Evaluation of population-based screening programs on colorectal cancer screening uptake and predictors in Atlantic Canada: insights from a repeated cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Colorectal cancer (CRC) poses a significant public health challenge in Canada, with the Atlantic provinces bearing a particularly high burden. The implementation of population-based colon screening programs is aimed to address this concern. However, limited research exists on the effect of these programs especially in Canada. This study aimed to examine the impact of the first few years of the CRC screening programs in the Atlantic provinces of Canada by assessing changes in screening uptake, barriers, and predictors of screening among eligible populations. METHODS: Employing a repeated cross-sectional design, this study analyzed data from a representative sample of 7614 respondents in 2010 and 6850 in 2017 from the Atlantic provinces aged 50-74 years, extracted from the Canadian Community Health Survey (CCHS). The outcomes measured were CRC screening rates, changes in predictors of screening uptake, and barriers to participation. Potential predictors examined included age, sex, income, education, smoking, and health status. RESULTS: The proportion of adults aged 50-74 years who meet CRC screening requirements increased from 42% in 2010 to 54% in 2017 yet below the national target of 60%. New Brunswick reported the most significant increase in screening prevalence (18%, p < 0.05). Participation in fecal tests increased from 19.6 to 32.4%. Despite these improvements, disparities in screening participation remained, with lower uptake observed among individuals with lower income and education levels. Age (> 60 years, OR = 2.09, p < 0.01), the presence of multiple chronic health conditions (OR = 2.11, p < 0. 01), being female (OR = 1.21, p < 0.01), married status (OR = 1.21, p < 0.05), access to regular healthcare (OR = 1.91, p < 0.01), and nonsmoking status (OR = 2.55, p < 0.01) were identified as significant predictors of CRC screening uptake. CONCLUSIONS: This study shows that while CRC screening uptake increased across the Atlantic provinces between 2010 and 2017, barriers to and disparities in screening participation persist. This highlights the need for targeted interventions to improve awareness, access, and screening uptake, particularly among disadvantaged groups, to promote equitable healthcare outcomes. Continued efforts should focus on reducing barriers to screening and leveraging available evidence to inform interventions aimed at mitigating the CRC burden in the region.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".