Can Routine Data be used to Determine the Target Population of Patients with Type 2 Diabetes in Early Benefit Assessments in Germany?

OBJECTIVES: Since 2011, early benefit assessment of all new drugs launched in Germany is mandatory. The exact determination of the appropriate target population (i. e. patients eligible for a drug) plays an important role for subsequent price negotiations. In type 2 diabetes, the size of the target population varies considerably between company dossiers submitted for assessment. Our aim was to explore whether routine data from all persons insured in German statutory health insurance (SHI) funds can be used to derive information on the size of the target population with type 2 diabetes. METHODS: We explored how the data available at the German Institute of Medical Documentation and Information (DIMDI) can be used to obtain the information required. A data-based concept was chosen and the selection criteria were developed in a multidisciplinary project group. Before finalizing the database query, the criteria were evaluated in a test database and the database query was then repeatedly modified. RESULTS: At the time of the design of our analysis in 2017, the most recent data available at DIMDI were for 2013. The algorithm we developed for identifying patients with type 2 diabetes and classifying them according to their medication, based primarily on the combination of ICD and ATC codes, enabled us to determine the size of target populations for different indications in diabetes mellitus type 2. CONCLUSION: Our methodological approach seems to be suitable to determine target populations in type 2 diabetes.

Patients' preferences in periodontal disease treatment elicited alongside an IQWiG benefit assessment: a feasibility study.

BACKGROUND AND PURPOSE: The German Institute for Quality and Efficiency in Health Care (IQWiG) previously tested two preference elicitation methods in pilot projects and regarded them as generally feasible for prioritizing outcome-specific results of benefit assessment. The present study aimed to investigate the feasibility of completing a discrete choice experiment (DCE) within 3 months and to determine the relative importance of attributes of periodontal disease and its treatment. PATIENTS AND METHODS: This preference elicitation was conducted alongside the IQWiG benefit assessment of systematic treatments of periodontal diseases. Attributes were defined based on the benefit assessment, literature review, and patients' and periodontologists' interviews. The DCE survey was completed by patients with a history of periodontal disease. Preferences were elicited for the attributes "tooth loss within next 10 years", "own costs for treatment, follow-up visits, re-treatment", "complaints and symptoms", and "frequency of follow-up visits". Patients completed a self-administered questionnaire including 12 choice tasks. Data were analyzed using a random parameters logit model. The relative attribute importance was calculated based on level ranges. RESULTS: Within 3 months, survey development, data collection among 267 patients, data analysis, and provision of a study report could be completed. The analysis showed that tooth loss (score 0.73) was the most important attribute in patients' decisions, followed by complaints and symptoms (0.22), frequency of follow-up visits (0.02), and costs (0.03) (relative importance scores summing up to 1). CONCLUSION: A preference analysis performing a DCE can be generally feasible within 3 months; however, a good research infrastructure and access to patients is required. Outcomes used in benefit assessments might need to be adapted to be used in preference analyses.

Estimation of Input Costs for a Markov Model in a German Health Economic Evaluation of Newer Antidepressants.

Background: Estimating input costs for Markov models in health economic evaluations requires health state-specific costing. This is a challenge in mental illnesses such as depression, as interventions are not clearly related to health states. We present a hybrid approach to health state-specific cost estimation for a German health economic evaluation of antidepressants. Methods: Costs were determined from the perspective of the community of persons insured by statutory health insurance ("SHI insuree perspective") and included costs for outpatient care, inpatient care, drugs, and psychotherapy. In an additional step, costs for rehabilitation and productivity losses were calculated from the societal perspective. We collected resource use data in a stepwise hierarchical approach using SHI claims data, where available, followed by data from clinical guidelines and expert surveys. Bottom-up and top-down costing approaches were combined. Results: Depending on the drug strategy and health state, the average input costs varied per patient per 8-week Markov cycle. The highest costs occurred for agomelatine in the health state first-line treatment (FT) ("FT relapse") with €506 from the SHI insuree perspective and €724 from the societal perspective. From both perspectives, the lowest costs (excluding placebo) were €55 for selective serotonin reuptake inhibitors in the health state "FT remission." Conclusion: To estimate costs in health economic evaluations of treatments for depression, it can be necessary to link different data sources and costing approaches systematically to meet the requirements of the decision-analytic model. As this can increase complexity, the corresponding calculations should be presented transparently. The approach presented could provide useful input for future models.

Preferences for antiviral therapy of chronic hepatitis C: a discrete choice experiment.

