Socioeconomic inequalities in prostate cancer mortality in Canada: Three decades trend analysis
Bibliographic record
Abstract
In Canada, prostate cancer is the most diagnosed cancer among males and the third leading cause of cancer-related deaths accounting for 10 % of all cancer fatalities in males. This study examines socioeconomic inequalities in prostate cancer mortality among the Canadian male population. Using a dataset compiled at the census division level (n = 280) from sources including the Canadian Vital Statistics Deaths database and the Canadian Census of Population for the years 1992, 1996, 2001, 2006, and 2016, as well as the 2011 National Household Survey, we investigated socioeconomic inequalities in mortality rates in prostate cancer among Canadian men from 1990 to 2019. We applied the age-standardized Concentration index (C) to measure inequalities in mortality linked to income and education levels. Trend analysis was conducted to evaluate the changes over time of these inequalities. The crude prostate cancer mortality in Canada was 24.82 per 100,000 males over the study period and decreased significantly over time. The age-standardized C showed a higher concentration of prostate cancer mortality among low-income males in 1999, 2001 and 2005. Additionally, we observed a significantly higher concentration of mortality among less-educated groups, particularly in the more recent study years. Trend analysis revealed a growing concentration of prostate cancer mortality among less-educated males over the study period. Our study revealed an increasing concentration of PCa mortality among low-educated male populations. Socioeconomic inequalities in PCa mortality may be partly attributable to variations in treatment access across different geographic regions and opportunistic screening among higher socioeconomic status males in the more recent study years.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.013 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".