September 2021Posttraumatic Stress Disorder Symptomatology and Substance Use in an

Posttraumatic Stress Disorder Symptomatology and Substance Use in an Outpatient Concurrent Disorders SampleSymptomatologie du trouble de stress post-traumatique et de l’utilisation de substances dans un échantillon ambulatoire de troubles co-occurrentsHerry Patel, MSc¹, Katherine Holshausen, PhD^1,2,3, Assaf Oshri, PhD⁴, Krysta Andrews, PhD², Stephanie Penta, BSc³, Holly Raymond, MSW³, Margaret McKinnon, PhD^2,3,5, Jennifer Brasch, MD^1,2,3, James MacKillop, PhD^1,2,5, and Michael Amlung, PhD^1,6,7img1

The Canadian Journal of Psychiatry /
La Revue Canadienne de Psychiatrie
2021, Vol. 66(9) 788–797
© The Author(s) 2021
Article reuse guidelines:
sagepub.com/journals-permissions
DOI: 10.1177/07067437211011851
TheCJP.ca | LaRCP.caimg2

AbstractObjective: Posttraumatic stress disorder (PTSD) and substance use disorders (SUDs) present a complex and often severe clinical presentation within a concurrent disorders context. The objective of this study was to examine associations between PTSD symptoms and SUD outcomes to better understand the clinical phenomenon of comorbid PTSD and SUD. Multivariate statistical methods were used to test the hypothesis that elevated PTSD symptoms, both at the level of global severity and specific PTSD symptom clusters, are associated with greater substance use and related problems.Methods: Data were collected from an intake assessment battery within a specialized concurrent disorders outpatient service in Hamilton, ON. The sample comprised 326 participants (mean age = 37.19, 45.4% female). Structural equation models examined associations between PTSD and alcohol, cannabis, and substance use frequency and problems, controlling for age and sex. Alcohol was ultimately dropped from the model due to non-significant bivariate associations.Results: Higher global PTSD symptomatology was significantly associated with higher cannabis and other substance use frequency and related problems. Analyses using PTSD cluster scores showed higher scores for alterations in arousal were positively associated with cannabis-related problems, drug-related problems, and cannabis and other substance use frequency. Avoidance was significantly associated with cannabis frequency and cannabis-related problems. In general, effect sizes were small in magnitude, accounting for between 9% and 25% of variance.Conclusion: Significant cluster-level associations indicate the importance of specific PTSD symptoms (hyperarousal, avoidance) in relation to substance use when identifying therapeutic targets among individuals presenting with comorbid PTSD-SUD. This multivariate approach provides a higher resolution and potentially more clinically informative representation of the complex clinical presentation of PTSD and SUD in a concurrent disorder population and could guide the development of more effective treatment paths.AbrégéObjectif : Le trouble de stress post-traumatique (TSPT) et les troubles d’utilisation de substances (TUS) ont une présentation clinique complexe et souvent grave dans un contexte de troubles co-occurrents. L’objectif de la présente étude était d’examiner les associations entre les symptômes du TSPT et les résultats du TUS pour mieux comprendre le phénomène clinique du TSPT et des TUS comorbides. Des méthodes statistiques multivariées ont servi à vérifier l’hypothèse selon laquelle les symptômes élevés de TSPT, tant au niveau de la gravité globale que des grappes de symptômes spécifiques du TSPT, sont associés à une utilisation plus forte de substances et aux problèmes connexes.Méthodes : Les données ont été recueillies par une batterie d’évaluation initiale au sein d’un service ambulatoire spécialisé en troubles co-occurrents à Hamilton, Ontario. L’échantillon comprenait 326 participants (âge moyen = 37,19, 45,4% de sexe féminin). Des modèles d’équation structurelle examinaient les associations entre le TSPT et l’alcool, le cannabis, et la fréquence et les problèmes de l’usage de substances, en contrôlant pour l’âge et le sexe. L’alcool a finalement été exclu du modèle en raison d’associations bivariées non significatives.Résultats : La symptomatologie globale plus élevée du TSPT était significativement associée à une plus grande fréquence d’utilisation de cannabis et d’autres substances et aux problèmes connexes. Des analyses utilisant les scores des grappes du TSPT ont montré que des scores plus élevés pour les Modifications de l’excitation étaient positivement associés aux problèmes liés au cannabis, aux problèmes liés aux drogues et à la fréquence d’utilisation du