BACKGROUND: The German Institute for Quality and Efficiency in Health Care (IQWiG) uses patient-relevant outcomes to inform decision-makers. OBJECTIVE: IQWiG conducted a pilot study to examine whether discrete choice experiments (DCEs) can be applied in health economic evaluations in Germany to identify, weight, and prioritize multiple patient-relevant outcomes, using the example of antiviral therapy for chronic hepatitis C (HCV). A further objective was to contribute to a more structured approach towards eliciting and comparing preferences across key stakeholders. METHODS: In autumn 2010, a DCE questionnaire was sent to patients with chronic HCV to estimate preferences across seven outcomes ("attributes"), including treatment efficacy [sustained viral response (SVR) at 6 months], adverse effects (flu-like symptoms, gastrointestinal symptoms, psychiatric symptoms, and skin symptoms/alopecia), and measures of treatment burden (duration of therapy, frequency of injections). A linear model and an effects coded full model were applied to assess the relative importance of the attributes. RESULTS: In total N = 326 patients were included. A clear preference for SVR was shown; frequency of injections and duration of therapy shared the second rank, while psychiatric symptoms ranked third. The duration of flu-like symptoms was the least important attribute. CONCLUSION: Our findings indicate that it is possible to perform a DCE at the national level in a health technology assessment agency. The weighting of multiple outcomes allows an indication-specific and evidence-based measure to be used in health economic evaluations. In decision-making in health care, the approach generally allows for consideration of patient-relevant trade-offs regarding the benefits and harms of medical interventions.

Differences in EQ-5D-3L health state valuations among patients with musculoskeletal diseases, health care professionals and healthy volunteers.

BACKGROUND: Results from existing studies indicate that different respondent groups' health state valuations in cost-utility analyses are not equivalent. OBJECTIVES: The objectives in our study were to analyse differences in health state valuations among three respondent groups in the context of medical rehabilitation in Germany. METHODS: Using the time trade-off (TTO) technique, valuations of EQ-5D-3L health states were obtained from patients with musculoskeletal diseases, healthy volunteers and health care professionals. We used linear mixed models to predict TTO utilities and specified and tested interaction effects. RESULTS: We identified statistically significant (p < 0.05) differences among the three groups in six out of 42 health states. On average, patients' TTO values were somewhat higher compared with other respondent groups. Most of these differences occurred in severe health states. Mean differences and mean absolute differences were 0.02 and 0.14 for patients vs healthy volunteers and 0.06 and 0.14 for patients vs health care professionals. Furthermore, significant effects among respondents were observed for seven of the 22 possible interactions describing differences between respondent groups. Coefficients associated with significant interaction effects ranged from 0.08 to 0.18 (absolute values). CONCLUSION: The results of our study suggest that TTO valuations of health states differ depending on the specific respondent group from which valuations are obtained. On average, these differences were small. However, researchers and decision makers should remain aware of these differences when interpreting incremental cost-utility assessments.

Decision making in Germany: is health economic evaluation as a supporting tool a sleeping beauty?

For many years, the legal situation within the statutory health insurance (SHI) system in Germany has allowed for health economic evaluations. There are various reasons why health economic evaluations have played virtually no role in decision making until now: to begin with, a method for the evaluation of the relation between benefits and costs which needed to be in accordance with the legal requirements had to be developed, the outcome of which was the efficiency frontier approach. Subsequent health care reforms have led to changing objectives and strategies. Currently, price negotiations of newly launched drugs are based on an early benefit assessment of dossiers submitted by pharmaceutical manufacturers. Other reasons might be the presently very comfortable financial situation of the statutory health insurance system as well as a historically grown societal fear and discomfort towards what is perceived to be a rationing of medicinal products. For the time being, it remains open how long the German health care system can afford to continue neglecting the benefits of health economic evaluations for drug and non-drug interventions, and when it will be time to wake this sleeping beauty.

[Decisions of the Federal Joint Committee for rejecting work with "weak" or failing evidence]. / Der Umgang des Gemeinsamen Bundesausschusses mit "schwacher" oder fehlender Evidenz.

With the aim to study the level of evidence on which coverage decisions of the Federal Joint Committee (G-BA) were made and how the G-BA deals with missing or insufficient evidence, we analysed the final reports of coverage decisions after benefit assessments (outpatient care) between 1.1.1998 and 31.10.2008. A total of 36 decisions were analysed. 12 technologies were adopted for provision in outpatient care, 22 were excluded and two were suspended of assessment procedure until new evidence is provided. The G-BA decided in each case on the basis of the best available evidence. In the majority of negative decisions (16 of 22) no randomised controlled trials (RCTs) were available, the decision was therefore made on the basis of non-randomised studies. Even five of 12 positive decisions lacked evidence based on RCTs. In these cases the G-BA acknowledged that the evaluation of efficacy in randomised clinical trials was, due to the disease characteristic, not feasible. The G-BA uses different ways to deal with missing or insufficient evidence. It should be noted that the concept of missing evidence in the public is often diffuse and should be better defined.