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可夫模型，陈述偏好数据和计量经济学的组合 采用治疗模式来计算新的治疗方法的成本、效益、社会价值和健康公平影响， 治疗或阿尔茨海默病和阿尔茨海默病相关痴呆（AD/ADRD）。本课题 将研究AD/ADRD新型治疗方法的成本效益模型对健康公平性的影响 使用马尔可夫模型的组合，来自文献和临床试验的数据，基于社区的 参与式研究（CBPR）与焦点小组和实验偏好数据。首先，我们将更新我们的 现有的成本效益马尔可夫模型（为aducanumab和donanemab治疗早期阿尔茨海默病而开发 疾病在美国），将种族特定的输入到开始值和转移概率。 接下来，我们将通过与风险非洲裔美国人/黑人进行AD/ADRD焦点小组来参与CBPR。 在一个贫穷，服务不足的地区（里士满，弗吉尼亚州）的个人。在这方面，我们将利用现有的关系， 与致力于健康老龄化的社区合作伙伴合作。我们还将与护理人员进行焦点小组讨论 来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 使用来自离散的实验性陈述偏好数据在不同人群中降低AD/ADRD的值 选择实验。最后，我们将使用潜在的 选择和混合logit模型来控制观察到的和未观察到的偏好异质性， 在不同种族群体之间和种族群体内部。我们将校准我们的模型， 代表性的医疗保险数据，以模拟接受率，以及新治疗的后续成本和收益 加上健康公平的影响。最后，我们将根据采用率开发模拟模型， 确定可用于确保新疗法改善卫生公平性的政策变化。

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.