Geospatial analysis and participant characteristics associated with colorectal cancer screening participation in Alberta, Canada: a population-based cross-sectional study
Bibliographic record
Abstract
Abstract Background Colorectal cancer (CRC) is a leading cause of death in Canada and early detection can prevent deaths through screening. However, CRC screening in Alberta, Canada remains suboptimal and varies by sociodemographic and health system characteristics, as well as geographic location. This study aimed to further the understanding of these participant and health system characteristics associated with CRC screening in Alberta and identify clusters of regions with higher rates of overdue or unscreened individuals. Methods We included Albertans aged 52 to 74 as of December 31, 2019 (index date) and we used data from administrative health data sources and linked to the Alberta Colorectal Cancer Screening Program database to determine colorectal cancer screening rates. We used multivariable multinomial logistic regression analysis to investigate the relationship between sociodemographic, health system characteristics and participation in CRC screening. We used optimized Getis-Ord Gi* hot-spot analysis to identify hot and cold-spots in overdue for and no record of CRC screening. Results We included 919,939 Albertans, of which 65% were currently up to date on their CRC screening, 21% were overdue, and 14% had no record of CRC screening. Compared to Albertans who were currently up to date, those who were in older age groups, those without a usual provider of care, those who were health system non-users, and those living in more deprived areas were more likely to have no record of screening. Areas with high number of Albertans with no record of screening were concentrated in the North and Central zones. Conclusions Our study showed important variation in colorectal cancer screening participation across sociodemographic, health system and geographical characteristics and identified areas with higher proportions of individuals who have no record of screening or are under-screened in Alberta, Canada.
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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 | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".