ArticleCancer causes & control : CCC2026
Predictors of perceived value of multi-cancer early detection testing: a national PRECEDE-PROCEED framework study.
Article in Cancer causes & control : CCC, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMulti-cancer early detection (MCED) tests present a paradigm transform in cancer screening by detecting multiple cancer types from one blood sample. While studies have shown high levels of public interest in MCED screening, little is known about factors shaping individuals' perception of the emerging technology. Guided by the PRECEDE-PROCEED framework, we aimed to identify factors associated with the perception of value of MCED testing among American adults.
methodsWe conducted a cross-sectional secondary analysis of the Health Information National Trends Survey cycle 7 (HINTS 7) to identify factors associated with the perceived value of MCED screening. After removing respondents with missing outcome data, our final sample size was 6,728. Using the PRECEDE-PROCEED framework, candidate predictors were grouped into predisposing, reinforcing, and enabling factors. We used descriptive, survey-weighted analyses, spearman correlation analyses, survey-weighted multivariable logistic regression, and supervised machine learning analyses (logistic regression and random forest) to explore the associations.
resultsOverall, 74.9% of the sample perceived MCED testing as very valuable. Trust-related domains showed the strongest associations with the outcome, including trust in physicians (OR = 1.89), trust in science (OR = 1.80), and trust in the healthcare system (OR = 1.64). Cancer information seeking (OR = 1.59), online health information use (OR = 1.63), smartphone access (OR = 1.54), cancer fatalistic beliefs (OR = 1.52), trust in government (OR = 1.50), knowledge of hepatitis B as a cancer risk factor (OR = 1.48), and family history of cancer (OR = 1.45) were also significantly associated with higher perceived value. Machine learning analyses demonstrated consistent predictor rankings, highlighting trust and health information engagement as the most influential determinants.
conclusionsThe value of MCED screening was largely driven by trust, cancer information seeking, and access to healthcare and health information resources. Our study highlights the efficacy of the PRECEDE-PROCEED approach to studying factors associated with complex health outcomes. Future implementation strategies should prioritize equitable communication, strengthen public trust, and improve access to reliable health information to facilitate informed decision-making regarding MCED testing.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.