Public Preferences for Determining Eligibility for Screening in Risk-Stratified Cancer Screening Programs: A Discrete Choice Experiment

Rebecca A. Dennison, Lily C. Taylor, Stephen Morris, Rachel A. Boscott, Hannah Harrison, Sowmiya A. Moorthie, Sabrina H. Rossi, Grant D. Stewart and Juliet A. Usher-Smith
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Rebecca A. Dennison: Primary Care Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
Lily C. Taylor: Primary Care Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
Stephen Morris: Primary Care Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
Rachel A. Boscott: School of Clinical Medicine, University of Cambridge, Cambridge, UK
Hannah Harrison: Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
Sowmiya A. Moorthie: PHG Foundation, University of Cambridge, Cambridge, UK
Sabrina H. Rossi: Department of Surgery, University of Cambridge, Cambridge, UK
Grant D. Stewart: Department of Surgery, University of Cambridge, Cambridge, UK
Juliet A. Usher-Smith: Primary Care Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK

Medical Decision Making, 2023, vol. 43, issue 3, 374-386

Abstract: Background Risk stratification has been proposed to improve the efficiency of population-level cancer screening. We aimed to describe and quantify the relative importance of different attributes of potential screening programs among the public, focusing on stratifying eligibility. Methods We conducted a discrete choice experiment in which respondents selected between 2 hypothetical screening programs in a series of 9 questions. We presented the risk factors used to determine eligibility (age, sex, or lifestyle or genetic risk scores) and anticipated outcomes based on eligibility criteria with different sensitivity and specificity levels. We performed conditional logit regression models and used the results to estimate preferences for different approaches. We also analyzed free-text comments on respondents’ views on the programs. Results A total of 1,172 respondents completed the survey. Sensitivity was the most important attribute (7 and 11 times more important than specificity and risk factors, respectively). Eligibility criteria based on age and sex or genetics were preferred over age alone and lifestyle risk scores. Phenotypic and polygenic risk prediction models would be more acceptable than screening everyone aged 55 to 70 y if they had high discrimination (area under the receiver-operating characteristic curve ≥0.75 and 0.80, respectively). Limitations Although our sample was representative with respect to age, sex, and ethnicity, it may not be representative of the UK population regarding other important characteristics. Also, some respondents may have not understood all the information provided to inform decision making. Conclusions The public prioritized lives saved from cancer over reductions in numbers screened or experiencing unnecessary follow-up. Incorporating personal-level risk factors into screening eligibility criteria is acceptable to the public if it increases sensitivity; therefore, maximizing sensitivity in model development and communication could increase uptake. Highlights The public prioritized lives saved when considering changing from age-based eligibility criteria to risk-stratified cancer screening over reductions in numbers of people being screened or experiencing unnecessary follow-up. The risk stratification strategy used to do this was the least important component, although age plus sex or genetics were relatively preferable to using age alone and lifestyle risk scores. Communication strategies that emphasize improvements in the numbers of cancers detected or not missed across the population are more likely to be salient than reductions in unnecessary investigations or follow-up among some groups. Future research should focus on developing implementation strategies that maximize gains in sensitivity within the context of resource constraints and how to present attributes relating to specificity to facilitate understanding and informed decision making.

Keywords: discrete choice experiment; population survey; public acceptability; cancer screening; health policy; risk factors (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:43:y:2023:i:3:p:374-386

DOI: 10.1177/0272989X231155790

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