Utility Decrements Associated With Diabetes and Related Complications: Estimates From a Population-Based Study in Germany

OBJECTIVES

Health utility decrement estimates for diabetes and complications are needed for parametrization of simulation models that aim to assess the cost-utility of diabetes prevention and care strategies. This study estimates health utility decrements associated with diabetes and cardiovascular and microvascular complications from a population-based German study.

METHODS

Data were obtained from the population based cross-sectional KORA (Cooperative Health Research in the Augsburg Region) health questionnaire 2016 and comprised n = 1072 individuals with type 2 diabetes and n = 7879 individuals without diabetes. Health utility was assessed through the EQ-5D-5L. We used linear regression models with interaction terms between type 2 diabetes and different cardiovascular and microvascular complications while adjusting for demographic and socio-economic factors and other comorbidities.

RESULTS

Type 2 diabetes (β = -0.028, standard error [SE] = 0.014), stroke (β = -0.070, SE = 0.010), cardiac arrhythmia (β = -0.031, SE = 0.006), heart failure (β = -0.073, SE = 0.009), coronary heart disease (β = -0.028, SE = 0.010), myocardial infarction (β = -0.020, SE = 0.011, estimates of main effect), and neuropathy (β = -0.067, SE = 0.020), diabetic foot (β = -0.042, SE = 0.030), nephropathy (β = -0.032, SE = 0.025), and blindness (β = -0.094, SE = 0.056, estimates of interaction terms) were negatively associated with health utility. The interaction term for diabetes x stroke (β = -0.052, SE = 0.021) showed that the utility decrement for stroke is significantly larger in people with type 2 diabetes than in people without diabetes.

CONCLUSIONS

Diabetes, cardiovascular, and microvascular conditions are associated with significant health utility decrements. Utility decrements for some conditions differ between people with and without type 2 diabetes. These results are of high relevance for the parametrization of decision analytic simulation models and applied health economic evaluations in the field of prevention and management of type 2 diabetes in Germany.

Assessment of excess medical costs for persons with type 2 diabetes according to age groups: an analysis of German health insurance claims data

AIM

This cross-sectional study used a large nationwide claims data set to assess the excess medical costs of people with type 2 diabetes according to age group in 2015.

METHODS

Data from 291 709 people with diabetes and 291 709 age- and sex-matched controls were analysed. Total costs (expressed as 2015 euros) of outpatient and inpatient services, medication, rehabilitation, and the provision of aids and appliances were examined. Overall and age-stratified excess costs of people with diabetes were estimated using gamma regression with a log-link.

RESULTS

Overall, the estimated total direct costs of a person with type 2 diabetes are approximately double those of a person without diabetes: €4727 vs. €2196, respectively. Absolute excess costs were approximately the same in all age groups (around €2500), however, relative excess costs of persons with diabetes were much higher in younger (~ 334% for < 50 years) than in older age groups (~ 156% for ≥ 80 years). Regional costs, both absolute and excess, partly differed from the national level.

CONCLUSIONS

This study complements and updates previous studies on the excess medical costs of people with diabetes in Germany. The results indicate the importance of preventing the development of type 2 diabetes, especially in younger age groups. Longitudinal and regional studies examining changes in prevalence and the development of excess costs in groups with different types of diabetes, and according to age, would be of interest to validate our findings and better understand the avoidable burden of having diabetes.

Performance of the UKPDS Outcomes Model 2 for Predicting Death and Cardiovascular Events in Patients with Type 2 Diabetes Mellitus from a German Population-Based Cohort

BACKGROUND AND OBJECTIVE

Accurate prediction of relevant outcomes is important for targeting therapies and to support health economic evaluations of healthcare interventions in patients with diabetes. The United Kingdom Prospective Diabetes Study (UKPDS) risk equations are some of the most frequently used risk equations. This study aims to analyze the calibration and discrimination of the updated UKPDS risk equations as implemented in the UKPDS Outcomes Model 2 (UKPDS-OM2) for predicting cardiovascular (CV) events and death in patients with type 2 diabetes mellitus (T2DM) from population-based German samples.

METHODS

Analyses are based on data of 456 individuals diagnosed with T2DM who participated in two population-based studies in southern Germany (KORA (Cooperative Health Research in the Region of Augsburg)-A: 1997/1998, n = 178; KORA-S4: 1999-2001, n = 278). We compared the participants' 10-year observed incidence of mortality, CV mortality, myocardial infarction (MI), and stroke with the predicted event rate of the UKPDS-OM2. The model's calibration was evaluated by Greenwood-Nam-D'Agostino tests and discrimination was evaluated by C-statistics.

