Dataset Information

Evaluation of risk adjustment performance of diagnosis-based and medication-based comorbidity indices in patients with chronic obstructive pulmonary disease.

ABSTRACT:

Objectives

This study assessed risk adjustment performance of six comorbidity indices in two categories of comorbidity measures: diagnosis-based comorbidity indices and medication-based ones in patients with chronic obstructive pulmonary disease (COPD).

Methods

This was a population-based retrospective cohort study. Data used in this study were sourced from the Taiwan National Health Insurance Research Database. The study population comprised all patients who were hospitalized due to COPD for the first time in the target year of 2012. Each qualified patient was individually followed for one year starting from the index date to assess two outcomes of interest, medical expenditures within one year after discharge and in-hospital mortality of patients. To assess how well the added comorbidity measures would improve the fitted model, we calculated the log-likelihood ratio statistic G2. Subsequently, we compared risk adjustment performance of the comorbidity indices by using the Harrell c-statistic measure derived from multiple logistic regression models.

Results

Analytical results demonstrated that that comorbidity measures were significant predictors of medical expenditures and mortality of COPD patients. Specifically, in the category of diagnosis-based comorbidity indices the Elixhauser index was superior to other indices, while the RxRisk-V index was a stronger predictor in the framework of medication-based codes, for gauging both medical expenditures and in-hospital mortality by utilizing information from the index hospitalization only as well as the index and prior hospitalizations.

Conclusions

In conclusion, this work has ascertained that comorbidity indices are significant predictors of medical expenditures and mortality of COPD patients. Based on the study findings, we propose that when designing the payment schemes for patients with chronic diseases, the health authority should make adjustments in accordance with the burden of health care caused by comorbid conditions.

SUBMITTER: Guo Ie H

PROVIDER: S-EPMC9269939 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Purpose:Multiple studies have suggested that comorbidities pose negative impacts on the survival of patients with chronic obstructive pulmonary disease (COPD); few have applied comorbidity measures driven from health insurance claims databases to predict various health outcomes. We aimed to examine the performance of commonly used comorbidity measures based on diagnosis and pharmacy dispensing claims information in predicting future death and hospitalization in COPD patients. Methods:We identified COPD patients in a population-based Taiwanese database. We built logistic regression models with age, sex, and baseline comorbidities measured by either diagnosis or pharmacy claims information as predictors of subsequent-year death or hospitalization in a random 50% sample and validated the discrimination in the other 50%. The diagnosis-based comorbidity measures included the Charlson Index and the Elixhauser comorbidity measure; the pharmacy-based comorbidity measures included the updated Chronic Disease Score (CDS) and the Pharmacy-Based Comorbidity Index (PBDI). Results:We identified 428,251 eligible patients. For overall death, the Elixhauser comorbidity measure showed the best predictive performance (c-statistic=0.832), followed by the PBDI (c-statistic=0.822), the Charlson Index (c-statistic=0.815), and the updated CDS (c-statistic=0.808). For overall hospitalization, the PBDI (c-statistics=0.730) and the Elixhauser comorbidity measure (c-statistics=0.724) outperformed the updated CDS (c-statistics=0.714) and the Charlson Index (c-statistics=0.710). For hospitalization due to cardiovascular, cerebrovascular, or respiratory diseases, the comorbidity models showed similar predictive ranks and demonstrated c-statistics higher than 0.75. However, none of the models could adequately predict hospitalization due to other reasons (c-statistics < 0.60). Conclusion:Our study comprehensively compared the predictive performance of comorbidity measures. The Elixhauser comorbidity measure and the PBDI are useful tools for describing comorbid conditions and predicting health outcomes in COPD patients.