Abstract W MP108: Accuracy of Diagnoses in Contemporary Medicare Data: REGARDS Study Linked With Medicare Claims
Background: Administrative data such as Medicare data are commonly used for health services research and comparative effectiveness studies. These databases are readily available and a good source of long-term data on healthcare utilization and health outcomes in real-world settings. Outcomes are captured using diagnostic codes, but the evidence on the validity of these codes is scarce in contemporary Medicare data.
Methods: We linked the Reasons for Geographic and Racial Differences in Stroke (REGARDS) study data using SSN, date of birth and sex to 2003-2009 Medicare claims. In addition to the events captured by REGARDS phone interviews, medical charts were pursued for Medicare-identified strokes not previously identified in REGRADS. Events were adjudicated by stroke specialists using the retrieved medical charts. Using all the adjudicated strokes as gold standard, we calculated the sensitivity, specificity, PPV and NPV of inpatient stroke algorithms with ICD-9-CM codes [430, 431, 433.x1, 434.x1, 436] in the primary discharge diagnosis.
Results: We successfully linked 15,089 REGARDS participants with mean age of 69.3, 52% female and 37% Black. We adjudicated 457 strokes, of which 48 were identified by claims only and not by interviews. The tested algorithms had high specificity (99.8-100%), PPV (88.6-90.5%) and NPV (98.9-99.9%), but low sensitivity (58.6-67.4%) (Table).
Conclusions: High specificity and PPV of our algorithms to identify strokes in contemporary Medicare populations support their use in etiological and comparative effectiveness studies. While our inpatient Medicare algorithms identified 12% extra cases of strokes, their usefulness for estimating stroke incidence or related healthcare utilizations is limited by their low sensitivity. Linking cohorts to Medicare data is feasible and should be considered to increase the completeness of follow-up. Further studies are needed to evaluate more sensitive Medicare algorithms.
Author Disclosures: H. Kumamaru: None. S.E. Judd: None. J.R. Curtis: None. R. Ramachandran: None. N. Hardy: None. J. Rhodes: None. M.M. Safford: None. B. Kissela: None. G. Howard: None. J.J. Jalbert: None. T.G. Brott: None. S. Setoguchi: Research Grant; Significant; Johnson &Johnson.
- © 2014 by American Heart Association, Inc.