{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,18]],"date-time":"2026-10-18T18:41:04Z","timestamp":1792348864226,"version":"4.4.0"},"reference-count":16,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T00:00:00Z","timestamp":1792022400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T00:00:00Z","timestamp":1792022400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"name":"National Institutes of Health\/National Cancer Institute","award":["grant number U01CA253912"],"award-info":[{"award-number":["grant number U01CA253912"]}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Med Decis Making"],"abstract":"<jats:sec>\n                    <jats:title>Background:<\/jats:title>\n                    <jats:p>Quality-adjusted life-years (QALYs) are commonly used as a measure for evaluating the value of cancer screening. However, it is often unclear what drives model-based QALY estimates. We examine factors influencing QALY estimates for cervical cancer screening using 4 independent Cancer Intervention and Surveillance Modeling Network (CISNET) models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>We calculated QALYs gained for 5 recommended cervical screening strategies in the United States (varying in test used, start age, interval, and follow-up of screen positives) and compared with no screening. Each model used common US data, standardized inputs for health-related quality of life (HRQoL) weights, and duration of HRQoL losses. We calculated the percentage change in QALYs gained between screening strategies within each model, comparing the least to the most effective screening strategies. Furthermore, we isolated the impact on the total QALYs gained per stage in the screening process (primary tests, repeat tests, colposcopies, and cancer diagnoses).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>The percentage change in QALYs gained between screening strategies within each model ranged from 47% to 120%. In contrast, life-years gained (LYG) ranged from 1% to 12%. The largest variation in QALY gains was driven by HRQoL assumptions for repeat tests. Sensitivity analyses showed that QALY gains are highly sensitive to the duration of HRQoL losses due to repeat tests and discounting.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions:<\/jats:title>\n                    <jats:p>Despite standardizing disease burden and key inputs, QALYs remained highly sensitive to individual model assumptions. Comparative modelling and providing additional disaggregated outcomes improve transparency, enabling decision makers to more confidently interpret and use model-based evaluations of screening strategies.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/0272989x261489534","type":"journal-article","created":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T11:30:26Z","timestamp":1792063826000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Why QALY Estimates Differ in Cervical Cancer Screening: An In-Depth Exploration Using CISNET Comparative Modeling"],"prefix":"10.1177","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9419-0452","authenticated-orcid":false,"given":"Inge M. C. M.","family":"de Kok","sequence":"first","affiliation":[{"name":"Erasmus MC University Medical Centre, Department of Public Health, Rotterdam, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-1522-3489","authenticated-orcid":false,"given":"Daniel D.","family":"de Bondt","sequence":"additional","affiliation":[{"name":"Erasmus MC University Medical Centre, Department of Public Health, Rotterdam, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emi","family":"Naslazi","sequence":"additional","affiliation":[{"name":"Erasmus MC University Medical Centre, Department of Public Health, Rotterdam, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"James","family":"Killen","sequence":"additional","affiliation":[{"name":"School of Public Health, University of Sydney, Sydney, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8090-3768","authenticated-orcid":false,"given":"Stephen","family":"Sy","sequence":"additional","affiliation":[{"name":"Center for Health Decision Science, Department of Health Policy and Management, Harvard T.H. Chan School of Public Health, Boston, MA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9539-8274","authenticated-orcid":false,"given":"Ran","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Public Health, University of Minnesota, Twin Cities, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Karen","family":"Canfell","sequence":"additional","affiliation":[{"name":"School of Public Health, University of Sydney, Sydney, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9892-9170","authenticated-orcid":false,"given":"Jane J.","family":"Kim","sequence":"additional","affiliation":[{"name":"Center for Health Decision Science, Department of Health Policy and Management, Harvard T.H. Chan School of Public Health, Boston, MA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shalini","family":"Kulasingam","sequence":"additional","affiliation":[{"name":"School of Public Health, University of Minnesota, Twin Cities, USA"},{"name":"Celia Scott Weatherhead School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6657-5849","authenticated-orcid":false,"given":"Emily A.","family":"Burger","sequence":"additional","affiliation":[{"name":"Center for Health Decision Science, Department of Health Policy and Management, Harvard T.H. Chan School of Public Health, Boston, MA"},{"name":"Department of Health Management and Health Economics, University of Oslo, Oslo, Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,10,15]]},"reference":[{"key":"e_1_3_5_2_2","doi-asserted-by":"crossref","unstructured":"Weinstein MC Torrance G McGuire A. QALYs: the basics. Value Health. 2009;12(Suppl 1):S5\u2013S9. https:\/\/doi.org\/10.1111\/j.1524-4733.2009.00515.x","DOI":"10.1111\/j.1524-4733.2009.00515.x"},{"key":"e_1_3_5_3_2","unstructured":"NICE. NICE health technology evaluations: the manual 2022. 2025 May 13 [accessed 2025 July 9]. https:\/\/www.nice.org.uk\/process\/pmg36\/resources\/nice-health-technology-evaluations-the-manual-pdf-72286779244741"},{"key":"e_1_3_5_4_2","unstructured":"National Cancer Institute. The cancer intervention and surveillance modeling network. [accessed 2025 July 9]. https:\/\/cisnet.cancer.gov\/"},{"key":"e_1_3_5_5_2","doi-asserted-by":"crossref","unstructured":"Weinstein MC et al. Principles of good practice for decision analytic modeling in health-care evaluation: report of the ISPOR Task Force on Good Research Practices\u2014Modeling Studies. Value Health. 2003;6(1):9\u201317. https:\/\/doi.org\/10.1046\/j.1524-4733.2003.00234.x","DOI":"10.1046\/j.1524-4733.2003.00234.x"},{"key":"e_1_3_5_6_2","doi-asserted-by":"crossref","unstructured":"de Kok I et al. The impact of different screening model structures on cervical cancer incidence and mortality predictions: the Maximum Clinical Incidence Reduction (MCLIR) methodology. Med Decis Making. 2020;40(4):474\u2013482. https:\/\/doi.org\/10.1177\/0272989X20924007","DOI":"10.1177\/0272989X20924007"},{"key":"e_1_3_5_7_2","doi-asserted-by":"crossref","unstructured":"Burger EA et al. Estimating the natural history of cervical carcinogenesis using simulation models: a CISNET comparative analysis. J Natl Cancer Inst. 2020;112(9):955\u2013963. https:\/\/doi.org\/10.1093\/jnci\/djz227","DOI":"10.1093\/jnci\/djz227"},{"key":"e_1_3_5_8_2","doi-asserted-by":"crossref","unstructured":"de Kok I et al. Quality of life assumptions determine which cervical cancer screening strategies are cost-effective. Int J Cancer. 2018;142(11):2383\u20132393. https:\/\/doi.org\/10.1002\/ijc.31265","DOI":"10.1002\/ijc.31265"},{"key":"e_1_3_5_9_2","doi-asserted-by":"crossref","unstructured":"Kulasingam SL et al. Estimated cancer risk in females who meet the criteria to exit cervical cancer screening. JAMA Netw Open. 2025;8(3):e250479. https:\/\/doi.org\/10.1001\/jamanetworkopen.2025.0479","DOI":"10.1001\/jamanetworkopen.2025.0479"},{"key":"e_1_3_5_10_2","doi-asserted-by":"crossref","unstructured":"Burger EA et al. 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