cannabis et d’autres substances. L’évitement était significativement associé à la fréquence d’usage du cannabis et aux problèmes liés au cannabis. En général, les tailles d’effet étaient petites en magnitude, représentant entre 9-25% de la variance.Conclusion : Des associations significatives au niveau des grappes indiquent l’importance de symptômes spécifiques du TSPT (hyperexcitation, évitement) en relation à l’utilisation de substances quand on identifie les cibles thérapeutiques chez les personnes présentant un TSPT-TUS comorbide. Cette approche multivariée offre une résolution supérieure et potentiellement une représentation cliniquement plus informative de la présentation clinique complexe du TSPT et du TUS dans une population de troubles co-occurrents, et pourrait orienter l’élaboration de trajectoires de traitement plus efficaces.Keywordsposttraumatic stress disorder, alcohol, cannabis, substance use, concurrent disordersIntroductionConcurrent disorders, also known as co-occurring or dual diagnosis disorders, are defined as having a combined diagnosis of one or more major psychiatric disorders and an addictive disorder, such as alcohol or other substance use disorder (AUD/SUD). Concurrent disorders are prevalent in psychiatric samples, with 20% of individuals experiencing mental illness also reporting co-occurring substance use, and individuals experiencing mental health issues are twice as likely to have a substance use problem compared to the general population in their lifetime.¹ Finally, up to 50% of individuals with schizophrenia have concurrent substance use.² Individuals diagnosed with concurrent disorders often have highly complex and severe psychiatric sequelae which may require specialized treatment intervention.^3-5 Yet, a general lack of consensus exists on best practices concerning integrative services and intervention strategies,⁶ further compounded by the fact that research commonly focuses on these disorders in isolation.Posttraumatic stress disorder (PTSD) can develop following exposure to a traumatic event.⁷ Symptoms of PTSD are typically categorized into four clusters: intrusive thoughts and memories, avoidance behaviours, negative alterations in cognition and mood (NACM), and alterations in arousal.⁷ Approximately 9% of Canadians report having PTSD within their lifetime.⁸ Of individuals meeting criteria for PTSD, 27.8% report alcohol use/dependence and 25.5% report substance use/dependence.⁸ Data from the 2010 National Epidemiologic Survey on Alcohol and Related Conditions (N = 34,653) estimated that 46.4% of individuals meeting criteria for PTSD also meet criteria for an SUD⁹ and these rates remained stable in the 2016 wave.¹⁰ Within treatmentseeking samples, patients with PTSD are 14 times more likely to meet criteria for a SUD than patients without PTSD.^11,12Comorbid PTSD and SUD represent a particularly challenging clinical presentation, whereby accurate identification and targeted treatments are critical. The presence of PTSD and SUD confer heightened risk of other mental health concerns, suicidality, mortality, and functional impairment.⁹ Although reductions in alcohol and/or substance use problems following treatment for PTSD have been reported in some studies,^13-15 other studies have linked comorbid presentation to poor treatment response and increased relapse risk compared to PTSD or SUD alone.^15,16 To address these concerns, there is a growing need to better understand the nuances of comorbid presentations with the ultimate goal of developing treatment paths that lead to improved outcomes.There are few high-resolution studies reporting associations between specific symptom clusters of PTSD and SUD in people with both diagnoses. The extant literature demonstrates some basic associations between PTSD symptom clusters and substance use. Among individuals receiving inpatient treatment for SUD, those with comorbid SUD and AUD reported greater intrusion/re-experiencing symptoms than patients without a comorbid AUD.¹⁷ Women with a comorbid addictive disorder and PTSD reported higher acuity of PTSD symptoms among the re-experiencing, avoidance and numbing, and arousal clusters compared to women with a PTSD diagnosis but no addictive disorders.¹⁸ Among individuals concurrently diagnosed with PTSD and AUD/SUD, fluctuations in PTSD symptoms were associated with alcohol and cocaine use disorder symptoms the following week, potentially supporting a self-medication conceptualization.