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The American Journal of Managed Care January 2015
Disease-Modifying Therapy and Hospitalization Risk in Heart Failure Patients
Fadia T. Shaya, PhD, MPH; Ian M. Breunig, PhD; and Mandeep R. Mehra, MD, FACC, FACP, FRCP
Frequency and Costs of Hospital Transfers for Ambulatory Care-Sensitive Conditions
R. Neal Axon, MD, MSCR; Mulugeta Gebregziabher, PhD; Janet Craig, PhD, RN; Jingwen Zhang, MS; Patrick Mauldin, PhD; and William P. Moran, MD, MS
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A. Mark Fendrick, MD, and Michael E. Chernew, PhD Co-Editors-in-Chief, The American Journal of Managed Care
Value-Based Insurance Design: Benefits Beyond Cost and Utilization
Teresa B. Gibson, PhD; J. Ross Maclean, MD; Michael E. Chernew, PhD; A. Mark Fendrick, MD; and Colin Baigel, MBChB
Changing Physician Behavior: What Works?
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Raina M. Merchant, MD, MSHP; Kristen Finne, BA; Barbara Lardy, MPH; German Veselovskiy, MPP; Casey Korba, MS; Gregg S. Margolis, NREMT-P, PhD; and Nicole Lurie, MD, MSPH
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Relationship of Diabetes Complications Severity to Healthcare Utilization and Costs Among Medicare Advantage Beneficiaries
Leslie Hazel-Fernandez, PhD, MPH; Yong Li, PhD; Damion Nero, PhD; Chad Moretz, ScD; S. Lane Slabaugh, PharmD, MBA; Yunus Meah, PharmD; Jean Baltz, MMSc, MSW; Nick C. Patel, PharmD, PhD; and Jonathan R. Bouchard, MS, RPh
Medical Homes: Cost Effects of Utilization by Chronically Ill Patients
Jason Neal, MA; Ravi Chawla, MBA; Christine M. Colombo, MBA; Richard L. Snyder, MD; and Somesh Nigam, PhD
Value-Based Insurance Design and Medication Adherence: Opportunities and Challenges
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Multiple Chronic Conditions in Type 2 Diabetes Mellitus: Prevalence and Consequences
Pei-Jung Lin, PhD; David M. Kent, MD, MSc; Aaron Winn, MPP; Joshua T. Cohen, PhD; and Peter J. Neumann, ScD
Prognostic Factors of Mortality Among Patients With Severe Hyperglycemia
Ya-Wun Guo, MD; Tzu-En Wu, MD, MS; and Harn-Shen Chen, MD, PhD
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Relationship of Diabetes Complications Severity to Healthcare Utilization and Costs Among Medicare Advantage Beneficiaries

Leslie Hazel-Fernandez, PhD, MPH; Yong Li, PhD; Damion Nero, PhD; Chad Moretz, ScD; S. Lane Slabaugh, PharmD, MBA; Yunus Meah, PharmD; Jean Baltz, MMSc, MSW; Nick C. Patel, PharmD, PhD; and Jonathan R. Bouchard, MS, RPh
This retrospective cohort study in a Medicare Advantage population posits that type 2 diabetes mellitus complications pose an excess burden on healthcare resource use and related costs.
This study evaluated the usefulness of the Diabetes Complications Severity Index (DCSI) in assessing healthcare resource utilization (HRU) and costs among Medicare Advantage plan members diagnosed with type 2 diabetes mellitus (T2DM).

Study Design
A retrospective cohort study of medical and pharmacy claims of 333,576 Medicare members aged 18 to 89 years with ≥1 medical claim with primary diagnosis or ≥2 medical claims with secondary diagnosis, of T2DM (International Classification of Diseases, Ninth Revision, Clinical Modification code 250.x0 or 250.x2) during the period of January 1, 2010, to December 31, 2011.

DCSI was assessed concurrently with HRU and healthcare costs (total, medical, and pharmacy). The cohort was subdivided into 6 DCSI groups: DCSI = 0 (no complications) through DCSI = 5+ (≥5). Associations of complication severity with HRU and costs of care were summarized using regression models.

A 1-point increase in DCSI was associated with a $2744 increase in total costs (a $2480 increase in medical costs plus a $264 increase in pharmacy costs). Increasing DCSI was associated with greater use of inpatient and emergency department (ED) services. Among the higher complications subgroups, there were greater representations of older patients, men, and cases of depressive disorders and hypoglycemia.

DCSI is useful for identifying Medicare plan members with T2DM who should be targeted for clinical programs. HRU and costs increased with DCSI severity. Increases in high-cost HRU, driven by inpatient and ED visits, suggest that preventing or delaying utilization of these services are essential to driving down costs in the T2DM population. Furthermore, high rates of depression and hypoglycemia warrant early screening and necessary treatment to improve patient outcomes.

