Advancing Colorectal Cancer Detection With Blood-Based Tests: Qualitative Study and Discrete Choice Experiment to Elicit Population Preferences
Notice bibliographique
Résumé
BACKGROUND: Colorectal cancer (CRC) is the second most deadly form of cancer, inducing an estimated 1.9 million incidence cases and 0.9 million deaths worldwide in 2020. Despite the availability of screening tests, their uptake remains suboptimal. However, blood-based tests that look for signs of cancer-specific markers in the body are increasingly available as an alternative for more invasive tests for cancer. Compared with existing tests, the benefits of blood-based tests for CRC include not needing pretest preparation, stool handling, and dietary or medication restrictions. OBJECTIVE: This study aims to explore the population's preferences for CRC screening tests, with a focus on blood-based tests, and investigate the factors influencing test uptake. METHODS: We used a mixed methods approach, combining semistructured interviews and a discrete choice experiment (DCE) survey. Interviews were analyzed using thematic analysis to identify salient attributes for CRC screening tests. These attributes informed the design of the DCE survey. The DCE data were analyzed using mixed logit and mixed-mixed multinomial logit models. RESULTS: Qualitative findings from 30 participants revealed that participants preferred blood-based tests due to their perceived low risk, minimal pain, and ease of sample collection. However, concerns about the test's lower accuracy were also expressed. The DCE survey was completed by 1189 participants. In the mixed logit model, participants demonstrated a stronger preference for blood-based tests over a 2-day stool-based test. The mixed-mixed multinomial logit model identified 2 classes, strong supporters and weak supporters, for CRC screening. Weak supporters, but not strong supporters, had a higher preference for blood-based tests. Women, ethnic Chinese, and people aged 40 to 60 years were more likely to be weak supporters. Both models highlighted the high influence of cost and test sensitivity on participants' preferences. Transitioning from a 2-day stool-based test to a blood-based test, assuming a national screening program at a base price of Singapore $5 (US $3.75), was estimated to have the potential to increase the relative uptake by 5.9% (95% CI 3.6%-8.2%). CONCLUSIONS: These findings contribute to our understanding of CRC screening preferences and provide insights into the factors driving test uptake. This study highlights the perceived advantages of blood-based tests and identifies areas of concern regarding their accuracy. Further research is needed to determine the actual increase in uptake rate when blood-based tests are made available.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».