Why QALY Estimates Differ in Cervical Cancer Screening: An In-Depth Exploration Using CISNET Comparative Modeling

BACKGROUND: 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. METHODS: 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). RESULTS: 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. CONCLUSIONS: 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.

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Publication Details

Journal
Medical Decision Making
Published
2026-10-15
DOI
https://doi.org/10.1177/0272989x261489534
Primary Topic
Health Systems, Economic Evaluations, Quality of Life
Type
article
Field-Weighted Citation Impact
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article

Why QALY Estimates Differ in Cervical Cancer Screening: An In-Depth Exploration Using CISNET Comparative Modeling

Karen Canfell, Emily Annika Burger, Ran Zhao, James Killen et al.
Medical Decision Making
Health Systems, Economic Evaluations, Quality of Life
article

Why QALY Estimates Differ in Cervical Cancer Screening: An In-Depth Exploration Using CISNET Comparative Modeling

Karen Canfell, Emily Annika Burger, Ran Zhao, James Killen, Daniel D de Bondt, Stephen Sy, Shalini Kulasingam, Inge M.C.M. de Kok, Jane J. Kim, Emi Naslazi
article en

Abstract

BACKGROUND: 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. METHODS: 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). RESULTS: 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. CONCLUSIONS: 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.

Medical Decision Making
Tulane University (US), The University of Sydney (AU), University of Minnesota (US), Harvard University (US), University of Oslo (NO), Erasmus MC (NL)
Good health and well-being
Openalex Percentile: Top 8%
Health Systems, Economic Evaluations, Quality of Life
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