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Poster display session: Basic science, Endocrine tumours, Gastrointestinal tumours - colorectal & non-colorectal, Head and neck cancer (excluding thyroid), Melanoma and other skin tumours, Neuroendocrine tumours, Thyroid cancer, Tumour biology & pathology

4921 - Measuring the Efficiency of Cancer Care in Europe

Date

21 Oct 2018

Session

Poster display session: Basic science, Endocrine tumours, Gastrointestinal tumours - colorectal & non-colorectal, Head and neck cancer (excluding thyroid), Melanoma and other skin tumours, Neuroendocrine tumours, Thyroid cancer, Tumour biology & pathology

Topics

Cancer Prevention;  Cancer Biology

Tumour Site

Presenters

Rikard Althin

Citation

Annals of Oncology (2018) 29 (suppl_8): viii1-viii13. 10.1093/annonc/mdy268

Authors

R. Althin1, R. Färe2, S. Grosskopf2, B. Jönsson3, N. Wilking4

Author affiliations

  • 1 Research, The Swedish Institute for Health Economics, 22002 - Lund/SE
  • 2 Economics, Oregon State, 97331 - Corvallis/US
  • 3 Economics, Stockholm school of economics, 11383 - Stockholm/SE
  • 4 Oncology, Skane University Hospital, 221 85 - Lund/SE
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Resources

Abstract 4921

Background

The rationale of this study is to develop the use of Data Envelopment Analysis (DEA) to measure and compare the efficiency of breast and lung cancer care in Europe in order to inform related policy discussions. In the wake of the increasing prevalence of cancer and pressures on constrained healthcare budgets, understanding how to make the most of available resources is essential to sustainable cancer care. DEA is a well-established instrument capable of identifying best practice in a complex production process such as cancer care with rapid change in technologies. DEA could be used to compare different production units such as e.g., countries, regions, or hospitals. In this study, we use real life data to evaluate the country specific performance of cancer care in Europe.

Methods

DEA is capable of handling many inputs and many outputs simultaneously to estimate the best practice of cancer care. The method is independent of unit of measurement allowing for the use of input and output quantities measured in different units. No data on prices are needed. For this application publicly available, aggregate, retrospective, and comparable data on breast cancer (BC) and lung cancer (LC) from Eurostat, WHO, and OECD was used in the analysis. In the model input variables such as number of radiation units, number of oncologists, and oncology drugs was used to produce survival and quality of life.

Results

The data displayed large differences in both inputs and outputs between countries and over time (2001-2015) and this was reflected in the performance measures. The efficiency base case in 2015 in BC identified 6 efficient countries out of the 23 included with a mean inefficiency of 80% and a minimum of 0.49%, i.e. the same outcome could have been produced with 49% of the inputs used. In the 2015 LC base case there were 8 efficient countries with a mean inefficiency of 82% (minimum 0.51%).

Conclusions

DEA is a policy relevant approach to measure and improve cancer care efficiency in Europe in order to provide information for decisions aimed at reducing waste and ensure better outcomes for patients. The research highlights key inefficiencies and opportunities to improve resource allocation in European cancer care.

Clinical trial identification

Legal entity responsible for the study

The Swedish Institute for Health Economics.

Funding

Bristol-Myers Squibb.

Editorial Acknowledgement

Disclosure

All authors have declared no conflicts of interest.

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