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IntroductionNicotine vaping is common among children and youth, and even more so among those with mental health concerns. Identifying and managing nicotine vaping in child and youth mental health treatment settings is key to addressing this modifiable risk factor for poorer physical and mental health in young people. Recommendations exist for screening, assessment and treatment of youth vaping; however, it remains unclear whether current practices in child and youth mental health programmes align with recommended standards.Methods and analysisAn explanatory sequential mixed methods design with three stages will be employed. In the first stage, a cross-sectional survey will be distributed to all eligible Canadian hospitals to identify practices in assessment and treatment of nicotine vaping within their child and youth mental health and addictions programmes. This survey will also assess barriers and facilitators for the uptake of the 2021 Canadian Paediatric Society recommendations on management of youth vaping. Semi-structured focus groups and interviews will be conducted in stage two, with clinicians, managers, youth and caregivers. Qualitative data will be analysed using a reflexive thematic approach. In stage three, findings and proposed behaviour change interventions will be reviewed at a knowledge mobilisation meeting with the goal of developing a national knowledge mobilisation plan to improve assessment and treatment of youth vaping in hospital-based mental health and addictions programmes.Ethics and disseminationThis study has received ethics approval from the Research Ethics Board at the Children’s Hospital of Eastern Ontario (Protocol #25/19X). Participants will provide informed consent prior to participating. Results will be published in peer-reviewed journals and presented at scientific conferences. Summaries will be provided to the funders of the study and to participating hospitals.

Multimorbidity, the presence of two or more (2+) chronic conditions, presents significant challenges for healthcare delivery, particularly among populations with opioid use disorder (OUD). Multimorbidity patterns among individuals with OUD are not well established, and minimal research exists examining the impact of clustering methods on identifying these patterns. Our study aimed to assess multimorbidity prevalence, explore associated sociodemographic and clinical characteristics, and determine multimorbidity patterns using hierarchical cluster analysis (HCA) and K-means clustering among people receiving treatment for OUD in Ontario, Canada between 2011 and 2021. Data from two prospective cohort studies were merged and linked to Ontario provincial health administrative databases. We identified 16 chronic conditions, used in prior research examining multimorbidity in Ontario, using ICD-10-CA diagnostic codes and the diagnostic codes of physician billing claims using a 2-year lookback. Multimorbidity was defined as the presence of 2+ of the above conditions, excluding the diagnosis of OUD. We conducted a retrospective cohort study, following the participants for eight years in the data holdings to ascertain the prevalence of multimorbidity. Sociodemographic and clinical characteristics were analyzed using modified Poisson regression models, and multimorbidity patterns were identified through HCA and K-means clustering. Among 3,430 people with OUD, 32.5% (n = 1,114, 95% confidence interval (CI)=30.9, 34.1) experienced multimorbidity over an eight-year period, with older age (Prevalence Ratio (PR)=3.39, 95% CI = 2.36, 4.87) and unemployment (PR = 1.31, 95% CI = 1.13, 1.54) associated with increased prevalence. HCA identified six distinct disease clusters, whereas K-means clustering identified four clusters. Both methods identified groupings of cardiovascular (coronary syndrome), cardiometabolic (diabetes, hypertension), and respiratory (chronic obstructive pulmonary disease) diseases, reflecting shared comorbidities among people with OUD. Our findings highlight the substantial burden of multimorbidity among populations with OUD, and the importance of considering sociodemographic factors in understanding multimorbidity prevalence. Moreover, the choice of clustering method significantly influences the identification and interpretation of multimorbidity patterns, with HCA providing more clinically meaningful groupings compared to K-means clustering. Our findings highlight the need for clinicians to tailor care plans and for policymakers to prioritize integrated healthcare delivery strategies to address the complex health needs of people with OUD.

