Treatment capacity required for full‐scale implementation of lung cancer screening in the United States
Notice bibliographique
Résumé
BACKGROUND: Full-scale implementation of lung cancer screening in the United States will increase detection of early stages. This study was aimed at assessing the capacity required for treating those cancers. METHODS: A well-established microsimulation model was extended with treatment data from the National Cancer Database. We assessed how treatment demand would change when implementing lung cancer screening in 2018. Three policies were assessed: 1) annual screening of current smokers and former smokers who quit fewer than 15 years ago, aged 55 to 80 years, with a smoking history of at least 30 pack-years (US Preventive Services Task Force [USPSTF] recommendations); 2) annual screening of current smokers and former smokers who quit fewer than 15 years ago, aged 55 to 77 years, with a smoking history of at least 30 pack-years (Centers for Medicare and Medicaid Services [CMS] recommendations); and 3) annual screening of current smokers and former smokers who quit fewer than 10 years ago, aged 55 to 75 years, with a smoking history of at least 40 pack-years (the most cost-effective policy in Ontario [Ontario]). The base-case screening adherence was a constant 50%. Sensitivity analyses assessed other adherence levels, including a linear buildup to 50% between 2018 and 2027. RESULTS: The USPSTF policy would require 37.0% more lung cancer surgeries in 2015-2040 than no screening, 2.2% less radiotherapy, and 5.4% less chemotherapy; 5.7% more patients would require any therapy. The increase in surgical demand would be 96.1% in 2018, 46.0% in 2023, 38.3% in 2028, and 24.9% in 2040. Adherence strongly influenced results. By 2018, surgical demand would range from 52,619 (20% adherence) to 96,121 (80%). With a gradual buildup of adherence, the increase in surgical demand would be 9.6% in 2018, 38.3% in 2023, 42.0% in 2028, and 24.4% in 2040. Results for the CMS and Ontario policies were similar, although the changes in comparison with no screening were smaller. CONCLUSIONS: Full-scale implementation of lung cancer screening causes a major increase in surgical demand, with a peak within the first 5 years. A gradual buildup of adherence can spread this peak over time. Careful surgical capacity planning is essential for successfully implementing screening.
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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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| 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 ».