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A cornerstone of for-profit schools’ business model is to encourage students to borrow as much as possible to finance their college attendance. After drawing people in with misleading advertisements, for-profit colleges quickly hand prospective students off to financial aid officers who rush students through the process of explaining the financial aid system—if they explain it at all. These employees drive students to take on massive federal and private debt loads, including loans that the school itself originates. They will sometimes fill out or even fraudulently sign forms on students’ behalf, all the while insisting that they are working in the students’ interest. Each of these tactics has one goal: boosting schools’ revenues by inflating students’ debt balances, regardless of how unmanageable those loans are likely to be for borrowers. This issue brief argues that each of these areas of conduct place for-profit colleges squarely within the authority of the Consumer Financial Protection Bureau (CFPB), and that the Bureau can therefore intervene to protect borrowers. The CFPB’s authorizing statute states that anyone providing a consumer financial product or service is a “covered person” and therefore falls under the Bureau’s purview, including with respect to the prohibition on unfair, deceptive, and abusive acts and practices. As this issue brief outlines, each one of the common features of the for-profit college business model described above is alone sufficient to make a for-profit college a “covered person,” including the practices of lending to students through institutional loan programs, brokering student loans through third parties, and providing students financial advisory services in the financial aid process (regardless of the quality of the advice). With for-profit college enrollment surging due to COVID, the need to rein in the industry has never been more pressing. The CFPB has the tools to combat the rampant illegal practices that the for-profit college industry relies on.

Objectives Clinical variables were investigated in the ‘treatment resistant depression (TRD)- III’ sample to replicate earlier findings by the European research consortium ‘Group for the Study of Resistant Depression’ (GSRD) and enable cross-sample prediction of treatment outcome in TRD. Experimental procedures TRD was defined by a Montgomery and angstrom sberg Depression Rating Scale (MADRS) score >= 22 after at least two antidepressive trials. Response was defined by a decline in MADRS score by >= 50% and below a threshold of 22. Logistic regression was applied to replicate predictors for TRD among 16 clinical variables in 916 patients. Elastic net regression was applied for prediction of treatment outcome. Results Symptom severity (odds ratio (OR) = 3.31), psychotic symptoms (OR = 2.52), suicidal risk (OR = 1.74), generalized anxiety disorder (OR = 1.68), inpatient status (OR = 1.65), higher number of antidepressants administered previously (OR = 1.23), and lifetime depressive episodes (OR = 1.15) as well as longer duration of the current episode (OR = 1.022) increased the risk of TRD. Prediction of TRD reached an accuracy of 0.86 in the independent validation set, TRD-I. Conclusion Symptom severity, suicidal risk, higher number of lifetime depressive episodes, and comorbid anxiety disorder were replicated as the most prominent risk factors for TRD. Significant predictors in TRD-III enabled robust prediction of treatment outcome in TRD-I.

Background: This European multicenter study aimed to elucidate suicidality in major depressive disorder. Previous surveys suggest a prevalence of suicidality in major depressive disorder of ≥50%, but little is known about the association of different degrees of suicidality with socio-demographic, psychosocial, and clinical characteristics. Methods: We stratified 1410 major depressive disorder patients into 3 categories of suicidality based on the Hamilton Rating Scale for Depression item 3 (suicidality) ratings (0 = no suicidality; 1–2 = mild/moderate suicidality; 3–4 = severe suicidality). Chi-squared tests, analyses of covariance, and Spearman correlation analyses were applied for the data analyses. Results: The prevalence rate of suicidality in major depressive disorder amounted to 46.67% (Hamilton Rating Scale for Depression item 3 score ≥1). 53.33% were allocated into the no, 38.44% into the mild/moderate, and 8.23% into the severe suicidality patient group. Due to the stratification of our major depressive disorder patient sample according to different levels of suicidality, we identified some socio-demographic, psychosocial, and clinical variables differentiating from the patient group without suicidality already in presence of mild/moderate suicidality (depressive symptom severity, treatment resistance, psychotic features, add-on medications in general), whereas others separated only when severe suicidality was manifest (inpatient treatment, augmentation with antipsychotics and benzodiazepines, melancholic features, somatic comorbidities). Conclusions: As even mild/moderate suicidality is associated with a failure of achieving treatment response, adequate recognition of this condition should be ensured in the clinical practice. © The Author(s) 2018. Published by Oxford University Press on behalf of CINP.

Background: As many patients with unipolar depression do not respond sufficiently to initial antidepressant monotherapy, a dose increase of the current administered antidepressant (dose escalation, high-dose treatment) is frequently carried out as next treatment measure. Methods: We conducted a meta-analysis which included all double-blind randomized controlled trials (RCTs) comparing a dose increase of antidepressants directly to continuation of standard-dose treatment in unipolar depressive patients who were non- responders to standard-dose pharmacotherapy. A mean change in the Hamilton Rating Scale for Depression (HAM-D) total score was the primary outcome. Secondary outcomes were response rates and discontinuation rates due to any reason, inefficacy, and adverse effects. Hedges g and risk ratios were calculated as effect sizes. Results: Seven double-blind RCTs (8 study arms) representing 1,208 participants were included. Fluoxetine (N [number of studies] = 2, n [number of patients] = 448), sertraline (N = 2, n = 272), paroxetine (N = 2, n = 146), duloxetine (N = 1, n = 255), and maprotiline (N = 1, n = 87) were investigated. Dose escalation was not more efficacious in HAM-D total score reduction than maintaining standard-dose treatment, neither for the pooled antidepressant group (N = 7, n = 999; Hedges g = -0.04, 95% CI: -0.20 to 0.12; p = 0.63) nor the individual antidepressants. No differences could be determined for response rates, all-cause discontinuation, and drop-outs due to inefficacy. Significantly more patients in the dose escalation group dropped out due to adverse effects than in the standard-dose continuation group. The metaregressions indicate no influence of baseline symptom severity or amounts of dose increments on effect sizes. Conclusions: According to our meta-analytic findings, dose escalation after initial non-response to standard-dose pharmacotherapy cannot be regarded as general evidence-based treatment option in unipolar depression.