Individuals with opioid use disorder (OUD) have a high prevalence of co-occurring mental health disorders; however, there exists little information on mental health service use for this population. We aimed to determine the prevalence of non-substance use-related mental health emergency department (ED) visits, hospitalizations, and outpatient physician visits for individuals receiving treatment for OUD over one year. We also explored individual-level characteristics associated with mental health care service use and estimated the costs of this care. We linked observational cohort data collected from 3,430 individuals receiving treatment for OUD in Ontario, Canada, with health administrative records available for all individuals enrolled in Ontario's public health insurance program. Eligible participants were receiving medication treatment for OUD and were recruited between 2011 and 2021 Starting on the day of cohort enrolment, we included health service data for up to 12 months. We identified ED visits and hospitalizations for non-substance use-related mental health disorders using ICD-10-CA diagnostic codes. Outpatient mental health visits to primary care providers and psychiatrists were ascertained by examining the diagnostic codes of physician billing claims. We used logistic regression to explore the association between demographic and clinical factors of interest and mental health-related ED visits or hospitalizations. Mean one-year mental healthcare costs, calculated in 2022 Canadian dollars, were estimated. We fit a two-part zero-inflated negative binomial model to explore the association between factors of interest and healthcare costs. Altogether, 14.9% of individuals had mental health-related acute care ED visits or hospitalizations and 37.3% had outpatient mental health visits during the follow up period. For participants with at least one visit, we determined the mean number of ED visits (1.93, standard deviation [SD] = 2.15), hospitalizations (1.46, SD = 1.05), primary care visits (3.51, SD = 4.31), and psychiatry visits (4.04, SD = 4.73). Lower odds of ED use and hospitalization were associated with older age (46+ compared to less than 25 years: odds ratio [OR] 0.43, 95% confidence interval [CI]: 0.29, 0.63) and being employed (OR 0.48, 95% CI 0.37, 0.61). Higher odds of ED use and hospitalization was associated with positive opioid urine drug screens (50% positive urine drug screens compared to 0%: OR 1.45, 95% CI 1.05, 2.01), having more comorbid conditions (7+ health conditions compared to 0-2 health conditions: OR 3.76, 95% CI 2.60, 5.44), and receipt of outpatient mental healthcare (OR 2.38, 95% CI 1.95, 2.92) were associated with higher odds of ED visits or hospitalizations. Mean one-year mental healthcare costs for individuals receiving ED visits or hospitalizations totaled $9,117.80 (95% CI 7,372.90, 10,862.70) per person. Mean one-year costs for individuals with outpatient mental healthcare alone totaled $382.30 (95% CI 343.20, 421.30) per person. Individuals receiving treatment for OUD receive care in EDs, inpatient units, and outpatient clinics for mental health conditions other than substance use-related diagnoses. Healthcare costs were considerably higher for those receiving acute care treatment for mental health conditions. Studying integrated mental health and substance use disorder treatment in the outpatient setting should be a priority to bolster care for this population.

ObjectivesTo compare the effectiveness of buprenorphine-naloxone (bup/nal) and methadone maintenance therapy (MMT) in the treatment of patients with opioid use disorder (OUD) during the fentanyl era.DesignSecondary analysis of prospective cohort study data.SettingData for the study were collected from 54 clinical sites across Ontario, Canada, between May 2018 and January 2023.ParticipantsTo be included in the present study, participants had to be at least 16 years of age, have provided written informed consent and be receiving either MMT or bup/nal therapy for OUD. This study includes data from 2601 participants, of whom 2068 were receiving MMT and 533 were receiving bup/nal for OUD. The mean age of participants was 39.4 years (SD: 10.9), and 45% were female.InterventionsMMT or bup/nal treatment for OUD.Outcome measuresWe employed a propensity score matched analysis to compare treatment outcomes among patients receiving MMT compared with bup/nal. We used ongoing illicit opioid use as an indicator of treatment outcome. We considered participants with >50% of urine drug screens in the past 12 months positive for non-prescribed opioids to be ‘non-responders’. We conducted subgroup analyses to identify whether treatment type was associated with ongoing non-prescribed opioid use among patients with and without a history of intravenous drug use (IVDU), and whether treatment type was associated with retention in treatment.ResultsEight per cent of patients on bup/nal were considered non-responders, compared with 11.9% of patients on MMT. We did not find a statistically significant association between treatment type and treatment response. However, we did find that patients on MMT were more likely to stay in treatment for 12 months (OR 1.79, 95% CI 1.45 to 2.22, p<0.001). We also found that, among patients without a history of IVDU, those on MMT were more likely to continue using non-prescribed opioids, compared with those on bup/nal (OR 1.72, 95% CI 1.07 to 2.77, p=0.023).ConclusionsAmong a cohort of patients with OUD receiving treatment during the fentanyl era, we find that there is no statistically significant difference in ongoing non-prescribed opioid use between patients receiving MMT compared with bup/nal. Future studies should aim to further compare treatment effectiveness using patient-centred outcomes and pragmatic trial designs.