¹⁹ Among military veterans, PTSD symptoms were uniquely associated with alcohol misuse, whereby clusters of intrusions, NACM, and hyperarousal symptoms significantly accounted for greater variance in alcohol misuse more so than demographic or military-related variables or co-occurring diagnoses of depression and/or anxiety. Greater PTSD symptom severity in clusters of NACM and alterations in arousal are seen in individuals who engage in co-use of alcohol and cannabis compared to those who use alcohol alone.²⁰ Taken together, these associations indicate a strong unique relationship between PTSD and substance use which needs to be accounted for when identifying therapeutic targets for intervention.²¹Despite the importance of these relations, there is a lack of consensus as to which PTSD symptom clusters are more strongly associated with AUD/SUD and prior studies typically evaluated associations between PTSD and alcohol and drug use separately despite high overlap between the use of these substances. Multivariate statistical methods such as structural equation modelling (SEM) examine multiple variables within a single analytic framework, potentially providing a more valid representation of the complex clinical presentation of concurrent disorders. Finally, to our knowledge, there are no published studies examining associations between PTSD symptom clusters and frequency of use of alcohol and other substances. Understanding the interaction between the use of different substances and PTSD symptom severity may be important in developing therapeutic interventions. As such, this study analyzed the associations between PTSD symptomatology and alcohol and/or substance use frequencies and related problems in a sample of outpatient clients with concurrent disorders. Using SEM, we characterized the pattern of relationships between PTSD and AUD/SUD simultaneously to better understand the clinical phenomenon of comorbid PTSD and AUD/SUD.MethodsParticipantsParticipants were drawn from a Concurrent Disorders Research Database maintained by the Peter Boris Centre for Addictions Research at McMaster University in collaboration with the Concurrent Disorders Outpatient Service (CDOS)—a specialized outpatient treatment service for concurrent disorders—at St. Joseph’s Healthcare Hamilton. Participants in the study were referred to the CDOS by a community-based physician, after discharge from a psychiatric admission, by another clinic at the psychiatric hospital, or self-referred. Patients have a wide range of psychiatric diagnoses including anxiety, mood and psychotic disorders, as well as problematic substance use. The CDOS serves patients whose mental health disorder is considered too severe for addiction-focused treatment programs. The population is representative of patients with concurrent disorders who can access a tertiary care mental health program.This study used a naturalistic design involving an intake battery administered to all clients at their initial appointment in the CDOS outpatient clinic. The only exclusion from completing the battery was acute distress or significant neurocognitive impairment, as judged by the clinical team. Participants for the current study had to be at least 18 years of age, have provided written informed consent for the database, and report at least 1 lifetime traumatic event on the Brief Trauma Questionnaire. Understanding of the consent form was confirmed by an interview-based consent verification form. There was no compensation for participants since assessments were part of standard care. The research database was approved by the Hamilton Integrated Review Ethics Board (HiREB #0863). A total of 450 clients completed the clinical battery between February 2018 and November 2019. Of these, 359 (79.8%) consented to the database, and the final sample included 329 participants who reported lifetime exposure to at least 1 traumatic event.MeasuresTesting occurred in a private cubicle prior to the client’s first appointment. The computerized battery lasted ~35 minutes and was collected using Research Electronic Data Capture (REDCap) software.^22,23 If computer literacy was an issue, participants completed a paper/pencil version.The battery included self-report questionnaires assessing demographics, addiction and mental health screens, and psychological assessments. Demographic variables included sex assigned at birth and age. The mental health measures included validated assessments for psychiatric disorders (e.g., major depressive disorder, anxiety disorders, psychosis, PTSD, borderline personality disorder). A subset of these measures was analyzed in the present study, as described below.Alcohol, cannabis, and drug use. Alcohol use frequency and alcohol-related problems were measured using the Alcohol Use Disorders Identification Test (AUDIT),²⁴ a self-report screen assessing the frequency and severity of alcohol use. Scores on the AUDIT range from 0 to 40 and a cut-off of 20 or greater indicates possible AUD. The Cannabis Use Disorder Identification Test-Revised (CUDIT-R)²⁵ assessed cannabis use frequency and related problems. Scores on the CUDIT-R ranged from 0 to 32, and a cut-off of 13 has high discriminant validity in concurrent disorders samples.