Chasing cancer: does the social-to-medical spending ratio relate to cancer incidence and mortality in Canadian provinces? A retrospective cohort study
Bibliographic record
Abstract
Introduction: Cancer is the leading cause of death in Canada, and cases are expected to rise by 83% between 2012 and 2042. Jurisdictions with higher ratios of social-to-medical spending exhibit better population health outcomes; however, the connection between the ratio and both cancer incidence and mortality is not well established. We aim to determine the association between the ratio and both age-standardised cancer incidence and mortality. Methods: Using linear regressions with provincial and yearly fixed effects, we measured associations between the ratio and incidence of the four most common cancers in Canada (ie, lung and bronchus, colorectal, breast and prostate cancer), and mortality from any cancer, from 1992 to 2017 (incidence) and 2000 to 2019 (mortality). Results: A one-cent increase in social spending for each dollar spent on medical services was significantly associated with a decrease in colorectal (-0.2%), breast (-0.1%), and prostate cancer (-0.6%). The relationship is statistically insignificant and negligible for lung cancer incidence and cancer mortality. Conclusion: The ratio was significantly associated with a decrease in three out of four cancer incidence categories, but not mortality. This implies that, consistent with the social determinants of health, preventing cancer incidence might be a function of social spending, whereas medical spending is more relevant for individuals already diagnosed with cancer. This analysis points to the importance of a health-in-all-policies perspective, as social spending might be more important for population health than spending on the medical care system. We provide evidence that morbidity measures are responsive to the ratio, building on a literature focused on mortality.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".