Background Patients with opioid use disorder (OUD) frequently present with comorbid psychiatric illnesses which have significant implications for their treatment outcomes. Notably, these are often identified by self-report. Our study examined the sensitivity and specificity of self-reported psychiatric diagnoses against a structured diagnostic interview in a cohort of patients receiving outpatient pharmacological treatment for OUD. Methods Using cross-sectional data from adults receiving outpatient opioid agonist treatment for OUD in clinics across Ontario, Canada, we compared participants’ self-reported psychiatric diagnoses with those identified by the Mini Neuropsychiatric Interview (MINI) Version 6.0 administered at the time of study entry. Sensitivity and specificity were calculated for self-report of psychiatric diagnoses. Results Amongst a sample of 683 participants, 24% ( n  = 162) reported having a comorbid psychiatric disorder. Only 104 of these 162 individuals (64%) reporting a comorbidity met criteria for a psychiatric disorder as per the MINI; meanwhile, 304 (75%) participants who self-reported no psychiatric comorbidity were in fact identified to meet MINI criteria for a psychiatric disorder. The sensitivity and specificity for any self-reported psychiatric diagnoses were 25.5% (95% CI 21.3, 30.0) and 78.9% (95% CI 73.6, 83.6), respectively. Conclusions Our findings raise questions about the utility of self-reported psychiatric comorbidity in patients with OUD, particularly in the context of low sensitivity of self-reported diagnoses. Several factors may contribute to this including remittance and relapse of some psychiatric illnesses, underdiagnosis, and the challenge of differentiating psychiatric and substance-induced disorders. These findings highlight that other methods should be considered in order to identify comorbid psychiatric disorders in patients with OUD.

There is a significant interindividual variability in treatment outcomes in methadone maintenance treatment (MMT) for opioid use disorder (OUD). This prospective cohort study examines the impact of comorbid psychiatric disorders on continued illicit opioid use in patients receiving MMT for OUD. Data were collected from 935 patients receiving MMT in outpatient clinics between June 2011 and June 2015. Using linear regression analysis, we evaluated the impact of having a comorbid psychiatric disorder on continued illicit opioid use during MMT, adjusting for important confounders. The main outcome measure was percentage of opioid-positive urine screens for 6 months. We conducted a subgroup analysis to determine the influence of specific comorbid psychiatric disorders, including substance use disorders, on continued illicit opioid use. Approximately 80% of participants had at least one comorbid psychiatric disorder in addition to OUD, and 42% of participants had a comorbid substance use disorder. There was no significant association between having a psychiatric comorbidity and continuing opioid use ( =0.248). Results from subgroup analysis, however, suggest that comorbid tranquilizer (β=20.781, <0.001) and cocaine (β=6.344, =0.031) use disorders are associated with increased rates of continuing opioid use. Results from our study may serve to guide future MMT guidelines. Specifically, we find that cocaine or tranquilizer use disorder, comorbid with OUD, places patients at high risk for poor MMT outcomes. Treatment centers may choose to gear more intensive therapy toward such populations.

