Dataset Information

Access to Disease-Modifying Alzheimer's Therapies: Addressing Possible Challenges Using Innovative Payment Models.

ABSTRACT:

Objectives

Aduhelm is the first approved disease-modifying therapies (DMT) for Alzheimer disease (AD). Nevertheless, under current payment models, AD DMTs-especially because they treat broader populations-will pose challenges to patient access since costs may accrue sooner than benefits do. New payment approaches may be needed to address this difference in timing.

Methods

We use the Future Elderly Model that draws on nationally representative data sets such as the Health and Retirement Study to estimate the potential benefits because of hypothetical AD DMTs in 4 stylized treatment scenarios for patients with mild cognitive impairment or mild AD, and develop a payment model to estimate the accrual of net costs and benefits to private and public payers.

Results

The modeled AD DMTs result in clinical benefit of 0.30 to 0.55 quality-adjusted life-years gained per patient in the baseline treatment scenario and 0.13 to 0.24 quality-adjusted life-years gained per patient in the least optimistic scenario. Private payers may observe a net loss in patients at the age of 61 to 65 years under the status quo (payment upon treatment). Constant and deferred installment payment models resolve this issue.

Conclusions

Innovative payment solutions, such as installment payments, may be required to address misaligned incentives that AD DMTs may create among patients younger than the age of 65 years and may help address concerns about the timing and magnitude of costs and benefits accrued to private payers.

SUBMITTER: Hlavka JP

PROVIDER: S-EPMC9813270 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Access to Disease-Modifying Alzheimer's Therapies: Addressing Possible Challenges Using Innovative Payment Models.

Hlávka Jakub P JP Tysinger Bryan B Yu Jeffrey C JC Lakdawalla Darius N DN

Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research 20220705 11

<h4>Objectives</h4>Aduhelm is the first approved disease-modifying therapies (DMT) for Alzheimer disease (AD). Nevertheless, under current payment models, AD DMTs-especially because they treat broader populations-will pose challenges to patient access since costs may accrue sooner than benefits do. New payment approaches may be needed to address this difference in timing.<h4>Methods</h4>We use the Future Elderly Model that draws on nationally representative data sets such as the Health and Retir ...[more]

PMID: 35803845

Similar Datasets

Project description:ImportanceHepatitis C virus (HCV) can be cured with direct-acting antiviral medications, but state Medicaid programs often restrict access to these lifesaving medications owing to their high costs. Subscription-based payment models (SBPMs), wherein states contract with a single manufacturer to supply prescriptions at a reduced price, may offer a solution that increases access. Whether SBPMs are associated with changes in HCV medication use is unknown.ObjectiveTo estimate changes in Medicaid-covered HCV prescription fills after Louisiana and Washington implemented SBPMs on July 1, 2019.Design setting and participantsThis cross-sectional study examined trends in prescription fills of Medicaid-covered direct-acting antiviral HCV medications in Louisiana and Washington after implementation of SBPMs. A synthetic control approach was used to compare changes in HCV prescription fills between states that did and did not implement SBPMs. The unit of analysis was state-quarter. Outpatient direct-acting antiviral HCV prescription fills from the Medicaid State Drug Utilization Data files were obtained from all 50 US states and the District of Columbia from January 1, 2017, to June 30, 2020.ExposuresImplementation of SBPMs for Medicaid-covered direct-acting antiviral HCV medications.Main outcomes and measuresDirect-acting antiviral HCV prescriptions filled per 100 000 Medicaid enrollees.ResultsIn the year preceding SBPM implementation, the mean (SD) rate of quarterly HCV prescription fills per 100 000 Medicaid enrollees was 43.1 (8.6) prescriptions in Louisiana and 50.1 (4.1) in Washington. After SBPM implementation, the mean (SD) rate of quarterly HCV prescription fills per 100 000 enrollees was 206.0 (51.2) prescriptions in Louisiana and 53.9 (11.0) in Washington. In synthetic control models, SBPM implementation in Louisiana was associated with an increase of 173.5 (95% CI, 74.3-265.3) quarterly prescription fills per 100 000 Medicaid enrollees during the following year, a relative increase of 534.5% (95% CI, 228.7%-1125.0%). Washington did not experience a significant change in prescription fills following SBPM implementation.Conclusions and relevanceIn this cross-sectional study, Louisiana experienced substantial increases in HCV medication use among its Medicaid-enrolled population following SBPM implementation, whereas Washington did not. These differences may partially be explained by state-level variation in SBPM implementation, historical restrictions on access to HCV medications, and responses to the COVID-19 pandemic.

Project description:ObjectivesOpen-access cancer imaging datasets have become integral for evaluating novel AI approaches in radiology. However, their use in quantitative analysis with radiomics features presents unique challenges, such as incomplete documentation, low visibility, non-uniform data formats, data inhomogeneity, and complex preprocessing. These issues may cause problems with reproducibility and standardization in radiomics studies.MethodsWe systematically reviewed imaging datasets with public copyright licenses, published up to March 2023 across four large online cancer imaging archives. We included only datasets with tomographic images (CT, MRI, or PET), segmentations, and clinical annotations, specifically identifying those suitable for radiomics research. Reproducible preprocessing and feature extraction were performed for each dataset to enable their easy reuse.ResultsWe discovered 29 datasets with corresponding segmentations and labels in the form of health outcomes, tumor pathology, staging, imaging-based scores, genetic markers, or repeated imaging. We compiled a repository encompassing 10,354 patients and 49,515 scans. Of the 29 datasets, 15 were licensed under Creative Commons licenses, allowing both non-commercial and commercial usage and redistribution, while others featured custom or restricted licenses. Studies spanned from the early 1990s to 2021, with the majority concluding after 2013. Seven different formats were used for the imaging data. Preprocessing and feature extraction were successfully performed for each dataset.ConclusionRadiomicsHub is a comprehensive public repository with radiomics features derived from a systematic review of public cancer imaging datasets. By converting all datasets to a standardized format and ensuring reproducible and traceable processing, RadiomicsHub addresses key reproducibility and standardization challenges in radiomics.Critical relevance statementThis study critically addresses the challenges associated with locating, preprocessing, and extracting quantitative features from open-access datasets, to facilitate more robust and reliable evaluations of radiomics models.Key points- Through a systematic review, we identified 29 cancer imaging datasets suitable for radiomics research. - A public repository with collection overview and radiomics features, encompassing 10,354 patients and 49,515 scans, was compiled. - Most datasets can be shared, used, and built upon freely under a Creative Commons license. - All 29 identified datasets have been converted into a common format to enable reproducible radiomics feature extraction.

Dataset Information

Access to Disease-Modifying Alzheimer's Therapies: Addressing Possible Challenges Using Innovative Payment Models.

Objectives

Methods

Results

Conclusions

Publications

Access to Disease-Modifying Alzheimer's Therapies: Addressing Possible Challenges Using Innovative Payment Models.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets