Socioeconomic Gradients in Prostate Cancer Incidence Among Canadian Males: A Trend Analysis From 1992 to 2010
Bibliographic record
Abstract
INTRODUCTION: Understanding the effects of socioeconomic status on cancer incidence and their trends over time will help inform public health interventions for cancer control. This study sought to investigate trends in socioeconomic inequalities in prostate cancer incidence among Canadian males. METHODS: Using a census division level dataset (n = 280) constructed from the Canadian Cancer Registry, Canadian Census of Population (1992, 1996, 2001, 2006) and 2011 National Household Survey, we examined the effect of socioeconomic status on prostate cancer incidence among Canadian males between 1992 and 2010. The age-adjusted concentration index was used to quantify education/income-related inequalities in prostate cancer incidence. RESULTS: The crude prostate cancer incidence increased from 115 to 137 per 100 000 males in Canada from 1992 to 2010 with a peak in 2007. The rate increased significantly in all but three of four western provinces. The age-adjusted concentration index showed a higher concentration of prostate cancer diagnoses among males living in high-income neighbourhoods in Canada in particular from 1996 to 2005. In contrast, the index was higher among males living in less-educated neighbourhoods in the most recent study years (2006-2010). CONCLUSIONS: The concentration of new prostate cancer cases among high-income populations in Canada may be explained by the rise of opportunistic screening of asymptomatic males; however, this should be studied in further detail. Since we found a higher incidence rate of prostate cancer among less-educated males in Canada in recent years, risk-benefit investigation of primary prevention and opportunistic screening for less-educated males is advised.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".