IntroductionThe current COVID-19 pandemic has resulted in high rates of infection and death, as well as widespread social disruption and a reduction in access to healthcare services and support. There is growing concern over how the pandemic, as well as measures put in place to curb the pandemic, will impact people with mental disorders. We aim to study the effect of pandemics and epidemics on mental health outcomes for people with premorbid mental disorders.Methods and analysisWith our predefined search strategy, we will search five databases for studies reporting on mental health outcomes in people with pre-existing mental disorders during pandemic and epidemic settings. Search dates are planned as follows: 5 May 2020 and 23 July 2020. The following databases will be searched: MEDLINE/PubMed, CINAHL, PsycINFO, MedRxiv and EMBASE. Data will be screened and extracted in duplicate by two independent reviewers. Studies involving non-clinical populations or patients diagnosed with a mental disorder during a pandemic/epidemic will be excluded. We will include data collected from all pandemics and epidemics throughout history, including the present COVID-19 pandemic. If possible, study findings will be combined in meta-analyses, and subgroup analyses will be performed. We hope that this review will shed light on the impact of pandemics and epidemics on those with pre-existing mental disorders. Knowledge generated may inform future intervention studies as well as healthcare policies. Given the potential implications of the current pandemic measures (ie, disruption of healthcare services) on mental health, we will also compile a list of existing mental health resources.Ethics and disseminationNo ethical approval is required for this protocol and proposed systematic review as we will only use data from previously published papers that have themselves received ethics clearance and used proper informed consent procedures.Systematic review registrationPROSPERO registration number: CRD42020179611.

Background With the ongoing opioid crisis and policy changes regarding legalization of cannabis occurring around the world, it is necessary to consider cannabis use in the context of opioid use disorder (OUD) and its treatment. We aimed to examine (1) past-month cannabis use in patients with OUD, (2) self-reported cannabis-related side effects and craving, and (3) the association between specific characteristics of cannabis use and opioid use during treatment in cannabis users. Methods Participants receiving pharmacological treatment for OUD ( n  = 2315) were recruited from community-based addiction treatment clinics in Ontario, Canada, and provided information on past-month cannabis use (self-report). Participants were followed for 3 months with routine urine drug screens in order to assess opioid use during treatment. We used logistic regression analysis to explore (1) the association between any cannabis use and opioid use during treatment, and (2) amongst cannabis-users, specific cannabis use characteristics associated with opioid use. Qualitative methods were used to examine responses to the question: “What effect does marijuana have on your treatment?”. Results Past-month cannabis use was reported by 51% of participants ( n  = 1178). Any cannabis use compared to non-use was not associated with opioid use (OR = 1.03, 95% CI 0.87–1.23, p  = 0.703). Amongst cannabis users, nearly 70% reported daily use, and half reported experiencing cannabis-related side effects, with the most common side effects being slower thought process (26.2%) and lack of motivation (17.3%). For cannabis users, daily cannabis use was associated with lower odds of opioid use, when compared  with occasional use (OR = 0.61, 95% CI 0.47–0.79, p  < 0.001) as was older age of onset of cannabis use (OR = 0.97, 95% CI 0.94, 0.99, p  = 0.032), and reporting cannabis-related side effects (OR = 0.67, 95% CI 0.51, 0.85, p  = 0.001). Altogether, 75% of cannabis users perceived no impact of cannabis on their OUD treatment. Conclusion Past-month cannabis use was not associated with more or less opioid use during treatment. For patients who use cannabis, we identified specific characteristics of cannabis use associated with differential outcomes. Further examination of characteristics and patterns of cannabis use is warranted and may inform more tailored assessments and treatment recommendations.

