Public Insurance Design and Health at Older Ages

公共保险设计与老年人健康

• 批准号: 10370342
• 负责人: Maria Polyakova
• 金额: $ 12.67万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-05-15 至 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10370342
• 关键词: Address; Admission activity; Affect; Age; Alzheimer' s disease related dementia; American; Amyloid; Anxiety; Barbiturates; Benzodiazepines; Caring; Characteristics; Clinical Medicine; Cost Sharing; Coupled; Data; Diagnostic; Diagnostic Procedure; Drug Prescriptions; Drug Targeting; Economics; Effectiveness; Elderly; Ethnic Origin; Exposure to; Face; Family Physicians; Formularies; Fracture; Frequencies; Future; Geography; Geriatrics; Gerontology; Goals; Health; Health Benefit; Health Insurance; Heart; Heterogeneity; Hospitalization; Individual; Inequality; Insurance; Insurance Coverage; Investigation; Knowledge; Learning Skill; Length of Stay; Light; Machine Learning; Measures; Medical; Medicare; Medicare Part B; Mental Depression; Mentors; Methods; Movement; Natural experiment; Nature; Office Visits; Outcome; Outcome Measure; Outpatients; Pain; Patients; Pharmaceutical Preparations; Pharmacology; Physicians; Policies; Policy Maker; Population; Positron-Emission Tomography; Procedures; Public Policy; Quasi-experiment; Race; Recording of previous events; Research; Services; Skilled Nursing Facilities; Source; Subgroup; System; Testing; Therapeutic procedure; Time; Training; Universal Coverage; Variant; aging population; alternative treatment; bariatric surgery; base; clinical practice; comorbidity; decision-making capacity; design; experience; high dimensionality; innovation; insurance claims; machine learning method; mortality; older patient; patient population; patient subsets; precision medicine; predictive modeling; programs; public health insurance; response; socioeconomics; theories; tool; treatment choice; treatment effect; treatment response

PROJECT ABSTRACT Health insurance has been at the forefront of US public policy debate throughout the last decade. For elderly Americans, who benefit from nearly universal coverage under Medicare, decisions about the scope of their insurance coverage have been a central policy concern since Medicare’s inception in 1965. Medicare’s coverage is extensive, but by statute the program only covers medical services that are “reasonable and necessary,” which can be a controversial definition. Some decisions about what Medicare should cover have engendered intense debate, underscoring the importance of the program’s coverage decisions for millions of patients and doctors. The debate around the scope of Medicare coverage is likely to intensify in the coming decades as options for medical treatment, and testing, expand rapidly, the population ages, and public insurance systems grapple with how to address inequality in access to medical innovations. Yet, despite the importance of decisions about the scope of insurance coverage in and outside of Medicare, we know little about how the scope of coverage (as opposed to patient cost-sharing), affects treatment choices, clinical practice, and health outcomes for the elderly. The research outlined in this proposal aims to start filling this gap. The project investigates how the presence or lack of insurance coverage for specific procedural or pharmacologic therapies affects treatment decisions and health outcomes for the elderly with Alzheimer’s Disease and related Dementias (ADRD). This is a population that may be particularly vulnerable to changes and limits in insurance coverage, as the patients may have limited decision-making capacity and may be disproportionately exposed to treatments that are deemed experimental and lacking effectiveness to clear the “reasonable and necessary” threshold. Aim 1 of the project is to estimate the average effect of coverage decisions across prescription drugs and outpatient procedures on treatment decisions and health outcomes of elderly Medicare enrollees with ADRD. Aim 2 is to predict and characterize the subgroups of ADRD patients that are most likely to be affected by decisions that restrict the scope of insurance coverage using machine learning methods. The proposed empirical method is to use quasi- experimental variation that arises from natural experiments of abrupt changes in insurance coverage within different parts of the Medicare program. The analysis takes advantage of variation in the scope of formularies across Medicare Part D plans, as well as the variation in local coverage decisions for physician services and outpatient procedures under Medicare Part B. These sources of variation coupled with methods for quasi- experimental estimation of treatment effects and machine learning methods for heterogeneity analyses, allow estimating the response of treatment decisions and health of the elderly with ADRD across different drugs, procedures, and subgroups of ADRD patients.

项目摘要 在过去的十年里，医疗保险一直处于美国公共政策辩论的前沿。老年 美国人，谁受益于几乎全民覆盖的医疗保险，决定他们的范围， 自1965年医疗保险制度建立以来，保险范围一直是一个中心政策问题。Medicare的覆盖范围 是广泛的，但根据法规，该计划只涵盖“合理和必要”的医疗服务， 这可能是一个有争议的定义。一些关于医疗保险应该覆盖什么的决定引起了激烈的争论。 辩论，强调该计划的覆盖决定对数百万患者和医生的重要性。 围绕医疗保险覆盖范围的辩论可能会在未来几十年内加剧， 医疗和检测迅速扩大，人口老龄化，公共保险系统努力应对 如何解决获得医疗创新方面的不平等问题。然而，尽管有关决定的重要性， 在医疗保险范围内和医疗保险范围外，我们对保险范围（如 相对于患者费用分摊），影响治疗选择，临床实践和老年人的健康结果。 本提案中概述的研究旨在开始填补这一空白。该项目调查了如何存在或 缺乏特定程序或药物治疗的保险覆盖影响治疗决定， 阿尔茨海默病和相关痴呆症（ADRD）老年人的健康结果。这是一个人口 这可能特别容易受到保险范围的变化和限制的影响，因为患者可能有有限的 决策能力，并可能不成比例地暴露于被认为是实验性的治疗 以及缺乏有效性来清除“合理和必要”的门槛。该项目的目标1是估计 处方药和门诊程序的承保决定对治疗的平均影响 决策和健康结果的老年医疗保险登记与ADRD。目标2是预测和表征 ADRD患者的亚组最有可能受到限制范围的决定的影响， 使用机器学习方法的保险范围。建议的经验方法是使用准- 实验变异，由保险范围内突然变化的自然实验引起， 医疗保险计划的不同部分。该分析利用了处方集范围的变化 医疗保险D部分计划，以及当地医生服务覆盖决定的变化， 医疗保险B部分规定的门诊程序。这些变化的来源加上准， 治疗效果的实验估计和用于异质性分析的机器学习方法，允许 估计不同药物对ADRD老年人治疗决策和健康的反应， 程序和ADRD患者亚组。