National Cancer System Characteristics and Global Pan-Cancer Outcomes
Notice bibliographique
Résumé
Importance: Approximately 29.9 million cancer cases and 15.3 million deaths are anticipated by 2040 globally, necessitating cancer system strengthening. A greater understanding of health system factors that can be leveraged to improve cancer control may guide health system planning. Objective: To evaluate predictors of improved cancer outcomes globally. Design, Setting, and Participants: This pan-cancer ecological study used the most recent available national health system metrics and cancer statistics, spanning the breadth of global income levels across 185 countries. Estimates of age-standardized mortality to incidence ratios were derived from GLOBOCAN 2022 for patients with cancer of all ages. The analysis took place on November 27, 2024. Main Outcomes and Measures: Health spending as a percent of gross domestic product (GDP), physicians per 1000 population, nurses and midwives per 1000 population, surgical workforce per 1000 population, GDP per capita, Universal Health Coverage (UHC) service coverage index, availability of pathology services, human development index, gender inequality index (GII), radiotherapy centers per 1000 population, and out-of-pocket expenditure as percentage of current health expenditure were collected. The association between the mortality to incidence ratio (MIR) and each metric was evaluated using univariable linear regressions (α = .0045), which were used to construct multivariable models (α = .05). Variation inflation factor allowed exclusion of variables with significant multicollinearity. R2 measured goodness of fit. Results: On univariable analysis, all metrics were significantly associated with MIR of cancer (P < .001 for all), including UHC index (β, -0.0076 [95% CI, -0.0083 to -0.0068]), GDP per capita (β, -5.10 × 10-6 [95% CI, -5.75 × 10-6 to -4.46 × 10-6]), clinical and workforce capacity, radiotherapy capacity (β, -88.25 [95% CI, -100.43 to -76.06]), and gender inequality index (β, 0.63 [95% CI, 0.57-0.70]). After including metrics significant on univariable analysis and correcting for multicollinearity, on multivariable analysis, greater UHC index and GDP per capita were independently associated with lower (improved) MIR for cancer. The multivariable model had R2 of 0.87. On multivariable analysis stratified by sex, greater UHC index and greater GDP per capita were independently associated with improved MIR for all cancers. R2 for the multivariable models was 0.87 for females and 0.85 for males. Conclusions: This study found that global health system metrics related to progress toward universal health care, greater health care spending and GDP per capita, strengthened clinical workforce and capacity, and increased gender equity were associated with improved pan-cancer outcomes at a population level on univariable analysis. The degree of UHC and GDP per capita were independently associated with improved cancer outcomes in multivariable models with good explanatory power. These exploratory findings merit further validation and may guide health system planning and prioritization.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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) | 0,001 | 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 ».