The Impact of a Population-Based Screening Program on Income- and Immigration-Related Disparities in Colorectal Cancer Screening
Bibliographic record
Abstract
Abstract Background: A population-based program promoting the Fecal Occult Blood Test (FOBT) for colorectal cancer screening was introduced in 2008 in Ontario, Canada, where opportunistic screening with colonoscopy had been increasing in frequency. We evaluated the impact of the program on income and immigration-related disparities in screening. Methods: We used linked administrative data to calculate colorectal cancer screening rates for eligible Ontarians in each year between 2001/02 (n = 2,852,619) and 2013/14 (n = 4,139,304). We quantified disparities using an “inequality ratio” of screening rates in the most disadvantaged group relative to the most advantaged group. We performed segmented logistic regression analyses stratified by screening modality and adjusted for age, sex, rurality, comorbidity, and morbidity. Results: Between 2001/02 and 2013/14, the income and immigration inequality ratios narrowed from 0.74 to 0.80 and 0.55 to 0.69, respectively. Before the screening program, the income inequality ratio was widening by 1% per year (95% CI 1% to 1%); in the year it was introduced, it narrowed by 4% (95% CI 2% to 7%) and in the years following, it remained stable [0% decrease (95% CI 1% decrease to 0% decrease) per year]. Results were similar for immigration-related disparities. After program introduction, disparities in receiving FOBT were narrowing at a faster rate while disparities in receiving colonoscopy were widening at a slower rate. Conclusions: Introduction of a population-based screening program promoting FOBT for colorectal cancer was associated with only modest improvements in immigration and income-related disparities. Impact: Reducing immigration and income-related disparities should be a focus for future research and policy work. Disparities in Ontario seem to be driven by a higher uptake of colonoscopy among more advantaged groups. Cancer Epidemiol Biomarkers Prev; 26(9); 1401–10. ©2017 AACR.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".