Cost-effectiveness of primary HPV screening for cervical cancer in Germany—a decision analysis
Abstract
Objectives To systematically evaluate the long-term effectiveness and cost-effectiveness of HPV-based primary cervical cancer screening in the German health care context using a decision-analysis approach. Methods A Markov-model for HPV-infection and cervical cancer was developed for the German health care context, and applied to evaluate various screening strategies that differ by screening interval and test algorithms, including HPV-testing alone or in combination with cytology. German clinical, epidemiological, and economic data, and test accuracy data from international meta-analyses were used. Outcomes predicted included the reduction in cervical cancer cases and deaths, life expectancy and discounted incremental cost-effectiveness ratios (ICER). The analysis was performed from the perspective of the healthcare system adopting a 3% annual discount rate for costs and outcomes. Extensive sensitivity analyses were performed. Results HPV-based screening is more effective than cytology alone. It results in a 71-97% reduction in cervical cancer cases as compared to 53-93% for cytology alone. The ICER range from 2600 Euro/LYG (cytology, 5-year-interval) to 155,500 Euro/LYG (annual HPV-testing starting at age 30 years, cytology age 20-29 years). Annual cytology alone, the current recommended screening strategy in Germany, is dominated by HPV-strategies. Increasing the age at screening initiation from 20 to 25 years does not result in a relevant loss in effectiveness but results…
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The Markov model was based on previously published and validated models for cervical cancer.
“Based on the previously published and validated models for cervical cancer, we developed a markov model on MS Excel spread sheet, considering the natural history of HPV infection and cervical cancer (Fig 1). [12–15]”
From Cost effectiveness of strategies for cervical cancer prevention in India (2020) · cites this paper as [14]
These estimates have also been used to parameterize models to evaluate cervical cancer prevention strategies in Thailand, United Kingdom and Germany.
“These estimates have also been used to parameterize models to evaluate cervical cancer prevention strategies in Thailand, United Kingdom and Germany. [13–15]”
From Cost effectiveness of strategies for cervical cancer prevention in India (2020) · cites this paper as [14]
Cites
Myers, E. R., McCrory, D. C., Nanda, K., Bastian, L., & Matchar, D. B. (2000) · American Journal of Epidemiology · doi:10.1093/oxfordjournals.aje.a010166 · Cited by 470 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
State-Transition Modeling: A Report of the ISPOR-SMDM Modeling Good Research Practices Task Force-3
Siebert, U., Alagoz, O., Bayoumi, A. M., Jahn, B., Owens, D. K., Cohen, D. J., & Kuntz, K. M. (2012) · Value in Health · doi:10.1016/j.jval.2012.06.014 · Cited by 495 · see citing works
Self-citationState-Transition Modeling: A Report of the ISPOR-SMDM Modeling Good Research Practices Task Force–3
Siebert, U., Alagoz, O., Bayoumi, A. M., Jahn, B., Owens, D. K., Cohen, D. J., & Kuntz, K. M. (2012) · Medical Decision Making · doi:10.1177/0272989x12455463 · Cited by 348 · see citing works
Self-citationMcSherry, L. A., Dombrowski, S. U., Francis, J. J., Murphy, J., Martin, C. M., O’Leary, J. J., Sharp, L., & ATHENS Group (2012) · Implementation Science · doi:10.1186/1748-5908-7-73 · Cited by 129 · see citing works
Cost effectiveness of strategies for cervical cancer prevention in India
Chauhan, A. S., Prinja, S., Srinivasan, R., Rai, B., Malliga, J., Jyani, G., Gupta, N., & Ghoshal, S. (2020) · PLOS ONE · doi:10.1371/journal.pone.0238291 · Cited by 75 · see citing works
- Authors
- Sroczynski, G., Schnell-Inderst, P., Mühlberger, N., Lang, K., Aidelsburger, P., Wasem, J., Mittendorf, T., Engel, J., Hillemanns, P., Petry, K. U., Krämer, A., & Siebert, U.
- Affiliations
- UMIT TIROL - Private University for Health Sciences and Health Technology (AT)
- University of Duisburg-Essen (DE)
- Leibniz University Hannover (DE)
- Ludwig-Maximilians-Universität München (DE)
- Medizinische Hochschule Hannover (DE)
- Bielefeld University (DE)
- Harvard University (US)
- Corresponding author
- Siebert, U.
- ORCID
- Venue
- European Journal of Cancer
- Publisher
- Elsevier BV
- Published
- 2011-07
- Volume
- 47
- Issue
- 11
- Pages
- 1633-1646
- ISSN
- 0959-8049 (ISSN-L)
- OpenAlex
- W2124327706
- Cited by
- 40
- References
- 1
- Citations by year
- 2026: 1 · 2023: 2 · 2022: 2 · 2021: 2 · 2020: 5 · 2019: 3 · 2018: 4 · 2017: 4 · 2016: 2 · 2015: 4 · 2014: 5 · 2013: 3 · 2012: 3
- FWCI
- 2.65
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- European Journal of Cancer (publishedVersion)
- PubMed (publishedVersion)
- PUB – Publications at Bielefeld University (Bielefeld University) (submittedVersion)
- Publikationen an der Universität Bielefeld (Universität Bielefeld) (submittedVersion)
- Topics
- Health Sciences › Medicine › Epidemiology › Cervical Cancer and HPV Research
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Health Systems, Economic Evaluations, Quality of Life
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Women's cancer prevention and management
- Keywords
- HPV-based cervical cancer screening, cost-effectiveness, cervical cancer, HPV testing, cytology, screening frequency, age at screening initiation, incremental cost-effectiveness ratio, Markov model, Germany
- MeSH terms
- Adult, Aged, Aged, 80 and over, Uterine Cervical Neoplasms, Cost-Benefit Analysis, Decision Support Techniques, Female, Humans, Markov Chains, Mass Screening, Medical Oncology, Middle Aged, Sensitivity and Specificity, Prevalence, Outcome Assessment, Health Care, Early Detection of Cancer
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review