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Background
Payers frequently rely on budget impact model (BIM) results to help determine drug coverage policy and its effect on their bottom line. It is unclear whether BIMs typically overestimate or underestimate real-world budget impact.Objective
We examined how different modeling assumptions influenced the results of 6 BIMs from the Institute for Clinical and Economic Review (ICER).Study Design
Retrospective analysis of pharmaceutical sales data.Methods
From ICER reports issued before 2016, we collected estimates of 3 BIM outputs: aggregate therapy cost (ie, cost to treat the patient population with a particular therapy), therapy uptake, and price. We compared these against real-world estimates that we generated using drug sales data. We considered 2 classes of BIM estimates: those forecasting future uptake of new agents, which assumed “unmanaged uptake,” and those describing the contemporaneous market state (ie, estimates of current, managed uptake and budget impact for compounds already on the market).Results
Differences between ICER's estimates and our own were largest for forecasted studies. Here, ICER's uptake estimates exceeded real-world estimates by factors ranging from 7.4 (sacubitril/valsartan) to 54 (hepatitis C treatments). The “unmanaged uptake” assumption (removed from ICER's approach in 2017) yields large deviations between BIM estimates and real-world consumption. Nevertheless, in some cases, ICER's BIMs that relied on current market estimates also deviated substantially from real-world sales data.Conclusions
This study highlights challenges with forecasting budget impact. In particular, assumptions about uptake and data source selection can greatly influence the accuracy of results. 相似文献- Download : Download high-res image (228KB)
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Robotic approaches have been steadily replacing laparoscopic approaches in metabolic and bariatric surgeries (MBS); however, their superiority has not been rigorously evaluated. The main goal of the study was to evaluate the 5-year utilization trends of robotic MBS and to compare to laparoscopic outcomes.
MethodsRetrospective analysis of 2015–2019 MBSAQIP data. Kruskal-Wallis test/Wilcoxon and Fisher’s exact/chi-square were used to compare continuous and categorical variables, respectively. Generalized linear models were used to compare surgery outcomes.
ResultsThe use of robotic MBS increased from 6.2% in 2015 to 13.5% in 2019 (N= 775,258). Robotic MBS patients had significantly higher age, BMI, and likelihood of 12 diseases compared to laparoscopic patients. After adjustment, robotic MBS patients showed higher 30-day interventions and 30-day readmissions alongside longer surgery time (26–38 min).
ConclusionRobotic MBS shows higher intervention and readmission even after controlling for cofounding variables.
Graphical Abstract