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Validating the Adapted Diabetes Complications Severity Index in Claims Data

Published Online: November 20, 2012
Hsien-Yen Chang, PhD; Jonathan P. Weiner, DrPH; Thomas M. Richards, MSEE; Sara N. Bleich, PhD; and Jodi B. Segal, MD, MPH
Objectives: To test the validity of the adapted Diabetes Complications Severity Index (aDCSI), which does not include laboratory test results, as an indicator of diabetes severity.


Study Design: Retrospective cohort study using 4 years of claims data from 7 health insurance plans.


Methods: Individuals with diabetes mellitus and continuous enrollment were study subjects (N = 138,615). The 2 independent variables—the aDCSI score (sum of 7 diabetes complications graded by severity as 0, 1, or 2; range 0-13) and the aDCSI diabetes complication count (sum of 7 diabetes complications without severity grading; range 0-7)—were generated using only claims data. We evaluated the numbers of hospitalizations attributable to the aDCSI with Poisson regression models, both categorically and linearly.


Results: The aDCSI score (risk ratio 1.39 to 6.10 categorically and 1.41 linearly) and diabetes complication count (risk ratio 1.67 to 9.11 categorically and 1.65 linearly) were both significantly positively associated with the number of hospitalizations over a 4-year period. Risk ratios from the aDCSI score were very similar to the risk ratios previously reported for the Diabetes Complications Severity Index (DCSI); the absolute difference between risk ratios ranged from 0.01 to 1.6 categorically and was 0.05 linearly.


Conclusions: The aDCSI is a good measure of diabetes severity, given its ability to explain hospitalizations and its similar performance to the DCSI.


(Am J Manag Care. 2012;18(11):721-726)
Diabetes mellitus is one of the major healthcare issues in the United States because of its high prevalence and the growing costs of caring for affected patients.1-3 Complications associated with diabetes drive the escalating costs of diabetes management. Although several risk measures were developed to quantify the severity of diabetes complications, they mostly targeted a specific condition instead of the broad array of diabetes complications.4-6 The Diabetes Complications Severity Index (DCSI), developed by Young and colleagues, uniquely incorporates a wide range of diabetes complications.7 The DCSI incorporates diagnosed complications along with select laboratory results to assess patients’ risks of adverse outcomes, including hospitalizations and death. It uses information from 7 diabetes complication categories.7 Even though the DCSI is a relatively new measure, it has been quickly adopted by researchers.8-12 However, DCSI’s utility as a risk measure to characterize a diabetic population may be limited because laboratory test results are not readily available to researchers, particularly those who rely on administrative claims.

Risk measures have increasingly relied on claims data because they are inexpensively available for a great number of individuals; these data include claims for diagnoses and procedures and often information on dispensed medications.13,14 One widely used risk adjustment measure, the Adjusted Clinical Group system, uses an individual’s diagnoses and pharmacy data from 1 year to assign a morbidity level.15 This approach has been validated both domestically16 and internationally.17,18 However, claims data usually do not include laboratory information, which makes it difficult for risk measures that require laboratory results to be applied on a large scale.

Therefore, our purpose was to test the validity of the adapted Diabetes Complications Severity Index (aDCSI), which excludes laboratory test results, as an indicator of diabetes severity. We hypothesized that the aDCSI would be comparable to the DCSI (which includes laboratory data) and be a good measure of diabetes severity.

RESEARCH DESIGN AND METHODS

Design


This was a retrospective cohort study using 4 years of claims data in which we tested the value of the aDCSI for explaining the number of hospitalizations.

Data

We accessed claims data from 7 Blue Cross Blue Shield plans; the detailed information was described in a published paper.19 The original data were collected from 2002 to 2005; the data were subsequently updated with additional data on the original individuals through 2006. The following data were acquired: (1) enrollment files for administrative data; (2) benefits information to determine medical and pharmacy coverage; and (3) inpatient, outpatient, and pharmacy claims records containing the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnosis, Current Procedural Terminology codes, and National Drug Code prescription information.

Defining the Analytic Cohort

We required that the enrollees have type 2 diabetes, and full medical and pharmacy coverage in the 4-year period.

