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Background <p>Breast cancer screening is vital for early detection and improved health outcomes but requires robust health economic evaluations to guide implementation. This systematic review examines the methodology, quality, and results of health economic evaluations of screening strategies to support decision making and future research.</p> Methods <p>A literature search was performed in PubMed, Embase, Web of Science, EconLit, and the HTA database. Health economic evaluations of breast cancer screening strategies using imaging modalities were included and screened independently by two reviewers. Data on study design, screening strategies, and outcomes were extracted and synthesized. Quality was assessed using the ISPOR checklist for model-based studies and the Consensus on Health Economics criteria (CHEC-extended) checklist for empirical data–based studies. All results were made publicly accessible via an interactive platform and the Open Science Framework, providing an open resource that facilitates transparency, reuse, and future updates.</p> Results <p>The review included 128 studies, comprising 96 model-based studies, 14 empirical data-based studies, 15 studies combining empirical data with extrapolation using a modeling approach, and 3 studies with unclear methods. Microsimulation and cohort simulation were used in 47 and 53 studies, respectively. Incremental cost-effectiveness ratios varied widely across studies depending on the screening modality, risk factors, age range of screening, and screening interval. Most studies found mammography to be cost effective compared with no screening, while some studies showed it as being not cost effective, especially for women at average risk of breast cancer, in young screening ages (40–49&#xa0;years), or with an annual interval. Ultrasound-based screening programs were generally cost effective compared with no screening in the women with average risk of breast cancer. Supplementing magnetic resonance imaging (MRI) with mammography was generally cost effective in women with dense breasts and a family history of breast or ovarian cancer but not cost effective in women with previous treatment using radiation therapy. The median quality score was 55% for model-based studies, with microsimulations having higher quality than cohort simulations. Empirical data–based studies with and without extrapolation had a similar median quality score of 65%.</p> Conclusions <p>Mammography was generally reported as cost effective compared with no screening. Supplementing mammography with ultrasound and/or MRI could be cost effective depending on comparator, risk factors, age range, and screening interval. Suboptimal quality was commonly observed across published health economic evaluations. Future studies should prioritize enhancing overall quality, particularly in data, validation, and reporting.</p>

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Health Economic Evaluations of Breast Cancer Screening Strategies: A Systematic Review Focusing on Methodology and Quality Assessment

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月29日

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  • Thi Hao Pham,
  • Susan de Braak,
  • Sjoukje van der Werf,
  • Sudewi Mukaromah Khoirunnisa,
  • Xuanqi Pan,
  • Andre Pfob,
  • Abrham Wondimu,
  • Jurjen van der Schans,
  • Maarten J. Postma,
  • Antoinette D. I. van Asselt,
  • Lisa A. de Jong

摘要

Background

Breast cancer screening is vital for early detection and improved health outcomes but requires robust health economic evaluations to guide implementation. This systematic review examines the methodology, quality, and results of health economic evaluations of screening strategies to support decision making and future research.

Methods

A literature search was performed in PubMed, Embase, Web of Science, EconLit, and the HTA database. Health economic evaluations of breast cancer screening strategies using imaging modalities were included and screened independently by two reviewers. Data on study design, screening strategies, and outcomes were extracted and synthesized. Quality was assessed using the ISPOR checklist for model-based studies and the Consensus on Health Economics criteria (CHEC-extended) checklist for empirical data–based studies. All results were made publicly accessible via an interactive platform and the Open Science Framework, providing an open resource that facilitates transparency, reuse, and future updates.

Results

The review included 128 studies, comprising 96 model-based studies, 14 empirical data-based studies, 15 studies combining empirical data with extrapolation using a modeling approach, and 3 studies with unclear methods. Microsimulation and cohort simulation were used in 47 and 53 studies, respectively. Incremental cost-effectiveness ratios varied widely across studies depending on the screening modality, risk factors, age range of screening, and screening interval. Most studies found mammography to be cost effective compared with no screening, while some studies showed it as being not cost effective, especially for women at average risk of breast cancer, in young screening ages (40–49 years), or with an annual interval. Ultrasound-based screening programs were generally cost effective compared with no screening in the women with average risk of breast cancer. Supplementing magnetic resonance imaging (MRI) with mammography was generally cost effective in women with dense breasts and a family history of breast or ovarian cancer but not cost effective in women with previous treatment using radiation therapy. The median quality score was 55% for model-based studies, with microsimulations having higher quality than cohort simulations. Empirical data–based studies with and without extrapolation had a similar median quality score of 65%.

Conclusions

Mammography was generally reported as cost effective compared with no screening. Supplementing mammography with ultrasound and/or MRI could be cost effective depending on comparator, risk factors, age range, and screening interval. Suboptimal quality was commonly observed across published health economic evaluations. Future studies should prioritize enhancing overall quality, particularly in data, validation, and reporting.