Health and economic impact of improved glucose, blood pressure and lipid control among German adults with type 2 diabetes: a modelling study

Aims/hypothesis The aim of this study was to estimate the long-term health and economic consequences of improved risk factor control in German adults with type 2 diabetes. Methods We used the UK Prospective Diabetes Study Outcomes Model 2 to project the patient-level health outcomes and healthcare costs of people with type 2 diabetes in Germany over 5, 10 and 30 years. We parameterised the model using the best available data on population characteristics, healthcare costs and health-related quality of life from German studies. The modelled scenarios were: (1) a permanent reduction of HbA1c by 5.5 mmol/mol (0.5%), of systolic BP (SBP) by 10 mmHg, or of LDL-cholesterol by 0.26 mmol/l in all patients, and (2) achievement of guideline care recommendations for HbA1c (≤53 mmol/mol [7%]), SBP (≤140 mmHg) or LDL-cholesterol (≤2.6 mmol/l) in patients who do not meet the recommendations. We calculated nationwide estimates using age- and sex-specific quality-adjusted life year (QALY) and cost estimates, type 2 diabetes prevalence and population size. Results Over 10 years, a permanent reduction of HbA1c by 5.5 mmol/mol (0.5%), SBP by 10 mmHg or LDL-cholesterol by 0.26 mmol/l led to per-person savings in healthcare expenditures of €121, €238 and €34, and 0.01, 0.02 and 0.015 QALYs gained, respectively. Achieving guideline care recommendations for HbA1c, SBP or LDL-cholesterol could reduce healthcare expenditure by €451, €507 and €327 and gained 0.03, 0.05 and 0.06 additional QALYs in individuals who did not meet the recommendations. Nationally, achieving guideline care recommendations for HbA1c, SBP and LDL-cholesterol could reduce healthcare costs by over €1.9 billion. Conclusions/interpretation Sustained improvements in HbA1c, SBP and LDL-cholesterol control among diabetes patients in Germany can lead to substantial health benefits and reduce healthcare expenditures. Graphical Abstract Supplementary Information The online version contains peer-reviewed but unedited supplementary material available at 10.1007/s00125-023-05950-3.


ESM Appendix A. Generation of synthetic population with R package 'Synthpop'
The generation of the synthetic data assumes that the observed data is a sample from a population with parameters that can be estimated by the synthesizer. The synthesizer fits the data to the assumed distribution and obtains estimates of its parameters (1). We compared the correlations between risk factors and demographic characteristics of the new sample with those observed in the original KORA-S4 sample using graphs and correlation coefficients. The distributions of variables and the correlations between them in the synthetic sample were mostly preserved from the original KORA-S4 sample.

ESM Appendix B. Model parameters for health care expenditures
We extracted the annual health care expenditures associated with eight complications: IHD (Ischemic heart disease), MI (Myocardial infarction), Heart failure, Stroke, Amputation, Blindness, Renal failure and Ulcer, for type 2 diabetes patients in Germany stratified by age and sex, at the year of events and in subsequent years, as input parameters for our model from 'Supplementary Table 8' of Kähm et al. 2018 (2).
For 'Heart Failure', we used the age and sex specific health care expenditure estimates of 'CHF (Chronic Heart Failure)' in the paper, as equivalent; For 'Renal Failure', we used the estimates of 'ESRD' (End Stage Renal Disease) as equivalent; For 'Ulcer', we used the estimates of 'Diabetic foot' as equivalent.
The age and sex specific annual health care costs associated with incident complications in patients with type 2 diabetes estimated from German population that we used as model parameters in UKPDS OM2 are presented in ESM Table 1 and cardiovascular or microvascular complications (ESM Table 3). In addition, the utility decrements (coefficients) associated with the demographic factors (age, sex, etc.) and comorbidities (cancer, asthma, etc.) can be extracted. From the sample characteristics (proportions of binary/categorical variables and means of continuous variables) and the regression coefficients we derived the utility value for an average person with type 2 diabetes, which we assumed to be representative for the utility in of an adult with Type 2 diabetes in Germany.

ESM Appendix D. Per-person effects from improved risk factor control stratified by age and sex
To explore the health outcomes in age and sex subgroups, we stratified the study population by age (<60 years / 60-70 years / >70 years) and sex (male / female) and calculated simulation effects in each subgroup. The primary reason for this age categorization was to ensure a sufficient sample size in each subgroup, in order to derive stable results from the simulation. Per-person cost savings, QALY and life years gains by age and sex from improved ABC level over 5, 10, and 30 years are presented in ESM

ESM Appendix E. Number of type 2 diabetes patients by age and sex in German population
To estimate the number of adults with type 2 diabetes by age and sex in Germany, population size and prevalence of type 2 diabetes in each age and sex subgroup is required. We extracted population size estimates from a paper that reported adjusted inter-censal German population estimates (data from year 2010 was used) (6), and extracted the prevalence of type 2 diabetes from a study that used ICDcoded diagnosis from statutory health insurance data in Germany from year 2010 (7). Details are presented in ESM Table 7.
With the population statistics available in finer groups, we were able to calculate the number of people with type 2 diabetes in the prespecified subgroups, shown in ESM Table 8. a In the paper 'The prevalence and incidence of diabetes in Germany'(7), the prevalence of type 2 diabetes was only reported for age groups over 40 years of age. We assumed the lowest prevalence reported as the prevalence for the younger age group (20-39 years).

ESM Appendix G. Tornado plots showing one-way sensitivity analysis by alternative discount rates, model parameters and number of internal loops
Tornado plots below show one-way sensitivity analysis of change in long-term cost savings (a) and QALY gains (b) from improved ABC, arising from alternative discount rates being 0% and 5% (vs. 3.5%), alternative model parameters of complications health care expenditures and utility decrements being double and half of the current values, and alternative number of internal loops being 5,000 and 20,000 (vs. 10,000).