Cancer prevalence, incidence and mortality in people who experience incarceration in Ontario, Canada: A population-based retrospective cohort study
Bibliographic record
Abstract
INTRODUCTION: Evidence suggests that many risk factors for cancer are overrepresented in people who experience incarceration, and data on cancer epidemiology are limited for this population. We aimed to describe cancer prevalence, incidence and mortality in adults admitted to provincial custody in Ontario, Canada in 2000. METHODS: We linked data on 48,166 adults admitted to provincial custody in Ontario in 2000 with Ontario Cancer Registry data to 2012. We calculated cancer prevalence in the 10 years prior to admission to custody in 2000, incidence between 2000 and 2012 and mortality between 2000 and 2011. Standardized for age, we calculated incidence and mortality ratios by sex compared to the general population of Ontario. RESULTS: The 10-year cancer prevalence was 0.4% in men and 0.6% in women at admission to provincial custody in 2000. Between 2000 and 2012, 2.6% of men and 2.8% of women were diagnosed with new cancer. The standardized incidence ratio for cancer was 1.0 (95% CI 0.9-1.0) for men and 0.9 (95% CI 0.7-1.0) for women compared to the general population, and was significantly increased for cervical, head and neck, liver and lung cancers. The standardized mortality ratio was 1.6 (95% CI 1.4-1.7) in men and 1.4 (95% CI 1.0-1.9) in women, and was significantly increased for head and neck, liver, and lung cancers. CONCLUSIONS: There is an excess burden of cancer in people who experience incarceration. Cancer prevention should include people who experience incarceration, and the period of incarceration may offer an opportunity for intervention.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".