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46 Measuring prevalence and trends in low-value care using German claims data – first results of the IndiQ-project
  1. Meik Hildebrandt1,
  2. Hanna Ermann1,
  3. Lotte Dammertz2,
  4. Monika Nothacker3,
  5. Udo Schneider4,
  6. Enno Swart5,
  7. Peter Ihle6,
  8. Reinhard Busse1,
  9. Verena Vogt1
  1. 1Department of Health Care Management, Technical University Berlin, Berlin, Germany
  2. 2Department of Epidemiology and Health Care Atlas, Central Research Institute for Ambulatory Health Care in the Federal Republic of Germany (Zi), Berlin, Germany
  3. 3Institute for Medical Knowledge Management (IMWi), Association of the Scientific Medical Societies in Germany (AWMF), Berlin, Germany
  4. 4Techniker Krankenkasse (TK, Health insurance company), Hamburg, Germany
  5. 5Institute of Social Medicine and Health Systems Research (ISMHSR), Otto von Guericke University, Magdeburg, Germany
  6. 6PMV research group, Cologne, Germany


Objectives Medical services whose benefits do not outweigh the costs and potential harm are labelled as low-value care. Reducing low-value care, either by reducing the number of diagnoses leading to the medical service or by reducing the number of prescriptions, shows a growing significance in health economic research.

The goal of the IndiQ-Project is to identify inappropriate health services and to quantify the prevalence and trends using German claims data. The results will then be used to develop best practice strategies in the form of recommendations for interventions that can contribute to decreasing the overall extent of low-value care.

Methods Low-value care arises in combination with certain diagnoses. The cross-section of a medical service, associated diagnoses and further restrictions (i.e. age, gender), provides indicators describing low-value care. The indicators in the IndiQ-project result from a systematic review, followed by a measurability assessment that considered the restrictions of German claims data (e.g., the missing encoding of symptoms and disease severity). Last, the indicators were validated by medical experts in a two-step panel according to the Delphi method.

Each indicator consists of a numerator representing the absolute number of low-value care services. Through corresponding denominators, considering either the affected population or the set of services, the absolute values are put into proportion. Where possible, a sensitive and specific numerator is used, aiming to represent the continuous interval of low-value care. The indicators will be measured using claims data provided by a large German statutory health insurance fund (Techniker Krankenkasse) from 2018 to 2021.

Results Upon finishing the identification process, 36 indicators were selected to be measured in German claims data. Both, the absolute frequency, and the prevalence of low-value care differs significantly between the indicators. Overall, the absolute number of low-value care cases in the sensitive setting adds up to over seven million, reduced to still over 3 million in the specific setting.

Many of the indicators as well as the cumulated case numbers exhibit a decreasing trend of low-value care (i.e. electrotherapy for pressure ulcers, opioid analgesics for non-specific back pain). Additionally, some indicators show patterns of change with the onset of the Covid-19 pandemic in 2020. For example, antibiotic prescriptions for uncomplicated respiratory infections have declined significantly since the beginning of the Covid-19 pandemic.

Conclusion The great importance of low-value care is confirmed by the measured indicators. However, the selected set of indicators does neither represent the whole set of low-value care services, nor is it aimed at identifying individual cases that might be inappropriate. Hence only a share of low-value care is displayed.

Nonetheless, the results attained thus far serve as a foundation for further research. We will examine regional variations and sectoral differences in our indicators to identify potential determinants of low-value care. Finally, the results will be used for developing intervention recommendations, categorized by the most promising ways regarding the expected effort and use.

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