CANCER SURVEILLANCE USING HEALTH CLAIMS BASED DATA

广泛目标：开发、评估和测试 医疗保险和退伍军人事务部（VA）基于索赔 建立高质量的国家癌症监测系统 （NCSS）美国老年人。 具体目标：1）发展-联合收割机的行政数据 医疗保险和退伍军人管理局对12种重要癌症的治疗; 2）评估-- 比较关于发病率和阶段的数据质量， 在NCSS中诊断为癌症，信息相同 1993年SEER计划报告; 3）利用率--（a） 开发和分析12种癌症的全国发病率，以及 作为州特定比率，（B）描述三种治疗模式 癌症部位，以及（3）一种癌症的模型治疗结果 绝佳的价钱 相关性：目前没有统一的国家制度， 报告癌症发病率和诊断时的癌症分期。 等 这些信息对规划和 评估癌症预防和控制干预措施，以及 描述和评估治疗。 研究设计：1989年医疗保险和VA管理数据 到目前为止，将被合并，以创建事件率， 确定老年医疗保险受益人的癌症阶段。 结果表明，该方法的敏感性、特异性和阳性预测值 将使用链接的SEER-Medicare文件确定NCSS数据。 三种癌症的治疗模式将使用 多项式概率单位方程，并开发了一个模型来描述 使用OLS回归和/或 考克斯比例风险技术。

Broad Objectives: to develop, evaluate, and test the utility of Medicare and Department of Veterans Affairs (VA) claims-based data to create a high quality National Cancer Surveillance System (NCSS) for the elderly U.S. population. Specific Aims: 1) Development--combine administrative data from Medicare and the VA for 12 important cancers; 2) Evaluation-- compare the quality of the data regarding incidence and stage of cancer at diagnosis in the NCSS with the same information reported by the SEER program in 1993; and 3) Utilization--(a) develop and analyze national incident rates for 12 cancers, as well as state specific rates, (b) describe treatment patterns for three cancer sites, and (3) model treatment outcomes for one cancer site. Relevance: Currently there is no uniform national system for reporting cancer incidence and stage of cancer at diagnosis. Such information would be extremely valuable for the planning and evaluation of cancer prevention and control interventions, as well as describing and evaluating treatment. Research Design: Medicare and Va administration data from 1989 to the present will be combined to create incident rates and determine the stage of cancer for elderly Medicare beneficiaries. The sensitivity, specificity and positive predictive values of the NCSS data will be determined using linked SEER-Medicare files. Treatment patterns for three cancers will be described using multinominal probit equations, and a model developed to describe treatment outcomes for one of them using OLS regression and/or Cox proportional hazards techniques.