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Open Access Research article

Predictive performance of comorbidity measures in administrative databases for diabetes cohorts

Lisa M Lix1234*, Jacqueline Quail3, Opeyemi Fadahunsi5 and Gary F Teare34

Author affiliations

1 Department of Community Health Sciences, University of Manitoba, Winnipeg, MB, Canada

2 School of Public Health, University of Saskatchewan, Saskatoon, SK, Canada

3 Saskatchewan Health Quality Council, Saskatoon, SK, Canada

4 Department of Community Health and Epidemiology, University of Saskatchewan, Saskatoon, SK, Canada

5 Department of Medicine, Reading Hospital, West Reading, PA, USA

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Citation and License

BMC Health Services Research 2013, 13:340  doi:10.1186/1472-6963-13-340

Published: 2 September 2013

Abstract

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.

Keywords:
Administrative health data; Comorbid conditions; Diabetes; Risk adjustment; Statistical models