²⁵ The Drug Use Disorders Identification Test (DUDIT)^26,27 assessed the severity of drug-related problems. Scores on the DUDIT ranged from 0 to 44, and a cut-off of 25 was chosen based on published research.²⁶ Since the CUDIT-R was included, the DUDIT instructions explicitly excluded cannabis. Frequency of illicit drug use was assessed using the National Institute on Drug Abuse (NIDA) modified version of the World Health Organization Alcohol Smoking and Substance Involvement Test (ASSIST).²⁸ Drug categories included cocaine, methamphetamine, prescription stimulants, street opioids (e.g., heroin, opium), prescription opioids (e.g., oxycodone, hydrocodone), sedatives/sleeping pills, and an open-ended “other” category (none reported). Frequency of use was reported from 1 (“once or twice”) to 4 (“daily or almost daily”). These scores were used within SEM to generate a latent factor of substance use. These self-report measures are well-validated and have been shown to accurately reflect actual substance use.^29-33Psychological variables. PTSD symptoms were assessed using the Posttraumatic Stress Disorder Checklist for Diagnostic and Statistical Manual-5 (PCL-5).³⁴ The PCL-5 is a 20-item measure comprised of four clusters corresponding to the DSM-5 diagnostic criteria: Cluster B (intrusive thoughts and memories), Cluster C (avoidance behaviours), Cluster D (NACM), and Cluster E (alterations in arousal). Scores on the PCL-5 range from 0 to 80, with the following subscale ranges: Cluster B (0 to 20), Cluster C (0 to 8), Cluster D (0 to 28), and Cluster E (0 to 20). SEM was used to generate a single latent factor of PTSD symptoms from the cluster scores, as described below. A shortened version of the Brief Trauma Questionnaire (BTQ) was administered to assess lifetime experiences of traumatic events.³⁵ This shortened version only assessed endorsement of a type of trauma (yes/no), while the severity of the experience was not evaluated. Symptoms of depression, anxiety, and psychosis were assessed via the Patient Health Questionnaire (PHQ-9),³⁶ General Anxiety Disorder-7,³⁷ and the Prodromal Questionnaire (PQ-16),³⁸ respectively.Data analysis plan. Descriptive statistics and zero-order correlations were run among the study variables. SEM was implemented in MPlus Version 8.0³⁹ using maximum likelihood estimation to assess the effects of a latent PTSD factor (described below) on substance use outcomes while controlling for age and sex (male = 0, female = 1). As cannabis use and severity were assessed separately from drug use, cannabis and other drugs were entered separately in the models. Two primary models were specified to evaluate the effects of a latent PTSD factor on cannabis and substance use frequency and severity. Follow-up models specified the effects of individual PTSD symptom clusters on the frequency and severity variables. The PTSD latent factor was determined with a measurement model comprised of 4 PTSD symptom cluster scores extracted from the PCL-5: intrusions (Cluster B), avoidance (Cluster C), NACM (Cluster D), and alterations in arousal (Cluster E). Prior to modelling, substance use measures (CUDIT, AUDIT, and DUDIT) were square-root transformed based on benchmarks of skewness >2 and kurtosis >7.⁴⁰ There were no significant outliers within the sample (Zs > 3.29). We used two-tailed tests with a statistical significance of α < 0.05. Following established conventions,^41,42 an excellent-fitting model had a comparative fit index (CFI) and Tucker Lewis index (TLI) ≥ 0.95, standardized root mean squared residual (SRMR) of ≤ 0.08, and root mean square error of approximation (RMSEA) ≤ 0.06.Table 1. Sample Characteristics and Zero-order Correlations.Tab1ResultsDescriptive Statistics and CorrelationsThe sample consisted of 326 participants (45.4% female sex) with a mean age of 37.19 years (SD = 11.78). Sample characteristics are presented in Table 1. The most frequently endorsed types of trauma were adult physical assault (71.5%), serious injury (59.8%), and unwanted sexual contact (59.2%). Furthermore, 61% of the sample met criteria for a provisional diagnosis of PTSD.³⁴ Alcohol and cannabis use was most common, with 90.5% and 72.1% of the sample reporting monthly or greater use, respectively (Figure 1). The three most commonly used illicit substances were cocaine (35.9%), sedatives (29.1%), and prescription opioids (17.8%).Fig1
