Stoppel, Cynthia

Substance use disorders (SUD) remain a significant source of morbidity and mortality in the United States, and access to treatment continues to be inadequate. Primary Care Clinicians (PCCs) are well-positioned to provide long-term SUD care for patients. However, multiple provider-level barriers exist. Collaborative Care Management (CoCM) has proven successful in supporting PCCs in treating psychiatric conditions, such as depression. Our group proposes an addiction-focused modified CoCM that leverages telemedicine and an electronic platform (Senyo) into primary care. This study assesses PCCs’ attitudes toward this proposed model and their likelihood (with support) of prescribing medications for alcohol and opioid use disorder. To achieve this, an anonymized and confidential electronic survey was deployed to all 489 of our institutions’ PCCs. Eighty-five completed the survey (17.4% response); the majority (94%) agreed that digital CoCM for SUD will be helpful for their practice, and 85% expressed agreement that such a model will increase their confidence and likelihood to prescribe anti-craving medications. Additionally, we found that PCCs’ comfort level in addressing SUD with patients is not associated with years in practice, clinician type (attending physicians, resident/fellow physicians, nurse practitioner or physician assistant), or their perception of currently available SUD treatment resources. Future work to evaluate practice changes, including rates of anti-craving medication prescribing and SUD-treatment outcomes after implementation of our digital CoCM will prove useful in determining the effectiveness of this model.

ABSTRACTObjectivesPrior studies have raised concerns about postpartum hemorrhage (PPH) risk associated with selective serotonin and serotonin‐norepinephrine reuptake inhibitors (SSRIs and SNRIs), although limitations include possible confounding by maternal indications. The current study examined the risk of PPH associated with prenatal S/NRIs accounting for diagnosed maternal depression during pregnancy.MethodsThe current study is a secondary analysis of data from a cohort of 2213 pregnancies (1997–2010) identified from a large electronic medical record‐linkage system. Exposures were identified from electronic prescriptions records and resulted in three main groups: S/NRI‐exposed pregnancies (S/NRI users), antidepressant‐unexposed pregnancies (non‐users), and S/NRI exposure in the year prior to (but not during) pregnancy (former users). Data used to identify PPH occurrences included obstetrician chart diagnoses, diagnosis codes, and recorded estimated blood loss (EBL). The risk of PPH by exposure group was estimated using unadjusted, minimally adjusted (maternal age at delivery, delivery year, parity, and maternal smoking), and depression‐adjusted models (previous covariates and maternal depression). Supplemental analyses included an examination of PPH risk with prenatal bupropion monotherapy.ResultsThe study cohort consisted of 837 S/NRI users, 401 former users, and 786 non‐users, as well as 114 bupropion users identified for additional analyses. The frequency of PPH was 11.6% in the overall cohort, and was 14.7% for S/NRI users, 10.2% for former users, and 8.7% for non‐users. The risk of PPH was significantly higher for S/NRI users than non‐users in unadjusted (OR 1.82 [95% CI 1.33, 2.49]), minimally adjusted (aOR 1.66 [1.19, 2.31]), and depression‐adjusted models (aOR 1.46 [1.02, 2.10]). The risk of PPH was also higher for S/NRI former users than non‐users in unadjusted models (OR 1.54 [1.05, 2.26]) but not minimally adjusted (aOR 1.42 [0.95, 2.11]) or depression‐adjusted models (aOR 1.41 [0.95, 2.10]). No significant differences in PPH risk were observed between S/NRI users and former users, between bupropion users and non‐users, or between S/NRI users and bupropion users.ConclusionsPrenatal S/NRI exposure was associated with an increased risk of PPH after adjusting for maternal depression, although residual confounding and confounding by other factors (including unmeasured factors) may be possible, as the risk of PPH appeared to be similar between S/NRI users and former users. Best‐practice PPH risk management strategies should be adhered to for depressed patients, especially those who require treatment with antidepressants during pregnancy.

In this case series, we present several examples from participants (2 patients and 1 healthy control) of a 12-week pilot feasibility study to create a digital phenotype of depression (unipolar or bipolar) through active and passive data collection from a smartphone and a wearable device combined with routine clinical care for mood disorders. The selected cases represent real clinical examples that highlight the intrinsic challenges that should be expected when conducting similar studies, including appropriate health data privacy protection, clinical standardization, and interindividual differences in levels of engagement and acceptability of active and passive data collection (ie, self-reported, behavioral, cognitive, and physiological data), particularly with patient-generated data in mobile apps, digital proficiency habituation, and consistent use of wearable devices. In the context of the rapidly growing use of digital technologies in psychiatry, anticipating challenges for the integration of personal mobile devices and smartphone mental health apps as aides to track specific aspects of depressive disorders is critical for a clinically meaningful digital transformation of mood disorders care.

AbstractThe purpose of this study was to estimate the extent of potential antidepressant overprescribing in a geographically defined U.S. population, and to determine the indications and factors that account for it. We conducted a cohort study of new antidepressant prescriptions for elderly residents of Olmsted County, Minnesota, 2005‐2012, using the Rochester Epidemiology Project medical records‐linkage system. Indications for antidepressants were abstracted from health records for all cohort members. Potential antidepressant overprescribing was defined based on regulatory approval, the level of evidence identified from a standardized drug information database, and multidisciplinary expert review. Predictors of potential antidepressant overprescribing were investigated using logistic regression models, stratified by general antidepressant indication (general medical indication, specific psychiatric diagnosis, and non‐specific psychiatric symptoms). Potential antidepressant overprescribing occurred in 24% of 3199 incident antidepressant prescriptions during the study period, and involved primarily newer antidepressants that were prescribed for non‐specific psychiatric symptoms and subthreshold diagnoses. Potential antidepressant overprescribing was associated with nursing home residence, having a higher number of comorbid medical conditions and outpatient prescribers, taking more concomitant medications, having greater use of urgent or acute care services in the year preceding the index antidepressant prescription, and being prescribed antidepressants via telephone, e‐mail, or patient portal. In conclusion, potential antidepressant overprescribing occurred in elderly persons and involved mainly newer antidepressants used for non‐specific psychiatric symptoms and subthreshold diagnoses, and was associated with indicators of higher clinical complexity or severity and with prescribing without face‐to‐face patient contact.