Measuring the Impact of the Value Flower and Unobserved Heterogeneity on the Cost Effectiveness and Use of Novel Treatments for Alzheimer's Disease and Related Dementias

项目摘要 这个项目的目的是使用马尔科夫模型、陈述的偏好数据和计量经济学的组合。 计算新型药物的成本、收益、社会价值和健康公平影响的治疗采纳量模型 治疗或阿尔茨海默病和阿尔茨海默病相关痴呆(AD/ADRD)。在这个项目中，我们 将研究AD/ADRD新疗法的成本效益模型对健康公平的影响 使用基于社区的马尔可夫模型、文献数据和临床试验数据的组合 具有焦点群体和实验偏好数据的参与性研究(CBPR)。首先，我们将更新我们的 现有的成本效益马尔可夫模型(为阿杜卡努单抗和多纳单抗开发，用于早期阿尔茨海默病 美国的疾病)将特定种族的投入纳入起始值和转移概率。 接下来，我们将与面临风险的非裔美国人/黑人一起，就AD/ADRD开展焦点小组，参与CBPR 贫穷、服务不足地区的个人(弗吉尼亚州里士满)。在这方面，我们将利用预先存在的关系来 与致力于健康老龄化的社区合作伙伴接触。我们还将与照顾者一起开展焦点小组 了解照顾者负担的影响。我们将使用模型的结果来衡量经济 利用离散型实验陈述偏好数据降低不同人群AD/ADRD的价值 选择实验。最后，我们将使用潜在的组合来估计治疗接受率的模型 控制可观测和未观测偏好异质性的选择和混合Logit模型 在不同种族群体之间和种族群体内部。我们将在全国范围内校准我们的模型 具有代表性的医疗保险数据，以模拟接受率，以及新疗法的后续成本和收益 再加上健康公平的影响。最后，我们将开发基于使用率的模拟模型，以 确定可用于确保新疗法改善卫生公平的政策变化。

Project Summary This purpose of this project is to use a combination of Markov models, stated preference data and econometric models of treatment take-up to calculate the costs, benefits, societal value and health equity impacts of novel treatments or Alzheimer disease and Alzheimer's disease-related dementias (AD/ADRD). In this project, we will study the health equity implications of the cost effectiveness models new novel treatments for AD/ADRD using a combination of Markov models, data from the literature and clinical trials, community based participatory research (CBPR) with focus groups and experimental preference data. First, we will update our existing cost effectiveness Markov model (developed for aducanumab and donanemab for early Alzheimer disease in the United States) in incorporate race specific inputs into the start values and transition probabilities. Next, we will engage in CBPR by conducting focus groups on AD/ADRD with at-risk African-American/Black individuals in a poor, underserved area (Richmond, VA). In this, we will leverage pre-existing relationships to engage with community partners working on healthy aging. We will also conduct focus groups with caregivers to understand the effect of caregiver burden. We will use the results of the models to measure the economic value of reducing AD/ADRD in different populations using experimental stated preference data from Discrete Choice Experiments. Finally, we will estimate models of treatment take-up using a combination of latent choice and mixed logit models to control for both observed and unobserved preference heterogeneity both between different racial groups and within racial groups. We will calibrate our models using nationally representative Medicare data to simulate take-up rates, and subsequent costs and benefits to new treatments plus the impact of health equity. Finally, we will develop simulation models based on the take-up rates to identify policy changes that could be used to ensure that new treatment improve health equity.