Socioeconomic Inequalities in Participation in Colorectal Cancer Screening in Ontario, Canada: A Decomposition Analysis
Bibliographic record
Abstract
BACKGROUND: The relationship between socioeconomic status and colorectal cancer screening in Canada remains poorly understood. This study aims to measure and explain the extent of socioeconomic inequalities in colorectal cancer screening participation in Ontario, Canada. METHODS: This study assesses socioeconomic inequalities in colorectal cancer screening uptake in Ontario among adults of ages 50 to 74 years (n = 12,039) utilizing cross-sectional data from the 2017 to 2018 Canadian Community Health Survey (CCHS). The Wagstaff index and the Erreygers index were used to quantify and decompose income-related inequality in colorectal cancer screening participation. RESULTS: The results revealed an overall colorectal cancer screening rate of 71.7%, with higher rates among females (78.4%) compared with males (69.4%). The positive values of the Wagstaff index (0.193; 95% confidence interval, 0.170-0.215) and the Erreygers index (0.156; 95% confidence interval, 0.138-0.174) indicated a pro-rich inequality in colorectal cancer screening participation in Ontario (i.e., screening is more concentrated among wealthier individuals). The decomposition analysis identified income (71.61%), education (8.61%), and language barriers with healthcare providers (5.76%) as the primary factors contributing to the observed income-related inequality in colorectal cancer screening participation. CONCLUSIONS: Income is the primary driver of socioeconomic inequality, requiring targeted strategies to boost screening rates among low-income residents. Addressing education and language barriers through awareness initiatives and language support can reduce socioeconomic inequalities in cancer screening uptake in Ontario. IMPACT: Our study reveals significant socioeconomic inequality in colorectal cancer screening in Ontario, driven by income, education, and language barriers, underscoring the need for targeted interventions to promote equitable access.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".