Substance Use–Related Problems Structural Equation ModelA measurement model (Figure 2) assessing the PTSD latent factor was specified. Factor loadings for the PTSD latent factor were significant and in the predicted direction; all indicators exceeded .70. A SEM was specified in which a latent PTSD factor predicted cannabis and substance use–related problems, with age and sex included as covariates (see Figure 3). AUDIT was not included within the model due to its lack of bivariate association with PTSD variables (Supplemental Table 1).The pathway from PTSD to CUDIT and DUDIT was assessed. Among control variables, age and sex were significantly and negatively associated with CUDIT (age: β = −0.363, P < 0.001; sex: β = −0.195, P < 0.001) and DUDIT (age: β = −0.153, P < 0.05; sex: β = −0.222, P < 0.001). Modification indices suggested covarying the residuals of intrusions and avoidance clusters. The final model and fit indices are presented in Figure 3. Within the model, PTSD was significantly associated with CUDIT (β = 0.290, P < 0.001) and DUDIT (β = 0.282, P < 0.001) scores. PTSD shows significant positive associations with cannabis- and substance-related problems. Overall, the model significantly accounted for 24.6% of the variance in CUDIT scores and 12.9% of the variance in DUDIT scores.Fig2
A secondary model was specified in which the effect of individual cluster scores was assessed for cannabis and substance use-related problems. Age and sex were included as covariates. AUDIT was not included within the model. A just-identified model emerged indicating model fit indices could not be determined. However, estimates revealed a significant association between the Alterations in Arousal cluster and CUDIT scores (Arousal: β = 0.270, P < 0.01). For DUDIT scores, a significant association was observed with the Alterations in Arousal cluster (β = 0.276, P < 0.01). Thus, higher scores for the Alterations in Arousal cluster were associated with higher cannabis- and substance-related problems.Substance Use Frequency Structural Equation ModelA SEM with a latent PTSD factor predicting cannabis and substance use frequencies was specified. Substance use frequency was specified as a latent factor comprised of endorsement frequencies reported on the NIDA-Modified ASSIST. Age and sex were included as covariates. Cannabis use frequency was kept as a separate outcome variable to reflect the delineation between CUDIT and DUDIT scores in the prior model. Refer to Figure 4 for a visual representation of this model. AUDIT was not included within the model due to its lack of association with PTSD variables (see Supplemental Table 1 for bivariate correlations).Fig3
Factor loadings for the substance use frequency latent factor were significant and in the predicted direction; all indicators exceeded 0.40 except for prescription stimulants and sedatives, which were trimmed from the model due to low factor loading (λ = 0.247; λ = 0.295, respectively). Next, the pathway from PTSD to cannabis use and substance use frequency was tested. Among the control variables, age was significantly associated with cannabis use (β = −0.290, P < 0.001) but not substance use (β = −0.072, P > 0.05). Sex was not significantly associated with any other variable and was trimmed from the model. Modification indices suggested covarying the residuals of intrusions and avoidance clusters. The final model and fit indices are presented in Figure 4. Higher PTSD scores were significantly associated with greater cannabis use frequency (β = 0.223, P < 0.001) and higher scores on the substance use frequency latent factor (β = 0.283, P < 0.001). Overall, the model significantly accounted for 15.6% of the variance in cannabis use frequency and 9.2% of the variance in substance use frequency.A secondary model was specified in which the effect of individual cluster scores was assessed for cannabis and substance use frequencies. Age and sex were included as covariates. AUDIT was not included within the model. Among control variables, age and sex were significantly associated with cannabis use (age: β = −0.302, P < 0.001; sex: β = −0.154, P < 0.01) but not substance use (age: β = −0.075, P > 0.05; sex: β = −0.084, P > 0.05). Modification indices suggested covarying the residuals of intrusions and avoidance clusters. The final model was adequately fitting, χ² (23, N = 326) = 37.913, P < 0.05, TLI = 0.880, CFI = 0.931, RMSEA = 0.045, SRMR = 0.033. Within the model, scores on the alterations in arousal cluster, but no other clusters, were positively associated with greater cannabis use frequency (β = 0.355, P < 0.001) and substance use frequency (β = 0.372, P < 0.01). Thus, greater symptoms related to alterations in arousal were associated with greater frequency of cannabis and other drug use.DiscussionThis study is among the first to use a SEM framework to model associations between PTSD symptoms and cannabis and illicit drug use and severity in a clinical sample of people with concurrent disorders. We observed significant associations between global PTSD symptomatology and cannabisand substance-related problems as well as frequency of use. The alterations in arousal cluster drove the associations between both frequency and problems for cannabis and other substances, whereas the avoidance cluster was uniquely associated with cannabis outcomes. The effect sizes were generally in the small range and the associations accounted for between 9% and 25% of variance in the substance use outcomes. Additional factors beyond PTSD-related symptoms likely contribute to substance use in this sample, which is highly plausible given the complex clinical presentation of people with concurrent disorders.Fig4
These results corroborate previous studies analyzing associations between PTSD symptom clusters and cannabis use and other SUDs. Similar to the current cluster associations, prior research has shown associations between arousal and avoidance clusters and substance-use frequency and severity in people with PTSD + SUD.^43,44 The current results contrast with prior literature showing an inverse relationship between avoidance and heroin use,⁴⁵ whereas we found a positive association between avoidance and substance use. These prior studies used DSM-IV criteria and clusters for PTSD which were changed in the DSM-5. Of the limited studies examining DSM-5 PTSD criteria, alcohol misuse was shown to be significantly associated with Intrusions, NACM, and arousal.²¹ However, alcohol use and related problems were not significantly associated with PTSD in our sample. Collectively, these results are generally consistent with existing findings underscoring the importance of recognizing the associations between PTSD symptom clusters and substance use when identifying intervention targets.The lack of significant associations with alcohol variables was surprising and somewhat inconsistent with prior literature. Greater arousal symptoms were observed in women with comorbid PTSD + AUD than those with comorbid PTSD + SUD.⁴³ However, another study in women who experienced intimate partner violence found that PTSD clusters of avoidance, numbing, and hyperarousal were not associated with alcohol use but were associated with drug use, similar to the present findings.¹⁸ One potential explanation for the lack of alcohol-related results is prior research indicating that associations between PTSD and alcohol may be temporally specific. Acute increases in PTSD symptoms were predictive of greater alcohol consumption in the following hours and the next day among adults meeting criteria for PTSD and AUD.⁴⁶ Considering the current study was cross-sectional, similar temporal associations could not be explored. The lack of associations may also be related to the clinical focus of the CDOS in which clients with a primary AUD and no other SUD are commonly referred to other alcohol-focused services.From a clinical perspective, these findings highlight the need to assess both substance use and comorbid psychiatric conditions, particularly PTSD, to ensure appropriate treatment can be provided. Integrative treatment approaches such as Concurrent Treatments of PTSD and Substance Use Disorders Using Prolonged Exposure (COPE)⁴⁷ directly target both PTSD and substance use, whereby sustained reductions in both disorders are evident.