A large proportion of medical costs for type 2 diabetes are attributable to complications and comorbidities, especially end stage renal disease with dialysis or kidney transplantation.
Published Online: May 23, 2013
Rui Li, PhD; Dori Bilik, MBA; Morton B. Brown, PhD; Ping Zhang, PhD; Susan L. Ettner, PhD; Ronald T. Ackermann, MD; Jesse C. Crosson, PhD; and William H. Herman, MD
Objectives: To estimate the direct medical costs associated with type 2 diabetes, its complications, and its comorbidities among US managed carepatients.
Study Design: Data were from patient surveys, chart reviews, and health insurance claims for 7109 people with type 2 diabetes from 8 health plans participating in the Translating Research Into Action for Diabetes (TRIAD) study between 1999 and 2002.
Methods: A generalized linear regression model was developed to estimate the association of patients’ demographic characteristics, tobaccouse status, treatments, related complications, andcomorbidities with medical costs.
Results: The mean annualized direct medical cost was $2465 for a white man with type 2 diabetes who had been diagnosed fewer than 15 yearsearlier, was treated with oral medication or dietalone, and had no complications or comorbidities. We found annualized medical costs to be 10% to 50% higher for women and for patients whose diabetes had been diagnosed 15 or moreyears earlier, who used tobacco, who were being treated with insulin, or who had several other complications. Coronary heart disease, congestive heart failure, hemiplegia, and amputation were each associated with 70% to 150% higher costs. Costs were approximately 300% higher for end-stage renal disease treated with dialysis and approximately 500% higher for end-stage renal disease with kidney transplantation.
Conclusions: Most medical costs incurred by patients with type 2 diabetes are related to complications and comorbidities. Our cost estimatescan help when determining the most cost-effectiveinterventions to prevent complications and comorbidities.
Am J Manag Care. 2013;19(5):421-430
Because currently available simulation models often do not differentiate costs by patient characteristics, our study has several advantages over previous ones.
Data on cost and patient characteristics were from both patient surveys and chart reviews.
Variations in cost associated with different patient characteristics and treatments were assessed in a large and demographically and geographically diverse sample of adults with diabetes.
Economic researchers can use these estimates in simulation models to assess the potential cost-effectiveness of interventions intended to prevent or delay type 2 diabetes and its complications.
The worldwide prevalence of type 2 diabetes and the demand for and costs of medical care for treating it have increased over the past decade.1-4 Simulation models have been developedto estimate the long-term health and economic consequences of diabetes and to help policy makers identify the most cost-effective interventions for preventing and controlling diabetes.5 The cost data needed for diabetes cost-effectiveness models should be accurate and broadly applicable, come from large samples of patients with type 2 diabetes, and account for variations in costs due to differences in treatments, demographic characteristics, complications, and comorbidities among patients.
Many previous cost studies have estimated the economic cost of diabetes for a country or region as an aggregate, rather than based on individual-level variation.3,4,6,7 Although others have used data from a single health plan, the resulting estimates might not be widely comparable to those from other settings.8,9 Most studies that estimated the costs of type 2 diabetes at the individual level did not consider the variation of the costs among patients with different characteristics.3,4,6 Other studies have used self-reported healthcare costs,10,11 which might have been inaccurate due to incomplete recall or potential bias from the influence of social desirability. Still others have obtained components of diabetes costs from multiple data sources, each of which might have had its own biases, thus limiting the comparability of the estimates.12
The purpose of this study was to use a large and demographically and geographically diverse sample of adults with diabetes in the United States to provide cost data for diabetes simulation models. We described the relationship between direct medical costs and individual patient demographic characteristics, tobacco use status, diabetes treatments, complications, and comorbidities in persons with type 2 diabetes.
We analyzed patient surveys, medical records, and administrative data for 7109 patients with type 2 diabetes who participated in the Translating Research Into Action for Diabetes (TRIAD) study between 1999 and 2002.13 TRIAD was designed to assess how the organization and structure of managed care health plans influence the processes and outcomes of diabetes care. The study involved 10 health plans and 68 provider groups serving approximately 180,000 persons with diabetes across the United States. TRIAD participants—who had to be 18 years or older, community dwelling, English or Spanish speaking, continuously enrolled in the same health plan for at least 18 months, not pregnant, and with more than 1 claim for health services—were sampled from the 10 health plans. We classified patients as having type 2 diabetes if the onset was before 30 years of age without current insulin treatment or if the onset was after 30 years of age with or without current insulin treatment. A total of 11,927 people from the initial sample met these criteria.
Of the 8820 patients whose medical records were abstracted at baseline, 8364 had Charlson Comorbidity Index scores. (The Charlson Comorbidity Index predicts the 10-year mortality for a patient who may have a range of comorbid conditions.) Of the 10 participating health plans, 2 (1255 patients) were excluded due to unavailability of some elements of the health plan administrative data, leaving 7109 patients for our analyses.
