Trends in socioeconomic inequalities in pancreatic cancer mortality in Canada: Evidence from the Canadian Vital Statistics Death Database
Bibliographic record
Abstract
Background Pancreatic cancer is one of the leading causes of death in Canada and is projected to be the second leading cause of cancer death by 2030. This study sought to evaluate education and income inequalities in pancreatic cancer mortality in Canada between 1990 and 2019. Methods Using a unique census division level dataset (n = 280) constructed from the Canadian Vital Statistics Death Database, Canadian Census of Population (1991, 1996, 2001, 2006, 2016), and National Household Survey (2011) we assess socioeconomic inequalities in pancreatic cancer in Canada. Age-standardized Concentration index was used to quantify income and education inequalities in pancreatic cancer mortality. Trends analyses were conducted to assess changes in income and education inequalities in pancreatic cancer mortality over time. Results Our results show that crude pancreatic cancer mortality in Canada increased significantly from 10.23 for males and 9.65 for females in 1990, to 15.99 for males and 14.28 for females in 2019, per 100,000 people. The statistically significant negative values of age-standardized Concentration indices suggest persistent income and education inequalities in pancreatic cancer mortality in Canada. Trend analyses indicates reductions in income and education inequalities in pancreatic cancer mortality over time, particularly among females. Conclusions Significant income and education inequalities in pancreatic cancer mortality in Canada warrant public policy concern and action. Further research is required to understand whether differential access to treatment across socioeconomic groups played a role in the observed socioeconomic inequalities.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.013 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".