Combining Information from Multiple Data Sources to Assess Population Health. (20th March 2020)

• Record Type: Journal Article
• Authors: Raghunathan, Trivellore; Ghosh, Kaushik; Rosen, Allison; Imbriano, Paul; Stewart, Susan; Bondarenko, Irina; Messer, Kassandra; Berglund, Patricia; Shaffer, James; Cutler, David
• Is Part Of: Journal of Survey Statistics and Methodology. Volume 9:Number 3(2021)
• Page Start: 598
• Page End: 625
• Publication Date: 2020-03-20
• Subjects: Calibration -- Measurement error -- Multiple imputation -- Propensity scores; Surveys -- Methodology -- Periodicals; Surveys -- Evaluation -- Periodicals; Sampling (Statistics) -- Periodicals; 001.433
• DOI: 10.1093/jssam/smz047
• Languages: English
• ISSNs: 2325-0984

Abstract

Abstract: 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.