Treatment Receipt Patterns among Individuals with Co-Occurring Posttraumatic Stress Disorder (PTSD) and Substance Use Disorders.
Journal of consulting and clinical psychology(2020)
摘要
Objective: To determine latent classes of treatment receipt among people with comorbid posttraumatic stress disorder (PTSD) and substance use disorder (SUD) and describe each class by demographics, disease characteristics, and psychiatric diagnoses. Method: Participants were National Epidemiologic Survey on Alcohol and Related Conditions-III respondents with lifetime PTSD and SUD (n = 1,349; mean age 40.3; 62.5% female; 30.9% non-White or Hispanic-White). Cross-sectional data were collected using the DSM-5 Alcohol Use Disorder and Associated Disabilities Interview Schedule. Latent class analysis was used to identify subgroups of participants with different patterns of treatment receipt. Results: Of the patients, 36% received at least 1 SUD treatment while 84% received at least 1 mental health (MH) treatment. Six latent classes were identified: no treatment (17.3%), outpatient MH (34.0%), outpatient + inpatient MH (17.9%), SUD (7.3%), SUD + outpatient MH (15.7%), and SUD + outpatient MH + inpatient MH (7.7%). The SUD treatment classes evidenced greater social instability, had higher alcohol use disorder symptom severity, and used more drug types than the non-SUD classes. Classes receiving inpatient MH treatment had a greater incidence of additional comorbid conditions and suicidal behaviors. Across all 6 classes, most respondents met diagnostic criteria for chronic PTSD (overall: 68.9%) while fewer met diagnostic criteria for chronic SUD (overall: 38.7%). Conclusions: Most people with lifetime PTSD and SUD have sought either SUD or MH treatment or both, with substantially greater receipt of MH treatment. This comorbid group has complex clinical presentations that differ depending upon treatment subgroup, and for most, their PTSD persisted despite high rates of treatment engagement.
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关键词
PTSD,substance use disorder,treatment,epidemiological,latent class analysis
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