COMBINING INFORMATION FROM MULTIPLE DATA SOURCES TO ASSESS POPULATION HEALTH.
COMBINING INFORMATION FROM MULTIPLE DATA SOURCES TO ASSESS POPULATION HEALTH.
复制标题
DOI：
10.1093/jssam/smz047
复制
发表时间：
2020-06
影响因子：
2.1
通讯作者：
Cutler D
中科院分区：
文献类型：
--
作者：
Raghunathan T;Ghosh K;Rosen A;Imbriano P;Stewart S;Bondarenko I;Messer K;Berglund P;Shaffer J;Cutler D
有关明确的受试者样本的广泛健康状况的信息对于评估人口健康、衡量各种政策的影响、建模成本和研究健康差异至关重要。不幸的是，没有单一的数据源可以提供有关健康状况的准确信息。我们结合了多个管理和调查数据集的信息，获得了 1999 年至 2012 年 14 年期间医疗保险当前受益人调查 (MCBS) 中老年人（65 岁及以上）受试者 107 种健康状况（疾病、预防措施和疾病筛查）的基于模型的虚拟变量。 MCBS 根据医疗保险索赔评估疾病患病率，并提供所有健康状况的详细信息，但容易出现低估偏差。另一方面，国家健康和营养检查调查 (NHANES) 仅收集 107 种健康状况中的一部分的自我报告和身体/实验室测量结果。这两个来源都没有提供完整的信息，但我们将它们一起使用，使用缺失数据和测量误差模型框架，在 MCBS 中导出基于模型的校正虚拟变量，以适应各种现有的健康状况。我们创建多重估算虚拟变量，并使用它们来构建患病率和趋势估计。然而，更广泛的目标是使用这些校正或建模的虚拟变量进行多种政策分析、成本建模以及其他关系的分析，将它们用作预测变量或结果变量。
Information about an extensive set of health conditions on a well-defined sample of subjects is essential for assessing population health, gauging the impact of various policies, modeling costs, and studying health disparities. Unfortunately, there is no single data source that provides accurate information about health conditions. We combine information from several administrative and survey data sets to obtain model-based dummy variables for 107 health conditions (diseases, preventive measures, and screening for diseases) for elderly (age 65 and older) subjects in the Medicare Current Beneficiary Survey (MCBS) over the fourteen-year period, 1999–2012. The MCBS has prevalence of diseases assessed based on Medicare claims and provides detailed information on all health conditions but is prone to underestimation bias. The National Health and Nutrition Examination Survey (NHANES), on the other hand, collects self-reports and physical/laboratory measures only for a subset of the 107 health conditions. Neither source provides complete information, but we use them together to derive model-based corrected dummy variables in MCBS for the full range of existing health conditions using a missing data and measurement error model framework. We create multiply imputed dummy variables and use them to construct the prevalence rate and trend estimates. The broader goal, however, is to use these corrected or modeled dummy variables for a multitude of policy analysis, cost modeling, and analysis of other relationships either using them as predictors or as outcome variables.
登录
查看更多内容
影响因子：
2
作者：
Bondarenko, Irina;Raghunathan, Trivellore
通讯作者：
Raghunathan, Trivellore
DOI：
10.1198/016214506000001293
发表时间：
2007-06-01
影响因子：
3.7
作者：
Raghunathan, Trivellore E.;Xie, Dawei;Feuer, Eric J.
通讯作者：
Feuer, Eric J.
影响因子：
2
作者：
Schenker, Nathaniel;Raghunathan, Trivellore E.;Bondarenko, Irina
通讯作者：
Bondarenko, Irina
影响因子：
7.2
作者：
Okura, Y;Urban, LH;Rodeheffer, RJ
通讯作者：
Rodeheffer, RJ
影响因子：
9.7
作者：
Cutler, David M.;Ghosh, Kaushik;Rosen, Allison B.
通讯作者：
Rosen, Allison B.