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dc.contributor.authorLix, Lisa M
dc.contributor.authorQuail, Jacqueline
dc.contributor.authorFadahunsi, Opeyemi
dc.contributor.authorTeare, Gary F
dc.date.accessioned2013-10-01T19:30:15Z
dc.date.available2013-10-01T19:30:15Z
dc.date.issued2013-09-02
dc.identifier.citationBMC Health Services Research. 2013 Sep 02;13(1):340
dc.identifier.urihttp://hdl.handle.net/1993/22217
dc.description.abstractAbstract Background The performance of comorbidity measures for predicting mortality in chronic disease populations and using ICD-9 diagnosis codes in administrative health data has been investigated in several studies, but less is known about predictive performance with ICD-10 data and for other health outcomes. This study investigated predictive performance of five comorbidity measures for population-based diabetes cohorts in administrative data. The objectives were to evaluate performance for: (a) disease-specific and general health outcomes, (b) data based on the ICD-9 and ICD-10 diagnoses, and (c) different age groups. Methods Performance was investigated for heart attack, stroke, amputation, renal disease, hospitalization, and death in all-age and age-specific cohorts. Hospital records, physician billing claims, and prescription drug records from one Canadian province were used to identify diabetes cohorts and measure comorbidity. The data were analysed using multiple logistic regression models and summarized using measures of discrimination, accuracy, and fit. Results In Cohort 1 (n = 29,058), for which only ICD-9 diagnoses were recorded in administrative data, the Elixhauser index showed good or excellent prediction for amputation, renal disease, and death and performed better than the Charlson index. Number of diagnoses was a good predictor of hospitalization. Similar results were obtained for Cohort 2 (n = 41,925), in which both ICD-9 and ICD-10 diagnoses were recorded in administrative data, although predictive performance was sometimes higher. For age-specific models of mortality, the Elixhauser index resulted in the largest improvement in predictive performance in all but the youngest age group. Conclusions Cohort age and the health outcome under investigation, but not the diagnosis coding system, may influence the predictive performance of comorbidity measure for studies about diabetes populations using administrative health data.
dc.rightsinfo:eu-repo/semantics/openAccess
dc.titlePredictive performance of comorbidity measures in administrative databases for diabetes cohorts
dc.typeJournal Article
dc.typeinfo:eu-repo/semantics/article
dc.language.rfc3066en
dc.description.versionPeer Reviewed
dc.rights.holderLisa M Lix et al.; licensee BioMed Central Ltd.
dc.date.updated2013-10-01T19:30:15Z
dc.identifier.doihttp://dx.doi.org/10.1186/1472-6963-13-340


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