Evidence map›Paper›PMID 36786399›Full record

ArticleMedical decision making : an international journal of the Society for Medical Decision Making2023

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, Juliet A Usher-Smith

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Article in Medical decision making : an international journal of the Society for Medical Decision Making, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Rebecca A DennisonPrimary Care Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.ORCID 0000-0002-0847-0723
Lily C TaylorPrimary Care Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
Stephen MorrisPrimary Care Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
Rachel A BoscottSchool of Clinical Medicine, University of Cambridge, Cambridge, UK.
Hannah HarrisonCentre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
Sowmiya A MoorthiePHG Foundation, University of Cambridge, Cambridge, UK.
Sabrina H RossiDepartment of Surgery, University of Cambridge, Cambridge, UK.
Grant D StewartDepartment of Surgery, University of Cambridge, Cambridge, UK.
Juliet A Usher-SmithPrimary Care Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.ORCID 0000-0002-8501-2531

Funding

Cancer Research UK C9685/A25117Department of Health NIHR300861
6 · The paper itself

Abstract

backgroundRisk 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.

methodsWe 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.

resultsA 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.

conclusionsThe 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.

Indexed as

Choice BehaviorNeoplasmsEarly Detection of CancerHumansLogistic ModelsRisk FactorsSensitivity and SpecificitySurveys and Questionnairescancer screeningdiscrete choice experimenthealth policypopulation surveypublic acceptabilityrisk factors

Identifiers

PMID36786399
PMCPMC10021112

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LicenceCC BY
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.