Geographical Variation and Factors Associated with Colorectal Cancer Mortality in a Universal Health Care System
Bibliographic record
Abstract
OBJECTIVE: To investigate the geographical variation and small geographical area level factors associated with colorectal cancer (CRC) mortality. METHODS: Information regarding CRC mortality was obtained from the population-based Manitoba Cancer Registry, population counts were obtained from Manitoba's universal health care plan Registry and characteristics of the area of residence were obtained from the 2001 Canadian census. Bayesian spatial Poisson mixed models were used to evaluate the geographical variation of CRC mortality and Poisson regression models for determining associations with CRC mortality. Time trends of CRC mortality according to income group were plotted using joinpoint regression. RESULTS: The southeast (mortality rate ratio [MRR] 1.31 [95% CI 1.12 to 1.54) and southcentral (MRR 1.62 [95% CI 1.35 to 1.92]) regions of Manitoba had higher CRC mortality rates than suburban Winnipeg (Manitoba's capital city). Between 1985 and 1996, CRC mortality did not vary according to household income; however, between 1997 and 2009, individuals residing in the highest-income areas were less likely to die from CRC (MRR 0.77 [95% CI 0.65 to 0.89]). Divergence in CRC mortality among individuals residing in different income areas increased over time, with rising CRC mortality observed in the lowest income areas and declining CRC mortality observed in the higher income areas. CONCLUSIONS: Individuals residing in lower income neighbourhoods experienced rising CRC mortality despite residing in a jurisdiction with universal health care and should receive increased efforts to reduce CRC mortality. These findings should be of particular interest to the provincial CRC screening programs, which may be able to reduce the disparities in CRC mortality by reducing the disparities in CRC screening participation.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".