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 and Marjolein van Ballegooijen
Additional contact information
Inge M. C. M. de Kok: Department of Public Health, Erasmus MC—University Medical Center, Rotterdam, Zuid-Holland, The Netherlands
Emily A. Burger: Department of Health Policy and Management, Harvard T. H. Chan School of Public Health, Boston, MA, USA
Steffie K. Naber: Department of Public Health, Erasmus MC—University Medical Center, Rotterdam, Zuid-Holland, The Netherlands
Karen Canfell: Cancer Research Division, Cancer Council NSW, Sydney, Australia
James Killen: Cancer Research Division, Cancer Council NSW, Sydney, Australia
Kate Simms: Cancer Research Division, Cancer Council NSW, Sydney, Australia
Shalini Kulasingam: School of Public Health, University of Minnesota, Minneapolis, MN, USA
Emily Groene: School of Public Health, University of Minnesota, Minneapolis, MN, USA
Stephen Sy: Department of Health Policy and Management, Harvard T. H. Chan School of Public Health, Boston, MA, USA
Jane J. Kim: Department of Health Policy and Management, Harvard T. H. Chan School of Public Health, Boston, MA, USA
Marjolein van Ballegooijen: Department of Public Health, Erasmus MC—University Medical Center, Rotterdam, Zuid-Holland, The Netherlands

Medical Decision Making, 2020, vol. 40, issue 4, 474-482

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.

Keywords: cervical cancer; comparative modeling; microsimulation modeling; screening (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://journals.sagepub.com/doi/10.1177/0272989X20924007 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:40:y:2020:i:4:p:474-482

DOI: 10.1177/0272989X20924007

Access Statistics for this article

More articles in Medical Decision Making
Bibliographic data for series maintained by SAGE Publications ().