Comparison of Rx-defined morbidity groups and diagnosis- based risk adjusters for predicting healthcare costs in Taiwan

Abstract

BackgroundMedication claims are commonly used to calculate the risk adjustment for measuring healthcare cost. The Rx-defined Morbidity Groups (Rx-MG) which combine the use of medication to indicate morbidity have been incorporated into the Adjusted Clinical Groups (ACG) Case Mix System, developed by the Johns Hopkins University. This study aims to verify that the Rx-MG can be used for adjusting risk and for explaining the variations in the healthcare cost in Taiwan.MethodsThe Longitudinal Health Insurance Database 2005 (LHID2005) was used in this study. The year 2006 was chosen as the baseline to predict healthcare cost (medication and total cost) in 2007. The final sample size amounted to 793 239 (81%) enrolees, and excluded any cases with discontinued enrolment. Two different kinds of models were built to predict cost: the concurrent model and the prospective model. The predictors used in the predictive models included age, gender, Aggregated Diagnosis Groups (ADG, diagnosis- defined morbidity groups), and Rx-defined Morbidity Groups. Multivariate OLS regression was used in the cost prediction modelling.ResultsThe concurrent model adjusted for Rx-defined Morbidity Groups for total cost, and controlled for age and gender had a better predictive R-square = 0.618, compared to the model adjusted for ADGs (R2 = 0.411). The model combined with Rx-MGs and ADGs performed the best for concurrently predicting total cost (R2 = 0.650). For prospectively predicting total cost, the model combined Rx-MGs and ADGs (R2 = 0.382) performed better than the models adjusted by Rx-MGs (R2 = 0.360) or ADGs (R2 = 0.252) only. Similarly, the concurrent model adjusted for Rx-MGs predicting pharmacy cost had a better performance (R-square = 0.615), than the model adjusted for ADGs (R2 = 0.431). The model combined with Rx-MGs and ADGs performed the best in concurrently as well as prospectively predicting pharmacy cost (R2 = 0.638 and 0.505, respectively). The prospective models showed a remarkable improvement when adjusted by prior cost.ConclusionsThe medication-based Rx-Defined Morbidity Groups was useful in predicting pharmacy cost as well as total cost in Taiwan. Combining the information on medication and diagnosis as adjusters could arguably be the best method for explaining variations in healthcare cost.

The influence of socio-economic status and multimorbidity patterns on healthcare costs: a six-year follow-up under a universal healthcare system

“…The ACG system was developed to predict healthcare utilization and costs based on groupings of diagnoses [3,16-18] and medications [19]. Although diagnosis-based models were widely adopted, literature shows that prescription data are superior for the prediction of pharmaceutical-related costs [16,20] and total costs [21-23].…”

“…An international mapping algorithm within the ACG system is used for Rx-MG assignment based on the WHO Anatomical Therapeutic Chemical (ATC) classification to classify medication-related data collected outside the U.S. [28]. Previous studies verified the effectiveness of medication-based Rx-MGs in predicting pharmaceutical costs and total medical costs in Taiwan [19]. Nine of the sixty-four Rx-MGs are used for minor, acute symptoms or diseases (e.g.…”

Predictability of pharmaceutical spending in primary health services using Clinical Risk Groups

“…Others are based on diagnoses: Clinical Risk groups (CRGs) [6,7], Adjusted Clinical Groups (ACGs) [8] and Diagnostic Cost Groups, (DCG). Finally there are the mixed models that combine both -Diagnostic Cost Groups, (DCG/Rx) [9] and Adjusted Clinical Groups (ACG/Rx) [10].The usefulness of these models is especially relevant for integrated health systems such as National Health Systems. These tools described above also allow stratification of the population according to morbidity, which permits patients with the greatest risk to benefit from case management programmes.…”

Projection of future pharmacy service fees using the dispensing claims in hospital and clinic outpatient pharmacies: national health insurance database between 2006 and 2012

“…Whereas previous studies developed a projection model of pharmacy service fees based on the rate of change for each variable by using claims data such as diagnoses and medications and validated projection models of pharmacy service fee by comparing R 2 values of the models [3, 11, 30], the projection model developed in this study is effective for validating the differences in projected and observed pharmacy service fees. Pharmacy service fees in Korea include fees for service provided by the pharmacy and pharmacist but not drug margin.…”