Public Preference Heterogeneity and Predicted Uptake Rate of Upper Gastrointestinal Cancer Screening Programs in Rural China: Discrete Choice Experiments and Latent Class Analysis
Notice bibliographique
Résumé
BACKGROUND: Rapid increases in the morbidity and mortality of patients with upper gastrointestinal cancer (UGC) in high-incidence countries in Asia have raised public health concerns. Screening can effectively reduce the incidence and mortality of patients with UGC, but the low population uptake rate seriously affects the screening effect. OBJECTIVE: We aimed to determine the characteristics that influence residents' preference heterogeneity for a UGC-screening program and the extent to which these characteristics predict residents' uptake rates. METHODS: A discrete choice experiment was conducted in 1000 residents aged 40-69 years who were randomly selected from 3 counties (Feicheng, Linqu, and Dongchangfu) in Shandong Province, China. Each respondent was repeatedly asked to choose from 9 discrete choice questions of 2 hypothetical screening programs comprising 5 attributes: screening interval, screening technique, regular follow-up for precancerous lesions, mortality reduction, and out-of-pocket costs. The latent class logit model was used to estimate residents' preference heterogeneity for each attribute level, their willingness to pay, and the expected uptake rates. RESULTS: Of the 1000 residents invited, 926 (92.6%) were included in the final analyses. The mean age was 57.32 (SD 7.22) years. The best model contained 4 classes of respondents (Akaike information criterion=7140.989, Bayesian information criterion=7485.373) defined by different preferences for the 5 attributes. In the 4-class model, out of 926 residents, 88 (9.5%) were assigned to class 1, named as the negative latent type; 216 (3.3%) were assigned to class 2, named as the positive integrated type; 434 (46.9%) were assigned to class 3, named as the positive comfortable type; and 188 (20.3%) were assigned to class 4, named as the neutral quality type. For these 4 latent classes, "out-of-pocket cost" is the most preferred attribute in negative latent type and positive integrated type residents (45.04% vs 66.04% importance weights), whereas "screening technique" is the most preferred factor in positive comfortable type residents (62.56% importance weight) and "screening interval" is the most valued attribute in neutral quality type residents (47.05% importance weight). Besides, residents in different classes had common preference for painless endoscopy, and their willingness to pay were CNY ¥385.369 (US $59.747), CNY ¥93.44 (US $14.486), CNY ¥1946.48 (US $301.810), and CNY ¥3566.60 (US $552.961), respectively. Residents' participation rate could increase by more than 89% (except for the 60.98% in class 2) if the optimal UGC screening option with free, follow-up for precancerous lesions, 45% mortality reduction, screening every year, and painless endoscopy was implemented. CONCLUSIONS: Public preference heterogeneity for UGC screening does exist. Most residents have a positive attitude toward UGC screening, but their preferences vary in selected attributes and levels, except for painless endoscopy. Policy makers should consider these heterogeneities to formulate UGC-screening programs that incorporate the public's needs and preferences to improve participation rates.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,048 | 0,044 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,003 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 source (Gemma direct ou Codex distillé), 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 ».