There is optimism that biosimilars will offer lower-cost therapeutic alternatives to branded biologic drugs, in the same manner as generic drugs have done for small-molecule drugs. On average, the ...cost of a small-molecule drug declines by 70% in the 24 months following initial generic approval. Furthermore, generic drugs currently account for 88% of all US prescriptions, yielding 10-year cost savings in excess of $1.5 trillion. Here, Hakim and Ross explore why biosimilars for chronic diseases, the largest category of biological therapies, are unlikely to yield widely expected cost savings.
Off-label prescribing of psychiatric drugs is common, despite lacking strong scientific evidence of efficacy and potentially increasing risk for adverse events. The goal of this study was to ...characterize prevalence of off-label prescriptions of psychiatric drugs and examine patient and clinician predictors of off-label use. This manuscript presents a retrospective, cross-sectional study using data from the 2012 and 2013 National Ambulatory Medical Care Surveys (NAMCS). The study examined all adult outpatient visits to psychiatric practices for chronic care management with a single listed visit diagnosis in which at least one psychiatric drug was prescribed. The main outcome measure was off-label prescribing of at least one psychiatric drug, defined as prescription for a condition for which it has not been approved for use by the FDA. Among our sample representative of 1.85 billion outpatient visits, 18.5 million (1.3%) visits were to psychiatrists for chronic care management in which at least one psychiatric drug was prescribed. Overall, the rate of off-label use was 12.9% (95% CI: 12.2-15.7). The most common off-label uses were for manic-depressive psychosis treated with citalopram and primary insomnia treated with trazodone. Several patient and clinician characteristics were positively associated with off-label prescribing, including seeing a psychiatrist (OR: 1.06, 95% CI, 1.01-1.12; p = 0.03) instead of another type of clinician, the office visit taking place in the Western region of the country (OR: 1.09, 95% CI, 1.01-1.17; p = 0.02), and the patient having 3 or more chronic conditions (OR: 1.12, 95% CI, 1.02-1.14; p = 0.003). In contrast, having Medicare coverage (OR: 0.93, 95% CI, 0.84-0.97; p = 0.04) and receiving payment assistance from a medical charity (OR: 0.91, 95% CI, 0.88-0.96; p = 0.03) instead of private insurance were negatively associated with off-label prescribing. These results suggest that certain classes of psychiatric medications are being commonly prescribed to treat conditions for which they have not been determined by the FDA to be clinically efficacious and/or safe.
Ravi Gupta and colleagues argue that we need to reconsider standards for targeted pharmaceutical marketing to doctors through speaker programmes, consulting programmes, and advisory board positions
Heterogeneity of treatment effect (HTE) refers to the nonrandom variation in the magnitude or direction of a treatment effect across levels of a covariate, as measured on a selected scale, against a ...clinical outcome. In randomized controlled trials (RCTs), HTE is typically examined through a subgroup analysis that contrasts effects in groups of patients defined "1 variable at a time" (for example, male vs. female or old vs. young). The authors of this statement present guidance on an alternative approach to HTE analysis, "predictive HTE analysis." The goal of predictive HTE analysis is to provide patient-centered estimates of outcome risks with versus without the intervention, taking into account all relevant patient attributes simultaneously. The PATH (Predictive Approaches to Treatment effect Heterogeneity) Statement was developed using a multidisciplinary technical expert panel, targeted literature reviews, simulations to characterize potential problems with predictive approaches, and a deliberative process engaging the expert panel. The authors distinguish 2 categories of predictive HTE approaches: a "risk-modeling" approach, wherein a multivariable model predicts the risk for an outcome and is applied to disaggregate patients within RCTs to define risk-based variation in benefit, and an "effect-modeling" approach, wherein a model is developed on RCT data by incorporating a term for treatment assignment and interactions between treatment and baseline covariates. Both approaches can be used to predict differential absolute treatment effects, the most relevant scale for clinical decision making. The authors developed 4 sets of guidance: criteria to determine when risk-modeling approaches are likely to identify clinically important HTE, methodological aspects of risk-modeling methods, considerations for translation to clinical practice, and considerations and caveats in the use of effect-modeling approaches. The PATH Statement, together with its explanation and elaboration document, may guide future analyses and reporting of RCTs.
IMPORTANCE: Readmission rates declined after announcement of the Hospital Readmission Reduction Program (HRRP), which penalizes hospitals for excess readmissions for acute myocardial infarction ...(AMI), heart failure (HF), and pneumonia. OBJECTIVE: To compare trends in readmission rates for target and nontarget conditions, stratified by hospital penalty status. DESIGN, SETTING, AND PARTICIPANTS: Retrospective cohort study of Medicare fee-for-service beneficiaries older than 64 years discharged between January 1, 2008, and June 30, 2015, from 2214 penalty hospitals and 1283 nonpenalty hospitals. Difference-interrupted time-series models were used to compare trends in readmission rates by condition and penalty status. EXPOSURE: Hospital penalty status or target condition under the HRRP. MAIN OUTCOMES AND MEASURES: Thirty-day risk adjusted, all-cause unplanned readmission rates for target and nontarget conditions. RESULTS: The study included 48 137 102 hospitalizations of 20 351 161 Medicare beneficiaries. In January 2008, the mean readmission rates for AMI, HF, pneumonia, and nontarget conditions were 21.9%, 27.5%, 20.1%, and 18.4%, respectively, at hospitals later subject to financial penalties and 18.7%, 24.2%, 17.4%, and 15.7% at hospitals not subject to penalties. Between January 2008 and March 2010, prior to HRRP announcement, readmission rates were stable across hospitals (except AMI at nonpenalty hospitals). Following announcement of HRRP (March 2010), readmission rates for both target and nontarget conditions declined significantly faster for patients at hospitals later subject to financial penalties compared with those at nonpenalized hospitals (for AMI, additional decrease of −1.24 95% CI, −1.84 to −0.65 percentage points per year relative to nonpenalty discharges; for HF, −1.25 95% CI, −1.64 to −0.86; for pneumonia, −1.37 95% CI, −1.80 to −0.95; and for nontarget conditions, −0.27 95% CI, −0.38 to −0.17; P < .001 for all). For penalty hospitals, readmission rates for target conditions declined significantly faster compared with nontarget conditions (for AMI, additional decline of −0.49 95% CI, −0.81 to −0.16 percentage points per year relative to nontarget conditions P = .004; for HF, −0.90 95% CI, −1.18 to −0.62; P < .001; and for pneumonia, −0.57 95% CI, −0.92 to −0.23; P < .001). In contrast, among nonpenalty hospitals, readmissions for target conditions declined similarly or more slowly compared with nontarget conditions (for AMI, additional increase of 0.48 95% CI, 0.01-0.95 percentage points per year P = .05; for HF, 0.08 95% CI, −0.30 to 0.46; P = .67; for pneumonia, 0.53 95% CI, 0.13-0.93; P = .01). After HRRP implementation in October 2012, the rate of change for readmission rates plateaued (P < .05 for all except pneumonia at nonpenalty hospitals), with the greatest relative change observed among hospitals subject to financial penalty. CONCLUSIONS AND RELEVANCE: Medicare fee-for-service patients at hospitals subject to penalties under the HRRP had greater reductions in readmission rates compared with those at nonpenalized hospitals. Changes were greater for target vs nontarget conditions for patients at the penalized hospitals but not at the other hospitals.
This cross-sectional study examines the ties between pharmaceutical and medical device industries and the executive leadership of the 50 highest-revenue US-based patient advocacy organizations.
PDF
