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Does diagnostic information contribute to predicting functional decline in long-term care
Quantitative Health Sciences Publications and Presentations
  • Amy K. Rosen, Boston University
  • Jeanne Wu
  • Bei-Hung Chang
  • Dan R. Berlowitz, Bedford Veterans Affairs Medical Center
  • Arlene S. Ash, University of Massachusetts Medical School
  • Mark A. Moskowitz, Boston University
UMMS Affiliation
Department of Quantitative Health Sciences
Publication Date
Document Type
*Activities of Daily Living; Analysis of Variance; Calibration; Cost-Benefit Analysis; Databases, Factual; Diagnosis-Related Groups; Discriminant Analysis; Humans; Likelihood Functions; *Long-Term Care; Outcome Assessment (Health Care); Predictive Value of Tests; Regression Analysis; Reproducibility of Results; Retrospective Studies; Risk Adjustment; United States; United States Department of Veterans Affairs
BACKGROUND: Compared with the acute-care setting, use of risk-adjusted outcomes in long-term care is relatively new. With the recent development of administrative databases in long-term care, such uses are likely to increase. OBJECTIVES: The objective of this study was to determine the contribution of ICD-9-CM diagnosis codes from administrative data in predicting functional decline in long-term care. RESEARCH DESIGN: We used a retrospective sample of 15,693 long-term care residents in VA facilities in 1996. METHODS: We defined functional decline as an increase of > or =2 in the activities of daily living (ADL) summary score from baseline to semiannual assessment. A base regression model was compared to a full model enhanced with ICD-9-CM codes. We calculated validated measures of model performance in an independent cohort. RESULTS: The full model fit the data significantly better than the base model as indicated by the likelihood ratio test (chi2 = 179, df = 11, P <0.001). The full model predicted decline more accurately than the base model (R2 = 0.06 and 0.05, respectively) and discriminated better (c statistics were 0.70 and 0.68). Observed and predicted risks of decline were similar within deciles between the 2 models, suggesting good calibration. Validated R2 statistics were 0.05 and 0.04 for the full and base models; validated c statistics were 0.68 and 0.66. CONCLUSIONS: Adding specific diagnostic variables to administrative data modestly improves the prediction of functional decline in long-term care residents. Diagnostic information from administrative databases may present a cost-effective alternative to chart abstraction in providing the data necessary for accurate risk adjustment.
Med Care. 2000 Jun;38(6):647-59. Link to article on publisher's site
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Citation Information
Amy K. Rosen, Jeanne Wu, Bei-Hung Chang, Dan R. Berlowitz, et al.. "Does diagnostic information contribute to predicting functional decline in long-term care" Vol. 38 Iss. 6 (2000) ISSN: 0025-7079 (Linking)
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