Comparison of different comorbidity measures for use with administrative data in predicting short- and long-term mortality

• Published in: BMC health services research Vol. 10; no. 1; p. 140
• Main Authors: Chu, Yu-Tseng, Ng, Yee-Yung, Wu, Shiao-Chi
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 27.05.2010; BioMed Central; BMC
• Subjects: Aged; Asia; Care and treatment; Chronic obstructive pulmonary disease; Cohort Studies; Comorbidity; Comparative analysis; Confidence intervals; Data entry; Databases, Factual; Datasets; Death Certificates; Female; Health insurance; Hospital Mortality - trends; Hospitalization; Hospitals; Humans; Insurance Claim Review; Logistic Models; Lung diseases, Obstructive; Male; Medical care; Medicine; Methods; Middle Aged; Mortality; Myocardial Infarction - mortality; National health insurance; Native peoples; Patients; Population; Predictive Value of Tests; Prognosis; Pulmonary Disease, Chronic Obstructive - mortality; Research article; Retrospective Studies; Statistics; Taiwan - epidemiology; Taiwan; Asia
• ISSN: 1472-6963; 1472-6963
• DOI: 10.1186/1472-6963-10-140

It is important to find a comorbidity measure with better performance for use with administrative data. The new method proposed by Elixhauser et al. has never been validated and compared to the widely used Charlson method in the Asia region. The objective of this study was to compare the performance of three comorbidity measures using information from different data periods in predicting short- and long-term mortality among patients with acute myocardial infarction (AMI) and chronic obstructive pulmonary disease (COPD). We conducted a retrospective cohort study using National Health Insurance claims data (2001-2002) in Taiwan. We constructed the Elixhauser, the Charlson/Deyo, and the Charlson/Romano methods based on the International Classification of Disease, 9th Revision, Clinical Modification codes in the claims data. Two data periods, including the index hospitalization as well as the index and prior 1-year hospitalizations, were used in the analysis. The performances were compared using the c-statistics derived from multiple logistic regression models that included age, gender, race, and whether the patient received surgery or not. The outcomes of interest were in-hospital and 1-year mortality. The performance was in the same rank order among both populations regardless of the outcome and data period: Elixhauser > Charlson/Romano > Charlson/Deyo. In predicting in-hospital mortality, the Elixhauser models using information from the index hospitalization performed best, even better than the Charlson/Deyo or Charlson/Romano models using information from the index and prior hospitalizations. Nevertheless, in predicting 1-year mortality, the Elixhauser models using information from the index and 1-year prior hospitalizations performed better than using information from the index hospitalization only. This is so far the first study to validate the Elixhauser method and compare it to other methods in the Asia region, and is the first to report its differences in data periods between short- and long-term outcomes. The comorbidity measurement developed by Elixhauser et al. has relatively good predictive validity, and researchers should consider its use in claims-based studies.