Brain-derived neurotrophic factor (BDNF) has been a focus of psychiatric research for the past two decades. BDNF has been shown to impact neural function and development. Studies have investigated serum BDNF as a biomarker for psychiatric disorders such as depression and schizophrenia. In some studies, investigators attempt to control for variables such as smoking status, exercise, or diet. However, the relationship between these factors and BDNF is not clearly established. Furthermore, some studies have questioned whether a difference in the impact of BDNF exists between psychiatric and healthy populations. We aim to examine the association between serum BDNF levels and modifiable risk factors such as body mass index (BMI), smoking, exercise levels, and diet. Subsequently, we aim to examine whether the relationship between these risk factors and serum BDNF is different between psychiatric and control populations. We use cross-sectional data from an age- and sex-matched case-control study of participants with psychiatric inpatients and community controls without psychiatric diagnoses. Participants completed comprehensive assessments at study enrolment including sociodemographic information, smoking status, exercise, diet, and BMI. Serum BDNF levels were collected from participants. Linear regression analysis was performed to determine the association between modifiable factors and serum BDNF level. A significant association was found between sedentary activity level and lower serum BDNF levels (Beta coefficient = -2.49, 95% confidence interval [CI] -4.70, -0.28,  = 0.028). Subgroup analysis demonstrated that this association held for psychiatric inpatients but not for community controls; it also held in females (Beta coefficient = -3.18, 95% CI -6.29, -0.07,  = 0.045) but not in males (Beta coefficient = -1.42, 95% CI -4.61, 1.78,  = 0.383). Antidepressant use had a significantly different association between male (Beta coefficient = 3.20, 95% CI 0.51, 5.88,  = 0.020) and female subgroups (Beta coefficient = -3.10, 95% CI -5.75, -0.46,  = 0.022). No significant association was found between other factors and serum BDNF. Sedentary activity level may lead to lower serum BDNF levels in individuals with psychiatric diagnoses. Our findings support the notion that physical activity can provide a positive impact as part of treatment for psychiatric illness.

Background Due to the loss of tolerance to opioids during medication-assisted treatment (MAT), this period may represent a time of heightened risk for overdose. Identifying factors associated with increased risk of overdose during treatment is therefore paramount to improving outcomes. We aimed to determine the prevalence of opioid overdoses in patients receiving MAT. Additionally, we explored factors associated with opioid overdose during MAT and the association between length of time enrolled in MAT and overdose. Methods Data were collected prospectively from 2360 participants receiving outpatient MAT in Ontario, Canada. Participants were divided into three groups by overdose status: no history of overdose, any lifetime history of overdose, and emergency department visit for opioid overdose in the last year. We used a multivariate multinomial regression model to assess demographic and clinical factors associated with overdose status. Results Twenty-four percent of participants reported a lifetime history of overdose ( n  = 562), and 8% reported an emergency department (ED) visit for opioid overdose in the last year ( n  = 179). Individuals with a recent ED visit for opioid overdose were in treatment for shorter duration (odds ratio [OR] 0.92, 95% confidence interval [CI] 0.87, 0.97, p  = 0.001). Individuals with a lifetime or recent history of overdose were more likely to be younger in age (OR 0.93, 95% CI 0.89, 0.98, p  = 0.007 and OR 0.84, 95% CI 0.77, 0.92, p  < 0.001, respectively), report more physical symptoms (OR 1.02, 95% CI 1.01, 1.03, p  = 0.005 and OR 1.03, 95% CI 1.01, 1.05, p  = 0.005, respectively), and had higher rates of non-prescription benzodiazepine use (OR 1.87, 95% CI 1.32, 2.66, p  < 0.001 and OR 2.34, 95% CI 1.43, 3.81, p  = 0.001, respectively) compared to individuals with no history of overdose. Conclusions A considerable number of patients enrolled in MAT have experienced overdose. Our study highlights that there are identifiable factors associated with a patient’s overdose status that may represent areas for intervention. In particular, longer duration in MAT is associated with a decreased risk of overdose.