Am J Manag Care. 2015;21(1):e62-e70
  • The Diabetes Complications Severity Index is useful for predicting direct costs and healthcare resource utilization (HRU) among Medicare Advantage patients with type 2 diabetes mellitus (T2DM) and for identifying at-risk subgroups to which preventive therapies and interventions can be targeted.
  • T2DM complications pose excess burdens on costs and HRU, particularly for members with more severe complications. Preventing or delaying use of these services can reduce costs.
  • Studies of associations between pharmacy utilization and medical outcomes can clarify whether small increases in drug costs may result in total cost savings.
  • Depression and hypoglycemia should be addressed early in the treatment of T2DM.
Adults 65 years and older make up the highest proportion of patients in the United States living with type 2 diabetes mellitus (T2DM).1 Healthcare resources used by elderly adults account for nearly 60% of diabetes-related healthcare expenditures, and these costs are largely absorbed by the Medicare program.1,2 The cost of care for T2DM is expected to increase in the coming decades as the US population continues to age and live longer.1 Strategies to decrease T2DM-associated morbidity, mortality, and costs are warranted.

Individuals of all ages who are diagnosed with T2DM are at increased risk for developing a range of medical complications including retinopathy, nephropathy, and cardiovascular disease.3-6 A significant proportion of healthcare resource utilization (HRU) and costs are for T2DM-related complications.3-6

The Diabetes Complications Severity Index (DCSI)7 measures the severity of diabetes-related complications, and predicts the related healthcare utilization and costs in managed care patient populations.7.8 A DCSI score ranges from 0 to 13 based on the presence and severity of 7 categories of complications: cardiovascular disease, nephropathy, retinopathy, peripheral vascular disease, stroke, neuropathy, and metabolic complications. Each complication category is scored on a scale from 0 to 2, determined by the presence and severity of the complications (0 = not present, 1 = some abnormality, and 2 = severe abnormality, except for neuropathy which is scored on a 2-point scale [0 = not present, 1 = abnormal]).7 Complications are identified as abnormal or severe based on International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes from medical records or medical claims, as done in previous studies using the DCSI as a measure.7,8 Within the retinopathy category, for instance, an ICD-9-CM diagnosis of background retinopathy (362.01) would be scored as “abnormal” (score of 1), whereas a diagnosis of blindness (369.xx.00-.99) would be scored as “severe” (score of 2).

The original DCSI was calculated using patients’ pharmacy, medical, and laboratory data. A more recent, modified version of the DCSI omitting laboratory claims from the nephropathy category has demonstrated similar effectiveness as the original DCSI in estimating diabetes severity and in predicting hospitalizations. In addition, the modified DCSI is a good predictor of healthcare costs.8,9 Furthermore, this DCSI version is superior at predicting concurrent healthcare costs as compared with predicting future costs.

Chang and colleagues constructed the modified version of the DCSI using administrative claims from 7 health insurance plans,8 demonstrating its effectiveness in predicting healthcare costs when the predictor (DCSI) and outcomes (healthcare costs) were measured concurrently.8 They concluded that healthcare plans could reduce costs by employing the DCSI to identify and target patients who were the most in need of disease management programs.8 The authors acknowledged, however, that 80% of participants had a DCSI score of 0 (no diabetes complications); thus, their study sample had limited variability in terms of morbidity. A study using a sample with varying levels of illness could allow for a more precise identification of patient groups to target for intervention based on DCSI scores.

This study was designed to describe the demographic and clinical characteristics of a large cohort of Medicare Advantage with Prescription Drug (MAPD) coverage health plan members diagnosed with T2DM, and to evaluate the association of DCSI with HRU and costs in this cohort. The modified DCSI8 was used in this study to determine complications severity.


Design and Data Sources

In this retrospective study involving MAPD health plan members diagnosed with T2DM, the association of DCSI with HRU and costs was analyzed. The data sources included member enrollment, medical, and pharmacy data generated from the claims database of a large national health plan (Humana). This analysis was part of a larger study, which was approved by an independent Institutional Review Board.

Study Population

The study sample was composed of Humana members enrolled in an MAPD plan in 2010 and 2011. Members with T2DM were identified via ICD-9-CM diagnostic codes. Members’ medical and pharmacy claims were assessed during a 24-month study period (January 1, 2010, to December 31, 2011) to determine the HRU and cost of care for members, stratified by their level of T2DM complications severity. The severity of complications was quantified using the modified DCSI,8 which in turn was assessed concurrently with annual HRU and costs over the study period.

Eligible study participants were: 1) continuously enrolled for at least 24 consecutive months; 2) aged 18 to 89 years as of January 1, 2010; and 3) diagnosed with T2DM (defined as having at least 1 medical claim with a primary diagnosis, or at least 2 medical claims with a secondary diagnosis, of T2DM [ICD-9-CM code 250.x0 or 250. x2]). Members were excluded from the study if they had medical claims for gestational diabetes mellitus (ICD-9 CM code 648.8), type 1 diabetes (ICD-9 CM code 250.x1 or 250.x3), or pregnancy (630.xx-679.xx or v22.x-v24.x) during the study period.

