Area-Level Income Disparities in Colorectal Screening in Canada: Evidence to Inform Future Surveillance
Bibliographic record
Abstract
Background: Participation in colorectal screening remains low even in countries with universal health coverage. Area-level determinants of low screening participation in Canada remain poorly understood. Methods: We assessed the association between area-level income and two indicators of colorectal screening (having never been screened, having not been screened recently) by linking census-derived local area-level income data with self-reported screening data from urban-dwelling respondents to the Canadian Community Health Survey (50–75 years of age, cycles 2005 and 2007, n = 18,362) who reported no known risk factors for colorectal cancer. Generalized estimating equation Poisson models estimated the prevalence ratios and differences for having never been screened and having not been screened recently, adjusting for individual-level income, education, marital status, having a regular physician, age, and sex. Results: About 53% of the study population had never been screened. Among individuals who had ever been screened, 35% had been screened recently. Adjusting for covariates, lower area-level income was associated with having never been screened [covariate-adjusted prevalence ratios: 1.24 for quartile 1; 95% confidence limits (cl): 1.16, 1.34; 1.25 for quartile 2; 95% cl: 1.15, 1.33; 1.15 for quartile 3; 95% cl: 1.08, 1.23]. Among individuals who had been screened in their lifetime, area-level income was not associated with having not been screened recently. Conclusions: Lower area-level income is associated with having never been screened for colorectal cancer even after adjusting for individual socioeconomic factors. Those findings highlight the potential importance of socioeconomic contexts for colorectal screening initiation and merit attention in both future research and surveillance efforts.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".