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The Estimation Power of Alternative Comorbidity Indices

dc.contributor.authorBaser, Onuren_US
dc.contributor.authorPalmer, Liisaen_US
dc.contributor.authorStephenson, Judith J.en_US
dc.date.accessioned2010-06-01T18:24:07Z
dc.date.available2010-06-01T18:24:07Z
dc.date.issued2008-09en_US
dc.identifier.citationBaser, Onur; Palmer, Liisa; Stephenson, Judith (2008). "The Estimation Power of Alternative Comorbidity Indices." Value in Health 11(5): 946-955. <http://hdl.handle.net/2027.42/71610>en_US
dc.identifier.issn1098-3015en_US
dc.identifier.issn1524-4733en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/71610
dc.identifier.urihttp://www.ncbi.nlm.nih.gov/sites/entrez?cmd=retrieve&db=pubmed&list_uids=18489502&dopt=citationen_US
dc.description.abstractObjective:  Health-care expenditures are strongly influenced by overall illness burden. Appropriate risk adjustment is required for correct policy analysis. We compared three risk adjustment methods: the Charlson comorbidity index (CCI), the chronic disease score (CDS), and the Agency for Healthcare Research and Quality's comorbidity index (AHRQCI) in terms of their estimation power in analyzing health-care expenditures. Method:  Data from the Thomson MarketScan ® Research Databases (Thomson Healthcare, Ann Arbor, MI) were used to estimate total health-care expenditures of migraine patients treated by a triptan. Seven distinct multivariate models were evaluated for model fit (CCI only, CDS only, AHRQCI only, CCI + CDS, CCI + AHRQCI, CDS + AHRQCI, and CCI + CDS + AHRQCI). The estimation power of these indices (alone and in combination) was evaluated using Bayesian and Akaike information criteria, log-likelihood scores, and pseudo R 2 values. Results:  Confirming results from previous studies, when comorbidity indices were considered individually the results were inconclusive. Statistically the best performance was observed in the model that included all three of the comorbidity measures (CCI + CDS + AHRQCI); however, the practical differences in the estimated values were small. Conclusion:  Low correlation between these comorbidity indices shows that it is possible to have potential risk factors that are not captured in the single comorbidity index. Each comorbidity measure considers different risks, and the collinearity of the three measures is not strong enough to preclude using them simultaneously in the same model.en_US
dc.format.extent90877 bytes
dc.format.extent3109 bytes
dc.format.mimetypeapplication/pdf
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dc.publisherBlackwell Publishing Incen_US
dc.rights© 2008 International Society for Pharmacoeconomics and Outcomes Researchen_US
dc.subject.otherComorbidityen_US
dc.subject.otherHealth-care Costsen_US
dc.subject.otherRegression Analysisen_US
dc.subject.otherRisk Adjustmenten_US
dc.titleThe Estimation Power of Alternative Comorbidity Indicesen_US
dc.typeArticleen_US
dc.subject.hlbsecondlevelMedicine (General)en_US
dc.subject.hlbtoplevelHealth Sciencesen_US
dc.description.peerreviewedPeer Revieweden_US
dc.contributor.affiliationumSTATinMED Research and University of Michigan, Ann Arbor, MI, USA;en_US
dc.contributor.affiliationotherThomson Healthcare, Washington, DC, USA;en_US
dc.contributor.affiliationotherHealthCore, Inc. Wilmington, DE, USAen_US
dc.identifier.pmid18489502en_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/71610/1/j.1524-4733.2008.00343.x.pdf
dc.identifier.doi10.1111/j.1524-4733.2008.00343.xen_US
dc.identifier.sourceValue in Healthen_US
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