Changing burden of adult early-onset cancers: a population-based study
Bibliographic record
Abstract
Abstract Background Adult early-onset cancers, i.e., diagnosed before the age of 50, appear to be rising globally, raising major concerns among the public and health authorities. However, distinctions between trends in the absolute numbers of cases, crude rates, and age-standardized rates are often unclear. Comparisons with later-onset cancers are also often missing, making it difficult to assess age-specific patterns. We described long-term changes in the burden of adult early-onset cancers, compared to later-onset cancers, from 1982 to 2021 in Switzerland. Methods We used population-based data from the Swiss National Institute for Cancer Epidemiology and Registration. We included all primary invasive malignant cancers from 1982 to 2021, except non-melanoma skin cancer. Adult early-onset cancers were defined as cancer diagnoses or deaths between ages 20 to 49, and later-onset at the age of 50 or older. We calculated absolute numbers, crude rates, and age-standardized incidence (ASIR) and mortality (ASMR) rates per 100,000, stratified by sex. Relative changes in ASIR and ASMR were assessed from 1982 to 2021. Results In 2021, early-onset cancers accounted for 10% of 48,850 diagnosed cases, compared to 13% in 1982. From 1982 to 2021, the absolute number of new early-onset cases increased by 56% compared to 111% for later-onset cases. The ASIR increased similarly for early- (+13%) and later-onset cancers (+12%). Among men, the ASIR was stable for early- (-0.2%) and increased for later-onset cancers (+5%). Among women, the ASIR rose similarly for early- (+22%) and later-onset cancers (+26%). The ASMR dropped by 64% for early-onset and by 41% for later-onset cancers. Conclusions Early-onset cancers showed a smaller rise in the absolute number of new cases compared to later-onset cancers, while the increase in age-standardized incidence rates was similar in both groups. Mortality rates have greatly decreased, showing substantial progress in fighting the disease. Key messages • Long-term changes in cancer trends were not specific to early-onset cancers, and these cancers still represent a minor portion of the overall cancer burden. • Distinguishing between absolute numbers and age-standardized cancer rates is essential to accurately assess the changing burden of cancer.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".