RESULTS

Of the 456 participants with T2DM (mean age 65 years, mean diabetes duration 8 years, 56% male), over the 10-year follow-up time 129 died (61 due to CV events), 64 experienced an MI, and 46 a stroke. The UKPDS-OM2 significantly over-predicted mortality and CV mortality by 25% and 28%, respectively (Greenwood-Nam-D'Agostino tests: p < 0.01), but there was no significant difference between predicted and observed MI and stroke risk. The model poorly discriminated for death (C-statistic [95% confidence interval] = 0.64 [0.60-0.69]), CV death (0.64 [0.58-0.71]), and MI (0.58 [0.52-0.66]), and failed to discriminate for stroke (0.57 [0.47-0.66]).

CONCLUSIONS

The study results demonstrate acceptable calibration and poor discrimination of the UKPDS-OM2 for predicting death and CV events in this population-based German sample. Those limitations should be considered when using the UKPDS-OM2 for economic evaluations of healthcare strategies or using the risk equations for clinical decision-making.

Exploring Different Strategies of Assessing the Economic Impact of Multiple Diabetes-Associated Complications and Their Interactions: A Large Claims-Based Study in Germany

BACKGROUND

In the context of an aging population with increasing diabetes prevalence, people are living longer with diabetes, which leads to increased multimorbidity and economic burden.

OBJECTIVE

The primary aim was to explore different strategies that address the economic impact of multiple type 2 diabetes-related complications and their interactions.

METHODS

We used a generalized estimating equations approach based on nationwide statutory health insurance data from 316,220 patients with type 2 diabetes (baseline year 2012, 3 years of follow-up). We estimated annual total costs (in 2015 euros) for type 2 diabetes-related complications and, in addition, explored different strategies to assess diabetes-related multimorbidity: number of prevalent complications, co-occurrence of micro- and macrovascular complications, disease-disease interactions of prevalent complications, and interactions between prevalent/incident complications.

RESULTS

The increased number of complications was significantly associated with higher total costs. Further assessment of interactions showed that macrovascular complications (e.g., chronic heart failure) and high-cost complications (e.g., end-stage renal disease, amputation) led to significant positive effects of interactions on costs, whereas early microvascular complications (e.g., retinopathy) caused negative interactions. The chronology of the onset of these complications turned out to have an additional impact on the interactions and their effect on total costs.

CONCLUSIONS

Health economic diabetes models and evaluations of interventions in patients with diabetes-related complications should pay more attention to the economic effect of specific disease interactions. Politically, our findings support the development of more integrated diabetes care programs that take better account of multimorbidity. Further observational studies are needed to elucidate the shared pathogenic mechanisms of diabetes complications.

Health Care Costs Associated With Incident Complications in Patients With Type 2 Diabetes in Germany

OBJECTIVE

The aim of this study is to provide reliable regression-based estimates of costs associated with different type 2 diabetes complications.

RESEARCH DESIGN AND METHODS

We used nationwide statutory health insurance (SHI) data from 316,220 patients with type 2 diabetes. Costs for inpatient and outpatient care, pharmaceuticals, rehabilitation, and nonmedical aids and appliances were assessed in the years 2013-2015. Quarterly observations are available for each year. We estimated costs (in 2015 euro) for complications using a generalized estimating equations model with a normal distribution adjusted for age, sex, occurrence of different complications, and history of complications at baseline, 2012. Two- and threefold interactions were included in an extended model.

RESULTS

The base case model estimated total costs in the quarter of event for the example of a 60- to 69-year-old man as follows: diabetic foot €1,293, amputation €14,284, retinopathy €671, blindness €2,933, nephropathy €3,353, end-stage renal disease (ESRD) €22,691, nonfatal stroke €9,769, fatal stroke €11,176, nonfatal myocardial infarction (MI)/cardiac arrest (CA) €8,035, fatal MI/CA €8,700, nonfatal ischemic heart disease (IHD) €6,548, fatal IHD €20,942, chronic heart failure €3,912, and angina pectoris €2,695. In the subsequent quarters, costs ranged from €681 for retinopathy to €6,130 for ESRD.

CONCLUSIONS

Type 2 diabetes complications have a significant impact on total health care costs in the SHI system, not only in the quarter of event but also in subsequent years. Men and women from different age-groups differ in their costs for complications. Our comprehensive estimates may support the parametrization of diabetes models and help clinicians and policy makers to quantify the economic burden of diabetes complications in the context of new prevention and treatment programs.