/12-year-old daughters were a consistent predictor for illicit drug use across adolescence.ConclusionsInattentiveness and hyperactivity may be more predictive of alcohol use disorders and maladaptive patterns of alcohol and illicit drug use among girls than boys. The importance of these behavioural symptoms should be assessed further in the community, as they could jeopardize adolescents' successful transitioning into adult roles.

scholarly journals Axis I comorbidity in adolescent inpatients referred for treatment of substance use disorders

2010 ◽  
Vol 4 (1) ◽  
Author(s):  
Tobias Langenbach ◽  
Alexandra Spönlein ◽  
Eva Overfeld ◽  
Gaby Wiltfang ◽  
Niklas Quecke ◽  
...  
Keyword(s):  
Substance Use ◽  
Substance Use Disorders ◽  
Axis I ◽  
Adolescent Inpatients

scholarly journals Estimating the number of children in households with substance use disorders

2020 ◽  
Author(s):  
Ludwig Kraus ◽  
Alfred Uhl ◽  
Josefine Atzendorf ◽  
Nicki-Nils Seitz
Keyword(s):  
Substance Use ◽  
Substance Use Disorders ◽  
Illicit Drugs ◽  
Close Contact ◽  
Epidemiological Survey ◽  
Related Disorder ◽  
Number Of Children ◽  
Dsm Iv ◽  
Household Members ◽  
Definition Of

Abstract Background Parental substance misuse is reported to endanger the health and psychological development of children and adolescents. The aim of the present study was to estimate the number of children currently affected by substance use disorders (SUDs) of any adult living in the same household. Methods Data came from the 2018 German Epidemiological Survey of Substance Abuse (ESA) among 18-64-year-olds (n = 9,267) and from population statistics. DSM-IV diagnostic criteria were used to assess SUD (abuse or dependence) related to tobacco, alcohol, cannabis, cocaine or amphetamine. Based on the number of household members, the number of children below the age 18 years and the information on SUD status of the respondent living in this household, the number of children in households with at least one member with SUD was estimated. Results In 2018, there were 13,597,428 children younger than 18 years living in Germany. Of these, 5.2–7.9% (686,482–696,279) were estimated to live in households where at least one adult had an alcohol-related disorder, 5.0–7.4% (674,065–690,792) in households where at least one adult was tobacco dependent and 0.38–1.05% (93,229–142,141) in households where at least one adult had a disorder related to the use of illicit drugs. The total number of children in close contact with SUD adults was estimated at 7.4–11.2% (1,000,725–1,522,667). Conclusions The large number of children affected by adults with SUD has implications for identification and prevention. Substantial variation in estimates, resulting from differences in methods, definition of exposure and exposure to whom, calls for international standardization in order to make estimates comparable.

Risk classes of patients diagnosed with substance use disorders in Dutch forensic psychiatric centers

2021 ◽  
Vol 23 (1) ◽  
pp. 39-52
Author(s):  
Michele Schmitter ◽  
Jeroen Vermunt ◽  
Eric Blaauw ◽  
Stefan Bogaerts
Keyword(s):  
Substance Use ◽  
Substance Use Disorders ◽  
Latent Class ◽  
Mental Health Problems ◽  
Treatment Approach ◽  
Future Research ◽  
Content Type ◽  
Forensic Patients ◽  
Axis I ◽  
Forensic Psychiatric

Purpose Given the complex association between substance use disorders (SUD), comorbid mental health problems and criminal recidivism in forensic patients, homogenous patient classes can contribute to a refined treatment. This paper aims to construct those classes in forensic patients (N = 286) diagnosed with SUD, unconditionally released between 2004 and 2013 of one of ten Dutch forensic psychiatric centers. Design/methodology/approach Retrospective data were derived from electronic patient files. Classes were based on the Dutch risk assessment tool, the Historisch Klinisch Toekomst-Revisie (Historical Clinical Future–Revised [HKT-R]) and identified by means of explorative Latent Class Analysis in Latent Gold version 5.1. In a three-step approach, posterior class memberships were related to external variables (i.e. diagnoses, type of drug and type of offence). Findings Four classes were identified that differ in the risk of recidivism, as well as Axis I and II diagnoses and type of drug consumption. Practical implications This study informed on the heterogeneity of forensic patients with SUD and identified four homogenous classes that differ in important variables for the treatment approach. Based on these classes, a more refined treatment approach can be developed. Possible treatment approaches are discussed, but future research is needed to provide evidence. Originality/value This study is the first to identify classes within forensic patients with SUD and, therefore, sets the first step to develop a tailored treatment approach based on characteristics informative for treatment.

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