The Impact of Different Screening Model Structures on Cervical Cancer Incidence and Mortality Predictions: The Maximum Clinical Incidence Reduction (MCLIR) Methodology

Inge M.C.M. de Kok, Emily A. Burger, Steffie K. Naber, Karen Canfell, James Killen, Kate Simms, Shalini Kulasingam, Emily Groene, Stephen Sy, Jane J. Kim, Marjolein van Ballegooijen

Research output: Contribution to journalArticlepeer-review

Abstract

Background. To interpret cervical cancer screening model results, we need to understand the influence of model structure and assumptions on cancer incidence and mortality predictions. Cervical cancer cases and deaths following screening can be attributed to 1) (precancerous or cancerous) disease that occurred after screening, 2) disease that was present but not screen detected, or 3) disease that was screen detected but not successfully treated. We examined the relative contributions of each of these using 4 Cancer Intervention and Surveillance Modeling Network (CISNET) models. Methods. The maximum clinical incidence reduction (MCLIR) method compares changes in the number of clinically detected cervical cancers and mortality among 4 scenarios: 1) no screening, 2) one-time perfect screening at age 45 that detects all existing disease and delivers perfect (i.e., 100% effective) treatment of all screen-detected disease, 3) one-time realistic-sensitivity cytological screening and perfect treatment of all screen-detected disease, and 4) one-time realistic-sensitivity cytological screening and realistic-effectiveness treatment of all screen-detected disease. Results. Predicted incidence reductions ranged from 55% to 74%, and mortality reduction ranged from 56% to 62% within 15 years of follow-up for scenario 4 across models. The proportion of deaths due to disease not detected by screening differed across the models (21%–35%), as did the failure of treatment (8%–16%) and disease occurring after screening (from 1%–6%). Conclusions. The MCLIR approach aids in the interpretation of variability across model results. We showed that the reasons why screening failed to prevent cancers and deaths differed between the models. This likely reflects uncertainty about unobservable model inputs and structures; the impact of this uncertainty on policy conclusions should be examined via comparing findings from different well-calibrated and validated model platforms.

Original languageEnglish (US)
Pages (from-to)474-482
Number of pages9
JournalMedical Decision Making
Volume40
Issue number4
DOIs
StatePublished - May 1 2020

Bibliographical note

Funding Information:
KC is co–principal investigator of an unrelated investigator-initiated trial of cervical cytology and primary human papillomavirus screening in Australia (“Compass”), which is conducted and funded by the VCS Foundation, a government-funded health promotion charity. The VCS Foundation has received equipment and a funding contribution for the Compass trial from Roche Molecular Systems and Ventana, Inc. However, neither KC nor her institution on her behalf (Cancer Council NSW) receives direct funding from industry for this or any other project. The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Financial support for this study was provided entirely by a grant from the National Cancer Institute as part of the Cancer Intervention and Surveillance Modeling Network (CISNET), grant U01CA199334. The funding agreement ensured the authors’ independence in designing the study, interpreting the data, writing, and publishing the report. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the National Cancer Institute.

Keywords

  • cervical cancer
  • comparative modeling
  • microsimulation modeling
  • screening

PubMed: MeSH publication types

  • Journal Article

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