Long‐Term Projections of Childhood Cancer Incidence and Prevalence in Ontario, Canada Until 2040
Bibliographic record
Abstract
BACKGROUND: The prevalence of childhood cancer continues to rise due to increases in cancer incidence and advances in treatment, leading to better survival. We generated epidemiologic projections for childhood cancer, by cancer type, in Ontario, Canada, until 2040. METHODS: We used the Pediatric Oncology Microsimulation Model for Prevalence (POSIM-Prev) to simulate incident and prevalent cases of childhood cancer across historical (1970-2019) and future (2020-2040) time periods. The model was utilized to estimate annual population-level projections of incidence (counts and crude rates per million children), overall survival rates, and limited-duration prevalence (counts and crude rates per 100,000 population) for 14 types of childhood cancer between 2020 and 2040. RESULTS: Across future years, crude incidence rates are projected to increase for 10 cancer types in Ontario, with the largest growth expected for non-Hodgkin lymphomas. Crude prevalence rates are projected to rise between 2020 and 2040 for 13 cancer types and remain stable for bone tumors. While individuals diagnosed with lymphoid leukemia will continue to comprise the largest proportion of overall prevalence during this period, the largest relative increases in prevalence are estimated for those diagnosed with hepatic tumors and acute myeloid leukemia. By 2040, the percentage of prevalent individuals, by malignancy, who are expected to reach late adulthood (aged 60+) ranges from 4% (hepatic tumors) to 19% (bone tumors). CONCLUSION: Further increases in incidence and improvements in survival for several pediatric cancer types will contribute to substantially higher prevalence by 2040, with a projected shift toward older subpopulations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".