Cost effectiveness of breast cancer screening and prevention: a systematic review with a focus on risk-adapted strategies.

Breast cancer prevention Breast cancer screening Cost effectiveness Decision analysis Overdiagnosis Risk stratification

Journal

The European journal of health economics : HEPAC : health economics in prevention and care
ISSN: 1618-7601
Titre abrégé: Eur J Health Econ
Pays: Germany
ID NLM: 101134867

Informations de publication

Date de publication:
Nov 2021
Historique:
received: 02 06 2020
accepted: 10 06 2021
pubmed: 4 8 2021
medline: 25 2 2023
entrez: 3 8 2021
Statut: ppublish

Résumé

Benefit and cost effectiveness of breast cancer screening are still matters of controversy. Risk-adapted strategies are proposed to improve its benefit-harm and cost-benefit relations. Our objective was to perform a systematic review on economic breast cancer models evaluating primary and secondary prevention strategies in the European health care setting, with specific focus on model results, model characteristics, and risk-adapted strategies. Literature databases were systematically searched for economic breast cancer models evaluating the cost effectiveness of breast cancer screening and prevention strategies in the European health care context. Characteristics, methodological details and results of the identified studies are reported in evidence tables. Economic model outputs are standardized to achieve comparable cost-effectiveness ratios. Thirty-two economic evaluations of breast cancer screening and seven evaluations of primary breast cancer prevention were included. Five screening studies and none of the prevention studies considered risk-adapted strategies. Studies differed in methodologic features. Only about half of the screening studies modeled overdiagnosis-related harms, most often indirectly and without reporting their magnitude. All models predict gains in life expectancy and/or quality-adjusted life expectancy at acceptable costs. However, risk-adapted screening was shown to be more effective and efficient than conventional screening. Economic models suggest that breast cancer screening and prevention are cost effective in the European setting. All screening models predict gains in life expectancy, which has not yet been confirmed by trials. European models evaluating risk-adapted screening strategies are rare, but suggest that risk-adapted screening is more effective and efficient than conventional screening.

Identifiants

pubmed: 34342797
doi: 10.1007/s10198-021-01338-5
pii: 10.1007/s10198-021-01338-5
doi:

Types de publication

Journal Article Systematic Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

1311-1344

Subventions

Organisme : Horizon 2020 Research and Innovation Programme
ID : 634570

Informations de copyright

© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

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Auteurs

Nikolai Mühlberger (N)

Institute of Public Health, Medical Decision Making and Health Technology Assessment, Department of Public Health, Health Services Research and Health Technology Assessment, UMIT-University for Health Sciences, Medical Informatics and Technology, Eduard-Wallnoefer-Zentrum I, 6060, Hall i.T, Austria.

Gaby Sroczynski (G)

Institute of Public Health, Medical Decision Making and Health Technology Assessment, Department of Public Health, Health Services Research and Health Technology Assessment, UMIT-University for Health Sciences, Medical Informatics and Technology, Eduard-Wallnoefer-Zentrum I, 6060, Hall i.T, Austria.

Artemisa Gogollari (A)

Institute of Public Health, Medical Decision Making and Health Technology Assessment, Department of Public Health, Health Services Research and Health Technology Assessment, UMIT-University for Health Sciences, Medical Informatics and Technology, Eduard-Wallnoefer-Zentrum I, 6060, Hall i.T, Austria.

Beate Jahn (B)

Institute of Public Health, Medical Decision Making and Health Technology Assessment, Department of Public Health, Health Services Research and Health Technology Assessment, UMIT-University for Health Sciences, Medical Informatics and Technology, Eduard-Wallnoefer-Zentrum I, 6060, Hall i.T, Austria.

Nora Pashayan (N)

Institute of Epidemiology and Healthcare, Department of Applied Health Research, UCL-University College London, 1-19 Torrington Place, London, WC1E 7HB, UK.

Ewout Steyerberg (E)

Department of Public Health, Erasmus MC, PO Box 9600, 3000 CA, Rotterdam, The Netherlands.
Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands.

Martin Widschwendter (M)

Department of Women's Cancer, EGA Institute for Women's Health, UCL - University College London, 74 Huntley St, Rm 340, London, WC1E 6AU, UK.

Uwe Siebert (U)

Institute of Public Health, Medical Decision Making and Health Technology Assessment, Department of Public Health, Health Services Research and Health Technology Assessment, UMIT-University for Health Sciences, Medical Informatics and Technology, Eduard-Wallnoefer-Zentrum I, 6060, Hall i.T, Austria. public-health@umit.at.
Division of Health Technology Assessment and Bioinformatics, ONCOTYROL - Center for Personalized Cancer Medicine, Innsbruck, Austria. public-health@umit.at.
Department of Health Policy and Management, Harvard T.H. Chan School of Public Health, Center for Health Decision Science, Boston, MA, USA. public-health@umit.at.
Harvard Medical School, Institute for Technology Assessment and Department of Radiology, Massachusetts General Hospital, Boston, MA, USA. public-health@umit.at.

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