Primary Outcome Measures

The primary outcome measures were all-cause HRU and costs, based on all medical and pharmacy claims regardless of diagnosis or medication therapeutic class. HRU was the quantity of outpatient, inpatient, and emergency department (ED) services captured from medical claims for the study period. Outpatient utilization included medical claims where the service location was a physician’s office or outpatient facility, and included physician office visits, procedures, and tests. Inpatient hospital utilization included medical claims originating from an inpatient hospital facility. Hospitalization claims with the same discharge and admit dates were considered a single hospitalization, and ED utilization was all medical claims where the service location was an ED. ED claims with the same service date as a hospitalization were counted as a hospitalization, as this is viewed as an admission occurring through ED triage.

All-cause healthcare costs (total, medical, and pharmacy) were measured for both study years. Medical and pharmacy costs were adjusted to 2011 dollars using the US Bureau of Labor Statistics’ Consumer Price Index for Medical Care. Medical costs were the sum of costs associated with medical claims where the place of service was an outpatient, inpatient, or ED facility. Pharmacy costs were the sum of costs associated with pharmacy claims. Total healthcare costs were the sum of medical and pharmacy costs.

Independent Variable and Covariates

The modified DCSI was used in this study to determine complications severity.8 The study sample was divided into 6 subgroups consisting of DCSI scores 0 (no complications) through 5+ (5 or more). The DCSI, which served as the independent variable, was assessed concurrently with HRU and costs.

The following demographic characteristics were controlled for when assessing the association of DCSI with HRU and costs: age (as of January 1, 2010), gender (male or female), race/ethnicity (white, black, Hispanic, or other), and regional assignment in 2010 (northeast, midwest, south, and west, based on the US Census Bureau assignment of states to geographic regions). Low-income subsidy status referred to members with limited resources and income below 150% of the US federal poverty threshold who were eligible for additional premium and cost-share assistance for prescription drugs.

The following clinical characteristics were controlled for in order to best isolate the relationship of DCSI with HRU and costs: chronic disease burden, antidiabetic medication use, hypoglycemic events, and depression. These clinical factors were chosen because they either are known or hypothesized to have a potentially significant impact on HRU and costs, and, with the exception of chronic disease burden, these factors are not captured in the DCSI. Chronic disease burden was measured with the unweighted RxRisk-V Score, a prescription claims-based comorbidity index10-13 that measures a patient’s level of co-occurring conditions by mapping pharmacy claims for drug classes and individual drugs via Medi-Span generic product identifier codes to specific disease states. It is important to note that the RxRisk-V score may be related to the DCSI itself, given that a patient could have been on medications to treat the respective T2DM complication.

Treatment by antidiabetic therapeutic classes was the proportion of members with at least 1 pharmacy claim during the 24-month period for a medication within 1 of the following respective medication classes: biguanides, sulfonylureas, thiazolidinediones, amylin agonists, meglitinides, alpha-glucosidase inhibitors, glucagon-like peptide-1 receptor agonists, dipeptidyl peptidase-4 inhibitors, bile acid sequestrants, dopamine-2 agonists, insulin, and combination oral antidiabetics. A hypoglycemic event was at least 1 outpatient claim with a diagnosis of hypoglycemia (ICD-9 CM codes 251.0, 251.1, 251.2, and 250.8 [excluding claims with co-diagnoses of 259.8, 272.7, 681.xx, 682.xx, 686.9x, 707.1-707.9, 709.3, 730.0-730.2, and 731.8 in the same visit or hospitalization]) in any position, or at least 1 ED claim with a diagnosis of hypoglycemia in the primary position.14 A diagnosis of depression, a common comorbidity of T2DM,15 indicated that a member had at least 1 claim with an ICD-9-CM diagnosis of depression (296.2x, 296.3x, 300.4, and 311).

Statistical Analyses

Univariate descriptive statistics were used to describe the demographic and clinical characteristics of each DCSI cohort. Medication use was the mean number of antidiabetic classes. Place of service (outpatient, inpatient, and ED) all-cause HRU was the mean (SD) number of healthcare visits calculated on an annual basis. All-cause HRU included counts and percentages. Utilization of a specific type of service would be reported as a dichotomous status variable indicating the proportion of total members within each DCSI cohort using the specific type of service. Chi-square analyses, ANOVA pairwise comparisons, and t tests were used to determine differences in characteristics across DCSI groupings.

Generalized linear models (GLMs) were used to test the association of DCSI with HRU and costs. GLMs were used because HRU and costs had skewed distributions (ie, a small number of members with very high values). Separate models were generated for each outcome, adjusting for demographic and clinical characteristics. HRU measures included numbers of outpatient visits, inpatient stays, and ED visits; cost measures included total healthcare, medical, and pharmacy costs. HRU and cost measures were averaged over the 2-year period.

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