Elsevier

Journal of Diabetes and its Complications

Volume 28, Issue 5, September–October 2014, Pages 612-616
Journal of Diabetes and its Complications

Risk of hospitalization and healthcare cost associated with Diabetes Complication Severity Index in Taiwan's National Health Insurance Research Database

https://doi.org/10.1016/j.jdiacomp.2014.05.011Get rights and content

Abstract

Objective

The aim of this study is to test the validity of adapted Diabetes Complication Severity Index (aDCSI) in predicting the risk of hospitalization and healthcare cost in type 2 diabetic patients using a nationally-representative claims database.

Study design

Retrospective cohort study used 4 years of claims data from Taiwan's National Health Insurance Research Database (NHIRD).

Methods

Type 2 diabetic patients who had 4-years of enrollment were identified as study subjects (N = 136,372). The aDCSI score (sum of diabetic complication with severity levels, range 0–13) and complication count (sum of diabetic complications, range 0–7) were generated using diagnostic codes for each patient. Poisson model and linear regression model were conducted to predict risk of hospitalization and healthcare costs associated with aDCSI score and count of diabetic complications.

Results

The aDCSI score (risk ratio 1.51 to 10.32 categorically, and 1.41 linearly) and count of diabetic complications (risk ratio 1.56 to 12.20 categorically, and 1.66 linearly) were significantly positively associated with risk of hospitalization. A one-point increase in the aDCSI score was positively associated with increased healthcare costs.

Conclusions

The performance of aDCSI in predicting risk of hospitalization and healthcare cost in the nationally-representative claims database is similar to those reported in the original study. It may serve as an efficient tool for stratifying type 2 diabetic patients for disease management programs and population-based studies.

Introduction

Diabetes mellitus is a complex chronic illness that is associated with multiple microvascular and macrovascular complications. These complications result in enormous morbidity, disability and mortality, and even poor quality of life (Chang, 2010). In addition, diabetic complications could be very costly. In the United States, diabetic complications have been reported to be responsible for 38% of total medical cost for this disease (American Diabetes Association, 2013).

Therefore, several studies were conducted to quantify patients of diagnosed diabetes based on the severity of complications for management of healthcare costs associated with this disease (Clarke et al., 2004, Rosenzweig et al., 2002, Selby et al., 2001). The Diabetes Complication Severity Index (DCSI), developed by Young and colleagues, is one of them. The DCSI is a 13-point scale scored from automated diagnostic, pharmacy and laboratory data and summarized diabetes complications into a single-value (Young, Lin, Korff, et al., 2008). The performance of DCSI in predicting mortality and risk of hospitalization in diabetic patients has been reported to be well accepted. In light of the increased use of claim-based database and unavailable laboratory data typically in most claim-based database, an adapted DCSI excluding laboratory data was suggested by Chang et al. The adapted DCSI has been found to show similar performance as the original DCSI and to be a useful tool for the prediction of risk of hospitalization and healthcare cost (Chang et al., 2012a, Chang et al., 2012b). However, the adapted DCSI has not been validated on external claims databases, which may limit the generalizability of the index.

Therefore, the aim of this study was to validate the adapted DCSI and to estimate healthcare costs associated with adapted DCSI using a nationally representative claims database in Taiwan.

Section snippets

Data source

The National Health Insurance Research database (NHIRD) is a nationwide database composed of anonymous eligibility and enrollment information, as well as claims for visits, procedures, and prescription medications of more than 99% of the entire population (approximately 23 million residents) in Taiwan (Hsiao, Yang, Huang, & Huang, 2007). The longitudinal nature of NHIRD permits one to identify a cohort based on diagnoses, health services and drug utilization, to track medical history, to

Results

Between January 1, 2000 and December 31, 2011, a total of 136,372 type 2 diabetic patients were identified from 3 subsets of LHIDs (Fig. 1). The mean age of the study cohort was 55.26 years (± 13.8 years). Approximately half of them (49.9%) were male. Greater than 60% of patients were aged between 45 and 65 years. Of them, 45,625 patients (33.46 %) had no complication, with a score of 0 for aDCSI score and complication count. Nearly 50% of study subjects had aDCSI score of 2 or more. The mean aDCSI

Discussion

To our knowledge, our study is the first study to validate the aDCSI and to estimate healthcare costs associated with aDCSI using an external claims database. The generalizability of aDCSI had been expanded to a nationally-representative Asian diabetic population in our study. This may lead to a better understanding of diabetic specific stratification system targeting high-risk patients to ameliorate utilization of medical service.

Our data source was from a nationally-representative claims

Conclusions

The performance of aDSCI in predicting risk of hospitalization and healthcare cost has been found to be well accepted in a nationally-representative claims database among Asian diabetic population. It may serve as an efficient tool for stratifying type 2 diabetic patients for disease management programs and population-based studies.

Acknowledgments

We thank the National Health Insurance Administration (NHIA) and National Health Research Institutes (NHRI) for making available the databases for this study. The content of this article, however, in no way represents any official position of the BNHI or NHRI. The authors bear all responsibilities for the results and the interpretation of the results.

References (17)

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Conflict of interests: None.

Financial support: F.Y. Hsiao received a part-time research assistantship from a research grant sponsored by National Science Council, Taiwan (NSC102-2410-H-002-058-MY2).

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