Center for Population Health and Aging, Duke University, Durham, NC 27708, USA.
Comput Math Methods Med. 2011;2011:857892. doi: 10.1155/2011/857892. Epub 2011 Jun 1.
Time trajectories of medical costs-associated with onset of twelve aging-related cancer and chronic noncancer diseases were analyzed using the National Long-Term Care Survey data linked to Medicare Service Use files. A special procedure for selecting individuals with onset of each disease was developed and used for identification of the date at disease onset. Medical cost trajectories were found to be represented by a parametric model with four easily interpretable parameters reflecting: (i) prediagnosis cost (associated with initial comorbidity), (ii) cost of the disease onset, (iii) population recovery representing reduction of the medical expenses associated with a disease since diagnosis was made, and (iv) acquired comorbidity representing the difference between post- and pre diagnosis medical cost levels. These parameters were evaluated for the entire US population as well as for the subpopulation conditional on age, disability and comorbidity states, and survival (2.5 years after the date of onset). The developed approach results in a family of new forecasting models with covariates.
利用国家长期护理调查数据与医疗保险服务使用文件相链接，分析了与 12 种与衰老相关的癌症和慢性非癌症疾病发病相关的医疗成本的时间轨迹。开发了一种用于选择每种疾病发病个体的特殊程序，并用于确定疾病发病日期。医疗成本轨迹由一个具有四个易于解释的参数的参数模型表示，这些参数反映了：（i）诊断前成本（与初始合并症相关），（ii）疾病发病成本，（iii）人群恢复，代表自诊断以来与疾病相关的医疗费用减少，以及（iv）获得性合并症，代表发病后和发病前医疗费用水平之间的差异。这些参数是针对整个美国人口以及根据年龄、残疾和合并症状态以及生存情况（发病后 2.5 年）进行评估的。所开发的方法产生了具有协变量的新预测模型系列。
