Publications

Background

Adolescents and young adults (AYAs) with opioid use disorder (OUD) frequently experience psychiatric comorbidities, yet little is known about how these conditions are managed pharmacologically in real-world settings. We aimed to describe psychotropic prescribing patterns, polypharmacy, augmentation, therapeutic-class switching, and non-persistence among AYAs with OUD.

Methods

We conducted a retrospective cohort study using electronic medical records from the Substance Use Treatment and Recovery clinic at the Nationwide Children’s Hospital between 2009 and 2022. AYAs aged 10–26 years diagnosed with OUD and at least one co-occurring psychiatric condition were included. Psychotropic prescriptions were grouped into therapeutic classes and evaluated longitudinally for patterns of medication use, medication switching, concurrent prescribing, and non-persistence. Analyses were descriptive and stratified by psychiatric diagnosis, and results are presented as means, standard deviations (SDs), and frequencies.

Results

The cohort included 101 patients (mean age = 18.5 years; 61.4% male) with anxiety disorders (64.4%) and depressive disorder (56.4%) being the most common diagnoses. Psychotropic polypharmacy occurred in 79.2% of patients. Non-persistence of psychotropic medications was common, affecting 63.2% of patients with depression, 58.5% with anxiety disorders, 44% with ADHD, and 66.7% with bipolar disorder. Medication switches were observed in 91.2% of patients. Medication switching was most frequent for depression (mean = 2.7 switches; SD = 1.6) and anxiety disorders (mean = 1.6; SD = 1.4). The most common switch pathways involved selective serotonin reuptake inhibitors (SSRIs) and second-generation antipsychotics (SGAs), as well as between SSRIs and bupropion. Augmentation strategies were prevalent, including SSRI plus SGA in depression (38.6%) and SSRI plus benzodiazepine in anxiety disorders (33.8%). Attention deficit/hyperactivity disorder (ADHD) treatment was comparatively stable, with fewer switches (mean = 0.5; SD = 0.7) and limited augmentation. Bipolar disorder management commonly involved combinations of SGAs and mood stabilizers.

Conclusions

Among AYAs with OUD, psychotropic treatment was characterized by frequent polypharmacy, augmentation, substantial non-persistence, and common medication class switching, especially for depression and anxiety disorders. These findings demonstrate challenges in achieving pharmacologic stability and support the need for prospective research to optimize psychiatric management in AYAs with OUD.

Clinical trial number

Not applicable.

Keywords: Opioid use disorder, Psychiatric comorbidity, Adolescents and young adults, Psychotropics, Pharmacologic treatment

Pursuing replicability — independent evidence for previous claims — is important for creating generalizable knowledge^1,2. Here we attempted replications of 274 claims of positive results from 164 quantitative papers published from 2009 to 2018 in 54 journals in the social and behavioural sciences. Replications were high powered on average to detect the original effect size (median of 99.6%), used original materials when relevant and available, and were peer reviewed in advance through a standardized internal protocol. Replications showed statistically significant results in the original pattern for 151 of 274 claims (55.1% (95% confidence interval (CI) 49.2–60.9%)) and for 80.8 of 164 papers (49.3% (95% CI 43.8–54.7%)), weighed for replicating multiple claims per paper. We observed modest variation in replication rates across disciplines (42.5–63.1%), although some estimates had high uncertainty. The median Pearson’s r effect size was 0.25 (95% CI 0.21–0.27) for original studies and 0.10 (95% CI 0.09–0.13) for replication studies, an 82.4% (95% CI 67.8–88.2%) reduction in shared variance. Thirteen methods for evaluating replication success provided estimates ranging from 28.6% to 74.8% (median of 49.3%). Some decline in effect size and significance is expected based on power to detect original effects and regression to the mean because we replicated only positive results. We observe that challenges for replicability extend across social–behavioural sciences, illustrating the importance of identifying conditions that promote or inhibit replicability^3,4.

Objectives

The All of Us (AoU) Research Program is a national-scale effort to build a dataset to help transform the future of health research by equipping researchers with comprehensive health data from diverse populations, especially those underrepresented in biomedical research. Our objectives were to evaluate the burden of HIV and major depressive disorder (MDD) in underrepresented groups and the frequency of the HIV/MDD comorbidity.

Methods

We conducted a cross-sectional analysis combining collected survey and electronic health record (EHR) data. We ascertained HIV and MDD cases using Observational Medical Outcomes Partnership codes. We used multivariable logistic regression to obtain the odds ratio of HIV in AoU participants and MDD in AoU participants with HIV.

Results

The latest AoU data release includes 412 211 participants: 254 700 have at least one medical condition concept in their EHR, of whom 5193 (1.3%) had HIV, and 2238 (43%) of those with HIV had a diagnosis of MDD. Black AoU participants had approximately 4.58 times the odds of having an HIV diagnosis compared with the combined odds of all other racial groups. AoU participants with HIV were more likely to have MDD (p = 0.001) than were participants without HIV.

Conclusion

Among AoU participants, Black individuals have a disproportionately high burden of HIV, pointing to underlying factors such as social determinants of health, limited access to healthcare or prevention resources, and potential systemic biases that contribute to these differences. In addition, HIV is a risk factor for mental health issues like MDD. Further data collection from people with HIV will elucidate contributing factors and the need for interventions.

We present a research-based framework for measuring successful environments on software teams for long-term and sustainable sociocognitive problem-solving. Across 1,282 full-time developers in 12+ industries, we tested the factors of our framework and found it predictive of developers’ self-reported productivity.