FORECASTING MODELS FOR ACUTE AND LONG TERM CARE

这是一个香农奖提供部分支持的研究 项目不属于指定机构的资助范围，但 在优秀的边缘。香农奖旨在提供 支持测试方法的可行性;支持进一步测试 和完善研究技术;进行二次分析或提供 数据集;或进行离散项目，以证明PI 研究能力或为已经优秀的 应用程序.以下摘要摘自原始文件 由主要经销商提交。 描述：此应用程序旨在创建年度预测 美国老年人按年龄、性别、慢性 认知障碍的强度、持续时间和使用水平 急性和长期护理服务。转换率将基于 各种国家调查，包括国家长期护理 调查（NLTCS）（1982年，84年，89年和94年），医疗保险现行受益人 调查（1991年及以后）和全国老龄化纵向研究。 关于非老年人口的数据将来自收入调查， 方案参与（1984年和1990年关于残疾问题的补充文件）， 其他来源投影的起始状态将基于 1990年人口普查的样本文件，其中还将 为验证工作提供一些目标。 将使用六种类型的模型或预测技术， 结构复杂性的增加。这些在 第41至43页。它们是：1）横截面患病率的应用 美国人口普查的现有官方人口预测 局和社会保障局。2)队列成分马尔可夫 具有不变转移率的链式投影 残疾国家的数量。3)队列分量马尔可夫预测 参数化乘性泊松子模型，经验平滑 贝叶斯混合4)隶属度模型的一个扩展 模型3涉及一种复杂的联营形式， 过渡矩阵的重新混合（显然，随着时间的推移仍然是恒定的）。 5)GoM评分的双组分随机过程投影模型 个体6)一个轨迹模型，用一个 年龄和性别特异性矩阵乘以时间不变的个体， 具体的弹道模型。

THIS IS A SHANNON AWARD PROVIDING PARTIAL SUPPORT FOR THE RESEARCH PROJECTS THAT FALL SHORT OF THE ASSIGNED INSTITUTE'S FUNDING RANGE BUT ARE IN THE MARGIN OF EXCELLENCE. THE SHANNON AWARD IS INTENDED TO PROVIDE SUPPORT TO TEST THE FEASIBILITY OF THE APPROACH; DEVELOP FURTHER TESTS AND REFINE RESEARCH TECHNIQUES; PERFORM SECONDARY ANALYSIS OR AVAILABLE DATA SETS; OR CONDUCT DISCRETE PROJECTS THAT CAN DEMONSTRATE THE PI'S RESEARCH CAPABILITIES OR LEND ADDITIONAL WEIGHT TO AN ALREADY MERITORIOUS APPLICATION. THE ABSTRACT BELOW IS TAKEN FROM THE ORIGINAL DOCUMENT SUBMITTED BY THE PRINCIPAL INVESTIGATOR. DESCRIPTION: This application aims at the creation of yearly projections of elderly persons in the United States by age, sex, chronic physical and cognitive disability intensities and durations and levels of use of acute and long-term care services. Transition rates will be based on a variety of national surveys, including the National Long-Term Care Survey (NLTCS) (1982,84,89, and 94), the Medicare Current Beneficiary Survey (1991 and later) and the National Longitudinal Study of Aging. Data on non-elderly populations will come from the Survey of Income and Program Participation (1984 and 1990 supplements on disability) and other sources. The starting state for the projections will be based on Public Use Microdata Sample files from the 1990 Census, which will also supply some targets for validation exercises. Six types of models or forecasting techniques will be used for projections of increasing structural complexity. These are described on pages 41 to 43. They are: 1) Application of cross-sectional prevalence rates to existing official population projections for the U.S. Census Bureau and Social Security Administration. 2) Cohort component Markov chain projections with unchanging transition rates defined for a small number of disability states. 3) Cohort component Markov projections with parameterized multiplicative Poisson submodels, smoothed by empirical Bayes mixing. 4) A Grade of Membership Model extension of the projections under Model 3, which involves a complex form of pooling and remixing of transition matrices (still, apparently, constant over time). 5) Two-component stochastic process projection models for GoM scores for individuals. 6) A trajectory model that replaces the GoM score with an age and sex specific matrix multiplying a time-invariant individual- specific trajectory model.