Core--Forecasts and Changes in Health and Illness Costs

这一核心将支持美国老年人口健康和功能变化的基于计算机的预测和模拟的产生，并估计由于使用不同假设而导致这些预测变化相关的成本差异。这些预测的经验基础最初将是1982年、1984年、1989年、1994年和1999年NLTCS中评估的功能状态和维度的变化以及死亡率。该模型的设计使得健康过程的参数可以成为不同类型的非健康投入的函数，这些非健康投入是由各种具体研究项目中的建模效应产生的。因此，将在项目2、3和4中生成对健康产生经济影响的模型。项目1将处理与在不同项目中开发并经经验校准的其他模型建立健康预测模型的数学接口所涉及的方法学问题。子项目中要处理的主要问题之一是研究投资如何与临床创新的采用率和有效性相关。关于这种关系的直接数据相对较少，也就是说，可以对特定类型的研究的费用进行估计，对通过该研究购买特定临床服务的费用进行估计，并对不同水平的医疗支出改变健康的程度进行估计。然而，几乎没有数据直接将研究投资与特定个人的健康变化联系起来。因此，我们建议在数学上一致的结构中使用可用的数据和链接，来自生物医学研究的专家和特定技术的临床应用的专家的输入。我们将考察健康结果对这类投资的敏感性，同时考虑到随机和结构性不确定性的明显程度。这一努力将有助于确定可能有助于解决某些不确定性的其他证据的类型和来源。

This core will support the production of computer based forecasts and simulations of changes in the health and functioning of the U.S. elderly population and estimate the cost differentials associated with changes in those forecasts due to the use of different assumptions. The empirical basis of these forecasts will initially be the changes in functional status and dimensions and mortality assessed in the 1982, 1984, 1989, 1994, and 1999 NLTCS. The model is designed so that the parameters of the health process can be made functions of different types of non-health inputs generated by modeling effects in various of the specific research projects. Thus, models of economic effects on health will be generated in Projects 2, 3 and 4. Project 1 will deal with the methodological issues involved in constructing the mathematical interfaces of the health forecasting model with the other models developed, and empirically calibrated, in the various projects. One of the major issues to be dealt with in the sub-projects is how research investment relates to the rate of adoption, and efficacy of, clinical innovations. There is relatively little direct data on this relation, That is, it is possible to derive estimates of the amount spent on specific types of research, to derive estimates of the amount spent on purchasing specific clinical services made possible by that research, and to derive estimates of how much health is changed by various levels of health spending. There is little data directly relating research investments to changes in health for specific individuals however. Thus, we propose to use the available data and link in a mathematically consistent structure with inputs from experts on biomedical research and from experts on the clinical application of specific technologies. We will examine the sensitivity of health outcomes to such investments taking account of the evident degree of stochastic and construct uncertainty. That effort will help in identifying types and sources of additional evidence that may help to resolve some of this uncertainty.