Lacosamide as a first-line treatment option in focal epilepsy: a cost-utility analysis for the Greek healthcare system

Deep Thoughts—Predicting Initial Treatment Response in Newly Diagnosed Epilepsy

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Changes in the Use of Brand Name and Generic Medications and Total Prescription Cost Among Medicare Beneficiaries With Epilepsy

BACKGROUND AND OBJECTIVE

To characterize trends in antiseizure medication (ASM) fills and total prescription costs in people with epilepsy.

METHODS

This was a retrospective cohort study of beneficiaries with epilepsy (ASM, plus ICD codes) in a 20% random Medicare sample, with continuous Fee-For-Service coverage (Parts A, B, and D) in 2008-2018. We summed the number of pill days and costs (adjusted to 2018 dollars) per person-year for each ASM. ASMs were categorized into brand vs generic, first vs newer generation, and enzyme inducers vs noninducers.

RESULTS

There were 77,000-133,000 beneficiaries with epilepsy per year. The most common ASM was phenytoin in 2008, which shifted to levetiracetam in 2018 (2008: phenytoin 25%, levetiracetam 14%; 2018: phenytoin 9%, levetiracetam 27%). Brand name (2008: 56%; 2018: 14%), first-generation (2008: 55%; 2018: 32%), and enzyme-inducing ASMs (2008: 44%; 2018: 24%) each decreased over time as a proportion of pill days. The number of brand pill days per person-year initially decreased (e.g., 2008: 250; 2009: 121; 2010: 96) but then plateaued (2013-2018: between 66 and 69) given a notable increase in lacosamide pill days per person (2008: 0; 2018: 20). Total brand name costs per year initially decreased 2008-2010 (2008: $150 million; 2010: $72 million) but then increased after 2010 (2018: $256 million). In 2018, brand name ASMs represented 79% of costs despite representing only 14% of pill days, a 1-year pill supply became 277% more expensive for brand name medications but 42% less expensive for generic medications over time (2008: brand ∼$2,800 vs generic ∼$800; 2018: brand ∼$10,700 vs generic ∼$460), and many common brand name ASMs cost approximately 10-fold more per pill day than their generic equivalents.

DISCUSSION

First-generation and enzyme-inducing ASMs waned from 2008 to 2018. Although brand name ASMs initially waned translating into lower costs and potentially higher value care, after 2010, brand name costs markedly increased because of increasing use of lacosamide plus a 277% increase in per-pill cost of brand name ASMs. Brand name ASMs represented a minority of prescriptions, but the majority of costs.

Discrete-Event Simulation Modeling in Healthcare: A Comprehensive Review

Discrete-event simulation (DES) is a stochastic modeling approach widely used to address dynamic and complex systems, such as healthcare. In this review, academic databases were systematically searched to identify 231 papers focused on DES modeling in healthcare. These studies were sorted by year, approach, healthcare setting, outcome, provenance, and software use. Among the surveys, conceptual/theoretical studies, reviews, and case studies, it was found that almost two-thirds of the theoretical articles discuss models that include DES along with other analytical techniques, such as optimization and lean/six sigma, and one-third of the applications were carried out in more than one healthcare setting, with emergency departments being the most popular. Moreover, half of the applications seek to improve time- and efficiency-related metrics, and one-third of all papers use hybrid models. Finally, the most popular DES software is Arena and Simul8. Overall, there is an increasing trend towards using DES in healthcare to address issues at an operational level, yet less than 10% of DES applications present actual implementations following the modeling stage. Thus, future research should focus on the implementation of the models to assess their impact on healthcare processes, patients, and, possibly, their clinical value. Other areas are DES studies that emphasize their methodological formulation, as well as the development of frameworks for hybrid models.

How to Address Uncertainty in Health Economic Discrete-Event Simulation Models: An Illustration for Chronic Obstructive Pulmonary Disease

Background. Evaluation of personalized treatment options requires health economic models that include multiple patient characteristics. Patient-level discrete-event simulation (DES) models are deemed appropriate because of their ability to simulate a variety of characteristics and treatment pathways. However, DES models are scarce in the literature, and details about their methods are often missing. Methods. We describe 4 challenges associated with modeling heterogeneity and structural, stochastic, and parameter uncertainty that can be encountered during the development of DES models. We explain why these are important and how to correctly implement them. To illustrate the impact of the modeling choices discussed, we use (results of) a model for chronic obstructive pulmonary disease (COPD) as a case study. Results. The results from the case study showed that, under a correct implementation of the uncertainty in the model, a hypothetical intervention can be deemed as cost-effective. The consequences of incorrect modeling uncertainty included an increase in the incremental cost-effectiveness ratio ranging from 50% to almost a factor of 14, an extended life expectancy of approximately 1.4 years, and an enormously increased uncertainty around the model outcomes. Thus, modeling uncertainty incorrectly can have substantial implications for decision making. Conclusions. This article provides guidance on the implementation of uncertainty in DES models and improves the transparency of reporting uncertainty methods. The COPD case study illustrates the issues described in the article and helps understanding them better. The model R code shows how the uncertainty was implemented. For readers not familiar with R, the model's pseudo-code can be used to understand how the model works. By doing this, we can help other developers, who are likely to face similar challenges to those described here.