National cancer system characteristics and global pan-cancer outcomes.
Bibliographic record
Abstract
1625 Background: Approximately 29.9 million cancer cases and 15.3 million deaths are anticipated by 2040 globally. Health systems must invest in cancer system strengthening. A greater understanding of health system factors that can be leveraged to improve cancer control may guide health system planning. Therefore, we conducted a pan-cancer ecological study making use of most recent available national health system metrics for cancer outcomes and health system metrics, spanning the breadth of global income levels across 185 countries. Methods: Estimates of age-standardized mortality-to-incidence ratios were derived from GLOBOCAN 2022 for patients with cancer of all ages. Health spending (% of gross domestic product [GDP]), physicians/1000population, nurses and midwives/1000population, surgical workforce/1000population, GDP per capita, Universal Health Coverage Service Coverage Index (UHC index), availability of pathology services, human development index, gender inequality index, radiotherapy centers/1000population, and out-of-pocket expenditure as percentage of current health expenditure were collected. The association between MIR and each metric was evaluated using univariable linear regressions. Metrics with P < 0.0045 (Bonferroni corrected) were included in multivariable models. Variation inflation factor allowed exclusion of variables with significant multicollinearity. R2 defined goodness of fit. Results: On univariable analysis, all metrics were significantly associated with MIR of cancer (P < 0.001 for all). After including metrics significant on univariable analysis and correcting for multicollinearity, the final multivariable model had R2 of 0.8729. Therefore, the following variables were associated with lower (improved) MIR for cancer: 1) nurses/midwives per 1000 population (β = —0.0049, P < 0.057), 2) UHC index (β = —0.0042, P < 0.001), 3) radiotherapy centers per 1000 population (β = —11.21, P = 0.072), and 4) GDP per capita (β = —1.7x10-6, P < 0.001). On analysis stratified by sex, the following were associated with improved MIR for all cancers among females: 1) UHC index (β = —0.0042, P < 0.001), 2) GDP per capita (β = —9.9x10-7, P = 0.02), and 3) gender inequality index (β = 0.13, P = 0.084) (R2 0.8699). The following were associated with improved MIR for all cancers among males: 1) nurses/midwives per 1000 population (β = —0.0053, P = 0.066), 2) UHC index (β = —0.0042, P < 0.001), 3) radiotherapy centers per 1000 population (β = —12.37, P = 0.076), 4) GDP per capita (β = —2.31x10-6, P < 0.001) (R2 0.8485). Conclusions: This comprehensive pan-cancer analysis of health system metrics suggests progress towards UHC, strengthening the nursing/midwifery workforce, facilitating access to services such as radiotherapy, and mitigating gender inequality are key priorities in cancer control. These generalizable findings may guide efforts to strengthen cancer systems throughout the world.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".