Colorectal, cervical and prostate cancer screening in Australians with severe mental illness: Retrospective nation-wide cohort study
Bibliographic record
Abstract
OBJECTIVE: People with severe mental illness have similar cancer incidence, but higher mortality than the general population. Participation in cancer screening may be a contributing factor but existing studies are conflicting. The aim of this study was to investigate the frequency of colorectal, prostate and cervical cancer screening among people with and without severe mental illness in Australia, who have access to universal health care. METHODS: We followed three cohorts using de-identified data from a random 10% sample of people registered for Australia's universal health care system: those aged 50-69 years ( n = 760,058) for colorectal cancer screening; women aged 18-69 years ( n = 918,140) for cervical cancer screening and men aged 50-69 years ( n = 380,238) for prostate cancer screening. We used Poisson regression to estimate incidence rate ratios and 95% confidence intervals for the association between severe mental illness and rates of faecal occult blood testing, pap smears and prostate-specific antigen testing. RESULTS: Having severe mental illness was associated with a 17% reduction in rates of pap smear (incidence rate ratio = 0.83, 95% confidence interval: 0.82-0.84) and prostate-specific antigen testing (incidence rate ratio = 0.83, 95% confidence interval: 0.81-0.85), compared to the general population. By contrast, incidence rates of faecal occult blood testing were only lower in people with severe mental illness among the participants who visited their general practitioner less than an average of five times per year (incidence rate ratio = 0.83, 95% confidence interval = [0.73, 0.94]). CONCLUSION: Our results suggest that differences in screening frequency may explain some of the mismatch between cancer incidence and mortality in people with severe mental illness and indicate that action is required to improve preventive screening in this very disadvantaged group.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".