⁴⁸ Interestingly, COPE is no more effective than relapse prevention therapy when PTSD is subthreshold relative to meeting full criteria,⁴⁹ furthering the notion that comprehensive diagnostic clarification is key to effective treatment outcomes. This study further clarifies relations among specific types of substance use and PTSD symptom clusters. While outcomes of different PTSD treatments do not appear to differentially affect PTSD symptom clusters among individuals without SUD,⁵⁰ these findings may not hold among a concurrent sample where PTSD clusters are differentially related to frequency and problems associated with different substances. Application of integrative treatments like COPE and additional targeted modules depending on type and patterns of substance use may yield the greatest outcomes.These results should be considered in the context of the study’s limitations. First, the current data are cross-sectional and cannot determine temporal associations. Second, although all participants reported at least 1 traumatic event on the BTQ, formal diagnostic interviews to confirm PTSD diagnosis were not available. Additionally, as adverse childhood experiences were not assessed, the implications of those events on the development of PTSD and substance use cannot be elucidated. Future studies should include a formal diagnosis of PTSD or utilize multiple assessments for PTSD to avoid shared method variance along with assessments of adverse childhood experiences in the development of complex PTSD. Third, alcohol, cannabis, and substance use frequencies were self-report and not verified with biochemical testing, although prior research indicates the validity of self-report data in the assessment of substance use rates.^29-33 Differences in frequency response options across the substance use measures prevented coding by psychoactive effect (e.g., stimulant, sedative) which limited our ability to characterize the self-medication effects that may have been operant for specific PTSD symptoms. These effects should be explored in future studies. Finally, all participants were recruited from a single clinic within 1 hospital which may limit generalizability to other populations and clinical settings.ConclusionThe results of this study build upon prior literature and add novel findings of associations between cannabis and other drug use and related problems with PTSD symptomatology. Significant associations between the arousal cluster score with cannabis and substance-related problems and frequencies but not alcohol underscores the importance of studying these associations further. The use of multivariate statistics presents a potentially more nuanced representation of the complex clinical presentation of concurrent disorders when identifying therapeutic targets for intervention especially among complex concurrent disorder samples where treatment outcomes are generally poor.AcknowledgmentsThe authors are grateful to the clients of the Concurrent Disorders Outpatient Service and Community Psychiatry Clinics at St. Joseph’s Healthcare Hamilton for providing clinical screening battery data. We thank the staff and students of these clinics for their assistance with data collection. The authors recognize and acknowledge that this work was conducted on the traditional territories of the Mississauga and Haudenosaunee nations, and within the lands protected by the Dish With One Spoon wampum agreement.Data AccessData access is not available as data are part of the intake assessment for clinical services at Concurrent Disorders Outpatient Service and cannot be released.Declaration of Conflicting InterestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Dr. James MacKillop is a Principal in BEAM Diagnostics, but no BEAM products were involved in this research. The other authors have no other conflicts of interest to report.FundingThe author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by funding from the Peter Boris Centre for Addictions Research at McMaster University/St. Joseph’s Healthcare Hamilton and the Mental Health and Addictions Program at St. Joseph’s Healthcare Hamilton. Dr. James MacKillop holds the Peter Boris Chair in Addictions Research. Dr. Margaret McKinnon is supported by the Homewood Chair in Mental Health and Trauma at McMaster University.ORCID iD Michael Amlung

https://orcid.org/0000-0003-4483-7155
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¹ Peter Boris Centre for Addictions Research, McMaster University & St. Joseph’s Healthcare Hamilton, Ontario, Canada² Department of Psychiatry & Behavioural Neurosciences, McMaster University, Hamilton, Ontario, Canada³ Mental Health and Addictions Program, St. Joseph’s Healthcare Hamilton, Ontario, Canada⁴ Department of Human Development and Family Science, University of Georgia, Athens, Georgia, USA⁵ Homewood Research Institute, Guelph, Ontario, Canada⁶ Department of Applied Behavioral Science, University of Kansas, Lawrence, Kansas, USA⁷ Cofrin Logan Center for Addiction Research and Treatment, University of Kansas, Lawrence, Kansas, USACorresponding Author:
Michael Amlung, PhD, Cofrin Logan Center for Addiction Research and Treatment, University of Kansas, 1000 Sunnyside Ave., Lawrence, KS 66045, USA.
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