We defined individuals as having type 2 diabetes if they had 1 relevant inpatient code or 2 outpatient ICD-9-CM codes separated by at least 30 days. The relevant codes were 250.xx, 648.0 (diabetes mellitus with pregnancy), and 362.0 (diabetic retinopathy) or 266.41 (diabetic cataract). Individuals only with 250.x3 (type 1 diabetes) were not included. Additionally, any individual filling a prescription for a medication for treatment of hyperglycemia was included (eAppendix A, available at www.ajmc.com). Combination medications were also identified. If the prescription was for metformin alone, the individual was also required to have an ICD-9-CM code for diabetes for inclusion in this group. The calendar year of the earliest diagnosis of diabetes was used as the starting point of the observation period.

Number of Hospitalizations and Costs

The number of hospitalizations was obtained from the inpatient claims over a 4-year period. Costs were obtained from the claims over a 4-year period; per person per year total costs and pharmacy costs were presented. Total costs were examined as well as pharmacy costs.20

DCSI Scores and DCSI Complication Counts

To replicate the DCSI scores and DCSI complication counts, we identified the claims coded with the ICD-9-CM system for individuals during the 4-year study period and applied the classification method developed by Young and colleagues (eAppendix B).7 The DCSI score consists of scores (0, 1, or 2) from 7 complication categories: retinopathy, nephropathy, neuropathy, cerebrovascular, cardiovascular, peripheral vascular disease, and metabolic; it ranges from 0 to 13. The DCSI complication count is a count of any complication in the 7 categories and ranges from 0 to 7. We did not include laboratory results in constructing these aDCSI scores and aDCSI complication counts.

Statistical Methods

For number of hospitalizations, we adopted a Poisson model with adjustment for overdispersion, to parallel what Young and colleagues did. Given that there were many people without any hospitalizations, a zero-inflated negative binomial model was also used for a sensitivity analysis. The patterns of results from both models were similar, so we just report the results from the Poisson model.

We calculated risk ratios for hospitalization. We tested inclusion of the main independent variables, aDCSI score and aDCSI complication count, categorically (0, 1, 2, 3, 4, 5+) and linearly. In categorical analysis, the risk ratio of hospitalizations was derived by comparing samples in a given category with those in category 0; in linear analysis, it was the risk ratio of hospitalizations associated with a 1-unit increase in aDCSI score.

Comparison of DCSI With and Without Laboratory Test Results

We compared the risk ratios of hospitalizations obtained in this study with those in the study by Young et al to determine whether the aDCSI (without laboratory information) and the DCSI (with laboratory information) perform similarly. Given the differences in study population and sample size, we decided that if the risk ratios from both sources were similar and showed similar patterns across risk groups, we would conclude that the aDCSI and DCSI performed similarly.

Review

The data were deidentified in accordance with the Health Insurance Portability and Accountability Act’s definition of a limited data set. The Johns Hopkins University Office of Research Subjects deemed the study to be exempt from federal regulations because the research activities were considered to be of minimal risk to subjects, as they were not identifiable.

RESULTS

Characteristics of the Study Samples


There were 138,615 study subjects (Table 1). The mean age was about 59 years and roughly 51% were male. About 70% of the study subjects had a score of 0 for DCSI score/complication count, and close to 60% had no hospitalization. The mean aDCSI score, aDCSI complication count, and number of hospitalizations were 0.50, 0.37, and 0.94, respectively. The average annual total cost was $11,500 over a 4-year period, of which $3000 (26%) was pharmacy cost.

With Laboratory Versus Without Laboratory

Without laboratory data, aDCSI score and aDCSI complication count were significantly positively associated with the number of hospitalizations over a 4-year period. Categorically, risk ratios ranged from 1.39 to 6.10 for aDCSI score and from 1.67 to 9.11 for aDCSI complication count, when comparing the non-zero categories (from 1 to 5+) with the category 0. Linearly, the risk ratio was 1.41 for each 1-unit increase in aDCSI score and 1.65 for each additional aDCSI complication count (Table 2).

Compared with the DCSI with laboratory data used by Young et al,7 risk ratios for hospitalization as determined by aDCSI score were very similar when the score was less than or equal to 3, and a little lower when the score was above 3 (Figure). The absolute difference in risk ratios between our results and those of Young et al increased as the score category increased, ranging from 0.01 to 1.6. Linearly, the risk ratio for aDCSI score was a little higher than that for DCSI score (1.41 vs 1.36). Similar patterns were observed using the DCSI complication count (Table 2).

DISCUSSION

We found that even without inclusion of laboratory test results, the aDCSI can be used to explain hospitalization. The similarities of the risk ratios determined by the DCSI with and without laboratory results suggest that the DCSI might be applied when laboratory data are not available.

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Issue: November 2012
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