Network Analysis of DSM Symptoms of Substance Use Disorders and Frequently Co-Occurring Mental Disorders in Patients with Substance Use Disorder Who Seek Treatment.
ADHD
borderline personality
comorbidity
conduct disorder
gender differences
network analysis
personality disorders
substance use disorders
Journal
Journal of clinical medicine
ISSN: 2077-0383
Titre abrégé: J Clin Med
Pays: Switzerland
ID NLM: 101606588
Informations de publication
Date de publication:
19 May 2022
19 May 2022
Historique:
received:
11
04
2022
revised:
10
05
2022
accepted:
15
05
2022
entrez:
28
5
2022
pubmed:
29
5
2022
medline:
29
5
2022
Statut:
epublish
Résumé
Substance use disorders (SUD) often co-occur with other psychiatric conditions. Research on SUD and comorbid disorders generally flows from a categorical diagnostic or dimensional latent variable perspective, where symptoms are viewed as independent indicators of an underlying disorder. In contrast, the current study took a network analysis perspective to examine the relationships between DSM symptoms of SUD, ADHD, conduct disorder (CD), depression (MDD), and borderline personality disorder (BPD). In addition, we explored possible gender differences in the network structures of these symptoms. In a sample of 722 adult treatment-seeking patients with SUD from the International ADHD in Substance Use Disorders Prevalence Study (IASP) we estimated the network structure for 41 symptoms of SUD, ADHD, CD, MDD, and BPD. We described the structure of symptom networks and their characteristics for the total sample, and we compared the symptom networks for males and females. Network analyses identified seven clusters of symptoms, largely corresponding with the DSM diagnostic categories. There were some connections between clusters, mainly between some hyperactivity symptoms and CD and depressive symptoms. ADHD hyperactivity was most central in the symptom network. Invariance tests revealed no significant gender differences in the structure of symptom networks. The current findings support the categorical DSM classification of mental disorders in treatment-seeking patients with SUD. Future network analyses should include a broader range of symptoms and prospectively explore changes in the symptoms network of patients during treatment.
Sections du résumé
BACKGROUND
BACKGROUND
Substance use disorders (SUD) often co-occur with other psychiatric conditions. Research on SUD and comorbid disorders generally flows from a categorical diagnostic or dimensional latent variable perspective, where symptoms are viewed as independent indicators of an underlying disorder. In contrast, the current study took a network analysis perspective to examine the relationships between DSM symptoms of SUD, ADHD, conduct disorder (CD), depression (MDD), and borderline personality disorder (BPD). In addition, we explored possible gender differences in the network structures of these symptoms.
METHOD
METHODS
In a sample of 722 adult treatment-seeking patients with SUD from the International ADHD in Substance Use Disorders Prevalence Study (IASP) we estimated the network structure for 41 symptoms of SUD, ADHD, CD, MDD, and BPD. We described the structure of symptom networks and their characteristics for the total sample, and we compared the symptom networks for males and females.
RESULTS
RESULTS
Network analyses identified seven clusters of symptoms, largely corresponding with the DSM diagnostic categories. There were some connections between clusters, mainly between some hyperactivity symptoms and CD and depressive symptoms. ADHD hyperactivity was most central in the symptom network. Invariance tests revealed no significant gender differences in the structure of symptom networks.
CONCLUSIONS
CONCLUSIONS
The current findings support the categorical DSM classification of mental disorders in treatment-seeking patients with SUD. Future network analyses should include a broader range of symptoms and prospectively explore changes in the symptoms network of patients during treatment.
Identifiants
pubmed: 35629008
pii: jcm11102883
doi: 10.3390/jcm11102883
pmc: PMC9145186
pii:
doi:
Types de publication
Journal Article
Langues
eng
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