To our knowledge, this is the first study that validates the Elixhauser method and compares it to other methods in the Asia region. Further, it is the only investigation that examines the modeling performance of both in-hospital and 1-year mortality of the Elixhauser method, and is the first to report its differences in data periods between short- and long-term outcomes.
Several implications for risk adjustment can be drawn from this study. First, this study showed further evidence of external validity of the Elixhauser method in a different dataset and population. In every comparison, it was superior to the Deyo et al. version of the Charlson comorbidity index, which has been used widely for outcome and epidemiology studies. It also outperformed the Romano et al. adaptation of the Charlson index, which has been reported to be better than the Charlson/Deyo method in several studies [13
]. These findings add to the literature by providing new evidence on the comparison of these three methods together.
Second, our findings expand upon the results of two earlier studies which used the same analytical method in creating comorbidity variables[6
], and demonstrate the same results not only for short-term mortality but also for long-term mortality. Stukenborg et al. [7
] concluded that the Elixhauser method had better statistical performance than the Charlson/Deyo method in predicting in-hospital mortality by creating comorbidity variables as individual categories (the presence or absence of the comorbidity) using California claims data [7
]. The same approach was implemented in Canadian administrative data for predicting MI inpatient's mortality, and reported similar findings [6
]. Our study suggests that similar results can also be found in predicting 1-year mortality of patients with AMI and COPD. However, the Charlson method may still be a useful tool in many studies because it provides weighted scores, which is valuable if there are insufficient cases to use independent categories for modeling.
Third, new findings from different data periods provide additional insight into the comorbidity measure. Since some diagnoses are included only when they appear in the prior admissions when using the Charlson/Deyo method and the Charlson/Romano method [13
], these two methods have worse performance using the index hospitalization only. Some disease categories, such as congestive heart failure, never present if there is no prior information [12
]. This is more important for long-term mortality since the Charlson/Deyo method using the index and prior hospitalizations performed better than using only the index hospitalization in 100% of the bootstrap replications. However, the Elixhauser method develops DRG screen, so it can include more secondary diagnoses, and also distinguishes comorbidities from complications [18
]. Our findings agree with previous reports [7
] that the Elixhauser method using the index hospitalization performed better than the Charlson/Deyo method using the index and prior hospitalizations when predicting in-hospital mortality. Furthermore, similar results were found when comparing to the Charlson/Romano method in our study. New findings showed that the Elixhauser method has different patterns between different outcomes. Prior information may be more important when predicting 1-year mortality. This may be because only the influential or important diagnoses for the index hospitalization are likely to be recorded, but some conditions that are not directly related to the index hospitalization may be important to the long-term survival.
One limitation of this study is that the administrative data are claimed for reimbursement purposes rather than research purposes and thus varied in data quality [31
]. The quality of administrative data depends on the gaps in clinical information, coding procedures, and the billing context [32
]. Another limitation is that only two populations were investigated. However, we examined one acute condition and one chronic condition with diverse in-hospital mortality, and found similar results.
Since comparative performance of different comorbidity measures can only be examined when other factors, such as population, outcome, and data periods, are all the same. So does the analysis of data periods. The present study examined three types of comparisons by using a manageable design which focused on three measures of comorbidities, two data periods, two diagnoses, and two outcomes. Moreover, such design could become strength for its simplicity to be applied to other populations in Asia or other areas of the world. Similar studies of comparative performance are needed and can be applied to different populations, datasets, outcomes, data periods, and other case-mix methods.