Chapter 19: Cost-effectiveness of cervical cancer screening
Abstract
In the last two decades, computer-based models of cervical cancer screening have been used to evaluate the cost-effectiveness of different secondary prevention policies. Analyses in countries with existing screening programs have focused on identifying the optimal screening interval, ages for starting and stopping screening, and consideration of enhancements to conventional cytology, such as human papillomavirus (HPV)-DNA testing as a triage for equivocal results or as a primary screening test for women over the age of 30. Analyses in resource-poor settings with infrequent or no screening have focused on strategies that enhance the linkage between screening and treatment, consider noncytologic alternatives such as HPV-DNA testing, and target women between the ages of 35 and 45 for screening one, two, or three times per lifetime. Despite differences in methods and assumptions, this paper identifies the qualitative themes that are consistent among studies, and highlights important methodological challenges and high-priority areas for further work.
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?
Because capturing screening history in Markov states can lead to state explosion, it may be necessary to use an individual-level STM with screening history included as tracker variables.
“As capturing screening history in the Markov states can lead to state explosion, it may be necessary to use an individual-level STM, with screening history included as tracker variables [22].”
From State-Transition Modeling: A Report of the ISPOR-SMDM Modeling Good Research Practices Task Force-3 (2012) · cites this paper as [22]
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
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) · Medical Decision Making · doi:10.1177/0272989x12455463 · Cited by 348 · see citing works
- Authors
- Goldie, S. J., Kim, J. J., & Myers, E.
- Affiliations
- Harvard University (US)
- Duke University (US)
- Corresponding author
- Goldie, Sue J.
- Venue
- Vaccine
- Publisher
- Elsevier BV
- Published
- 2006-08
- Volume
- 24
- Pages
- S164-S170
- ISSN
- 0264-410X (ISSN-L)
- OpenAlex
- W2057061243
- Cited by
- 84
- References
- 0
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 1 · 2023: 1 · 2021: 2 · 2020: 1 · 2018: 2 · 2017: 3 · 2016: 3 · 2015: 3 · 2014: 7 · 2012: 8
- FWCI
- 7.81
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Vaccine (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Cancer Institute (R01CA093435)
- Topics
- Health Sciences › Medicine › Epidemiology › Cervical Cancer and HPV Research
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Women's cancer prevention and management
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Health Systems, Economic Evaluations, Quality of Life
- Keywords
- cervical cancer screening, cost-effectiveness, secondary prevention, screening frequency, HPV testing, resource-limited settings
- MeSH terms
- Adult, Uterine Cervical Neoplasms, Cost-Benefit Analysis, Female, Humans, Mass Screening, Middle Aged, Papillomavirus Infections
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