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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Methods: 3,742 individuals with T1D age ≥50 were followed for dementia from 1/1/96-9/30/2015. Depression, dementia, and comorbidities were abstracted from electronic medical records. Cox proportional hazard models estimated the association between depression and dementia adjusting for demographics, glycosylated hemoglobin, severe dysglycemic epidsodes, stroke, heart disease, nephropathy, and end stage renal disease. The cumulative incidence of dementia by depression was estimated conditional on survival dementia-free to age 55.
Results: Five percent (N = 182) were diagnosed with dementia and 20% had baseline depression. Depression was associated with a 72% increase in dementia (fully adjusted HR = 1.72; 95% CI:1.12-2.65). The 25-year cumulative incidence of dementia was more than double for those with versus without depression (27% vs. 12%).
Conclusions: For people with T1D, depression significantly increases dementia risk. Given the pervasiveness of depression in T1D, this has major implications for successful aging in this population recently living to old age. 相似文献