Public Insurance Design and Health at Older Ages

公共保险设计与老年人健康

• 批准号: 10605332
• 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 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10605332
• 关键词: Address; Admission activity; Affect; Age; Alzheimer' s disease patient; 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; Formularies; Fracture; Frequencies; Future; Geography; Geriatrics; Gerontology; Goals; Health; Health Benefit; Health Insurance; Heart; Heterogeneity; Hospitalization; Individual; Inequality; Insurance; Insurance Coverage; Knowledge; Learning Skill; Length of Stay; 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; clinical practice; comorbidity; decision-making capacity; design; experience; high dimensionality; human old age (65+); innovation; insurance claims; machine learning method; mortality; older patient; patient population; patient subsets; pharmacologic; 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