TRIAD collected baseline and follow-up data from health plans, provider groups, and diabetes patients. For this analysis, we used patient survey data gathered from the third quarter of 2000 to the first quarter of 2002, and chart review data and administrative data for the 18 months prior to each respondent’s survey date.
Direct medical costs from the 18 months prior to each patient’s baseline survey were determined from health plan administrative data; costs included inpatient, outpatient, emergency department treatment, pharmacy, and other expenses such as outpatient radiology and laboratory tests. We attempted to minimize price variations due to different labor, nonlabor, and other costs for the same services provided in different health plans. Inpatient costs were calculated based on the patient’s final diagnosis using the group weight rate from the Centers for Medicare & Medicaid Services (CMS) for fiscal year (FY) 2002 multiplied by the FY 2000 multiplier ($4328). Outpatient costs represented the estimated costs for procedures based on standardized reimbursement rates developed from the FY 2002 Medicare fee schedules by procedure code. Outpatientpharmaceutical costs were based on average wholesale prices per unit. We used the actual cost in dollars as described above without adjusting it to a single year’s cost index because the way health plans reported the data did not allow us to do so. However, we adjusted for the survey interview year in the regression model to attenuate the inflation effect. We also reported the costs in 2010 dollars using the Consumer Price Index for medical services to reflect inflation in treating diabetes over the past decade.Patient copayments and other out-of-pocket costs were not considered in our analyses. Thus, our analyses represent direct medical costs from the perspective of a large health system.
Patient characteristics (eg, age, sex, race and ethnicity, education, income, tobacco use) were determined from patient surveys. Diabetes-related variables, including time since diagnosis of diabetes and methods of treatment, were also determined from patient surveys. Diabetic complications and comorbidities—including retinopathy, nephropathy, neuropathy, hypertension, dyslipidemia, cerebrovascular disease, cardiovascular disease, and peripheral vascular disease—were determined from both patient surveys and medical record reviews. A comorbid condition was considered present if the 18-month medical record or chart review showed that the patient had the condition or if, in the patient survey, the patient recounted being told that in the past 18 months he or she had had the condition. Additional comorbid conditions that are components of the Charlson Comorbidity Index were determined from medical record reviews.14,1
We divided 18-month cost figures by 1.5 to standardize them as annual cost amounts. Cost distributions were right skewed; for regression analysis, to account for the skewed distribution we developed a generalized linear model (GLM) with log-link function to estimate the association between costs and patient demographic characteristics, tobacco use status, diabetes treatments, complications, and comorbidities. Health plan indicators and the year of the interview were also included in the calculations to control for health plan fixed effects and cost inflation, respectively. As the GLM with log-link function regression model required original cost data to be log-transformed, coefficients and 95% confidence intervals from the regression model needed to be back-transformed to the ordinal scale using an exponential function to get the cost estimates. We called these back-transformed coefficients cost multipliers in this study.
For this model, the base case was determined to be the 1-year direct medical costs for a white man diagnosed with diabetes for fewer than 15 years, treated with diet or oral agents, and with no complications or comorbidities. Because the costs for treating such a person differed among health plans and we did not know which health plan represented the “true” cost, it was not appropriate to use a cost estimate from any of the health plans as the base-case cost. We decided to use the mean of the estimated base-case costs among all of the health plans. To do this, we included all of the indicators for each health plan in the model and omitted the intercept to get the mean base-case cost in each of the 8 health plans, then computed the mean of the estimated mean costs in the health plans. That provided a modeled mean cost to use as the uniform direct medical cost for a base-case patient.
Because the model had a log-link function, it was a multiplicative model. To determine the relative increase in direct medical costs for a given patient with characteristics other than those of a base-case patient, we multiplied the direct medical costs for a base-case patient by the product of the cost multipliers calculated for each demographic characteristic, tobacco use status, diabetes treatment, complication, or comorbidity that applied to that patient.
All of the independent variables were coded as dichotomous or discrete variables. In the regression analysis, missing values for independent variables were imputed 5 times using a multiple imputation method.16 We did not impute missing dependent variables. Because the purpose of our model was cost prediction, we used stepwise regression and only variables with regression coefficients significant at the P <.05 level were kept in the model. For the same group of covariates at different levels, we collapsed the groups that were not statistically significant into 1 level. When several independent variables were highly correlated with each other (correlation coefficient >.25), only 1 was included in the model. For example, income and education are highly correlated, so income was deleted from the model. We did not consider interaction effects for our analyses. We used SAS version 9.1.3 (SAS Institute, Cary, North Carolina) and STATA version 10.1 (StataCorp, College Station, Texas) to perform the analyses.
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