Drug Coverage and Cost Offsets: Two-Part Models with Endogenous Treatment Effects

药物承保和成本抵消：具有内源性治疗效果的两部分模型

基本信息

批准号：
7675219
负责人：
JOSEPH VINCENT TERZA
金额：
$ 22.51万
依托单位：
UNIVERSITY OF NORTH CAROLINA GREENSBORO
依托单位国家：
美国
项目类别：
财政年份：
2008
资助国家：
美国
起止时间：
2008-08-15 至 2011-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7675219
关键词：
Drug Coverage Cost Offsets Two

项目摘要

DESCRIPTION (provided by the applicant): Prescription drug costs are the fastest rising component of national health expenditures over the last half decade and are projected to significantly outpace other service sectors for at least another decade to come (Hefler, et al. 2003). This trend would be viewed in a more favorable light from a policy perspective if it were known to have resulted in significant cost offsets in other healthcare spending categories. Moreover, such cost offsets could be taken as a marker for real improvements in population health. We propose to conduct an empirical investigation of the cost-offset effects of certain types of prescription coverage chosen by Medicare beneficiaries using data from the Medicare Current Beneficiary Survey (MCBS) for 1997 through 2004. We will estimate the cost-offset effects on hospital spending of four categories of insurance coverage for prescription drugs: employer-sponsored insurance; Medicaid prescription coverage; self-purchased Medigap policies; and other forms of public coverage. We believe that important insights about the potential cost-offset effects of some aspects of the Medicare Part D prescription benefit will be gained from our results, and will help in formulating expectations about the future viability of the program. Given the billions of dollars in projected Part D costs over the coming decade, finding cost-offsets would provide some hope that this expensive benefit would be sustainable in a Medicare program that is fast approaching insolvency. There are two methodological challenges to be confronted in the estimation of distinct cost-offset effects for the various drug coverage types. First, a non-trivial proportion of the person-years in our MCBS sample have no hospital admissions. Therefore, a two-part regression specification is called for. Secondly, the coverage-type variables are potentially endogenous; i.e. there exist unobservable variables that are both correlated with individual's coverage choice and exert an influence on his level of hospital expenditure. We find that extant econometric methods designed to simultaneously deal with both of these complicating features are either flawed or not suitable in the present context. Therefore, as part of the proposed project, we will develop, test, compare, and program (in Stata(r)) a new unbiased and robust estimator. The proposed research has the following specific aims: 1) to estimate and statistically test the cost-offset effects of the various types of drug coverage; 2) to develop a model, estimator, and corresponding STATA(r) software to take account of the potential endogeneity of multiple dummy variables in a conventional two-part modeling context; 3) to evaluate the accuracy (unbiasedness) and precision (statistical efficiency) of the most general version of our new estimator using simulated data.

描述（由申请人提供）：处方药成本是过去五年中国家卫生支出最快的成本，预计至少在未来十年的时间内将显着超过其他服务部门（Hefler等人，2003年）。如果众所周知，这种趋势从政策的角度看待，从政策的角度来看，这将导致其他医疗保健支出类别的大量成本偏移。此外，这种成本偏移可以作为人口健康的真正改善的标志。我们建议使用Medicare当前受益人调查（MCBS）（MCBS）的数据（MCBS）（MCBS）在1997年至2004年对某些类型的处方覆盖率的成本偏变影响进行实证研究。我们将估计成本偏移对处方药的医院支出的成本偏移影响：处方药的四类保险范围：雇主保险保险保险保险；医疗补助处方覆盖；自购买的Medigap政策；和其他形式的公共报道。我们认为，关于Medicare D Partripcrion Felast的某些方面的潜在成本偏变影响的重要见解将从我们的结果中获得，并有助于提出对该计划未来可行性的期望。鉴于未来十年内数十亿美元的预计D部分成本，寻找成本率将提供一些希望，在快速接近破产的Medicare计划中，这种昂贵的收益将是可持续的。在估计各种药物覆盖类型的不同成本偏变效应时，应面临两个方法论挑战。首先，在我们的MCBS样本中，人年的非平凡比例没有医院入院。因此，要求两部分回归规范。其次，覆盖型变量可能是内源性的。即，存在不可观察的变量，这两者都与个人的覆盖范围选择相关，并且对他的医院支出水平产生了影响。我们发现，旨在同时处理这两种复杂特征的现有计量经济学方法在当前情况下都是有缺陷或不合适的。因此，作为拟议项目的一部分，我们将开发，测试，比较和程序（在Stata（r）中）一个新的无偏见且可靠的估计器。拟议的研究具有以下具体目的：1）估计和统计测试各种药物覆盖的成本偏移效应； 2）开发一个模型，估计器和相应的Stata（R）软件，以考虑到常规的两部分建模上下文中多个虚拟变量的潜在内生性； 3）使用模拟数据评估我们新估计器最通用版本的准确性（无偏见）和精度（统计效率）。