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dc.contributor.authorRosen, Amy K.
dc.contributor.authorWu, Jeanne
dc.contributor.authorChang, Bei-Hung
dc.contributor.authorBerlowitz, Dan R.
dc.contributor.authorAsh, Arlene S.
dc.contributor.authorMoskowitz, Mark A.
dc.date2022-08-11T08:10:42.000
dc.date.accessioned2022-08-23T17:17:09Z
dc.date.available2022-08-23T17:17:09Z
dc.date.issued2000-06-08
dc.date.submitted2010-07-01
dc.identifier.citationMed Care. 2000 Jun;38(6):647-59. <a href="http://journals.lww.com/lww-medicalcare/Abstract/2000/06000/Does_Diagnostic_Information_Contribute_to.6.aspx">Link to article on publisher's site</a>
dc.identifier.issn0025-7079 (Linking)
dc.identifier.pmid10843312
dc.identifier.urihttp://hdl.handle.net/20.500.14038/47562
dc.description.abstractBACKGROUND: 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.
dc.language.isoen_US
dc.relation<a href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=pubmed&cmd=Retrieve&list_uids=10843312&dopt=Abstract">Link to Article in PubMed</a>
dc.relation.urlhttp://journals.lww.com/lww-medicalcare/Abstract/2000/06000/Does_Diagnostic_Information_Contribute_to.6.aspx
dc.subject*Activities of Daily Living
dc.subjectAnalysis of Variance
dc.subjectCalibration
dc.subjectCost-Benefit Analysis
dc.subjectDatabases, Factual
dc.subjectDiagnosis-Related Groups
dc.subjectDiscriminant Analysis
dc.subjectHumans
dc.subjectLikelihood Functions
dc.subject*Long-Term Care
dc.subjectOutcome Assessment (Health Care)
dc.subjectPredictive Value of Tests
dc.subjectRegression Analysis
dc.subjectReproducibility of Results
dc.subjectRetrospective Studies
dc.subjectRisk Adjustment
dc.subjectUnited States
dc.subjectUnited States Department of Veterans Affairs
dc.subjectBiostatistics
dc.subjectEpidemiology
dc.subjectHealth Services Research
dc.titleDoes diagnostic information contribute to predicting functional decline in long-term care
dc.typeJournal Article
dc.source.journaltitleMedical care
dc.source.volume38
dc.source.issue6
dc.identifier.legacycoverpagehttps://escholarship.umassmed.edu/qhs_pp/695
dc.identifier.contextkey1378841
html.description.abstract<p>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.</p> <p>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.</p> <p>RESEARCH DESIGN: We used a retrospective sample of 15,693 long-term care residents in VA facilities in 1996.</p> <p>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.</p> <p>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.</p> <p>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.</p>
dc.identifier.submissionpathqhs_pp/695
dc.contributor.departmentDepartment of Quantitative Health Sciences
dc.source.pages647-59


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