Cancer Incidence Projections in Northern Ireland to 2040
Bibliographic record
Abstract
Background: Data on historical trends and estimates of future cancer incidence are essential if cancer services are to be adequately resourced in future years. <br/><br/>Methods: Age-standardised incidence rates for all cancers combined and 19 common cancers diagnosed during 1993-2017 were determined by sex, year of diagnosis and age. Data were fitted using an age-period-cohort model, which was used to predict rates in future years up to 2040. These were combined with population projections to provide estimates of the future case number. <br/><br/>Results: Compared to the annual average in 2013-2017, for all cancers (excluding non-melanoma skin) age-standardised incidence rates are expected by 2040 to fall 9% among males and rise 12% among females, while the number of cases diagnosed is projected to increase by 45% for males and 58% for females. Case volume is projected to rise for all cancer types except for cervical and stomach cancer, with the annual number of cases diagnosed projected to more than double among males for melanoma, liver, and kidney cancers, and among females for liver, pancreatic and lung cancers.<br/><br/>Conclusion: Increased numbers of cancer cases is projected, due primarily to projected increases in the number of people aged 60 years and over. <br/><br/>Impact: Projected increases will significantly impact the health services which diagnose and treat cancer. However, while population growth is primarily responsible, reduction of exposure to cancer risk factors, especially tobacco use, obesity, alcohol consumption and UV radiation, could attenuate the predicted increase in cancer cases.<br/>
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".