International Trends in the Incidence of Cancer Among Adolescents and Young Adults
Bibliographic record
Abstract
BACKGROUND: Although adolescent and young adult (AYA) cancers represent a unique spectrum of malignancies, epidemiological studies of cancer incidence often group AYAs together with younger or older populations, obscuring patterns specific to this population. METHODS: We examined AYA cancer incidence trends in 41 countries over a 15-year period using the CI5plus database. Truncated age-standardized incidence rates were calculated and the annual percentage change was assessed, with statistical significance corresponding to a 95% confidence interval that does not include zero. RESULTS: From 1998 to 2012, the 41 included countries contributed a total of 1 846 588 cancer cases and 3.1 billion person-years among AYAs. Although statistically significant increases in the overall cancer burden were observed in 23 countries, the magnitude varied considerably, with the greatest increase in incidence observed in South Korea (annual percentage change2002-2012 = 8.5%, 95% confidence interval = 7.6% to 9.4%) due to thyroid cancer. Notable trends included sharp increases in the incidence of obesity-related malignancies among AYAs; indeed, statistically significant increases were observed among AYAs for 10 of 11 and 9 of 11 obesity-related cancer sites in the US and UK, respectively, with at least five obesity-related cancers statistically significantly increasing in Canada, Japan, South Korea, Australia, and the Netherlands. Other striking trends were noted for thyroid and testicular cancer, with statistically significantly increasing rates observed in 33 and 22 countries, respectively, whereas statistically significant declines in incidence were observed for smoking-related cancers, cervical cancer, and Kaposi sarcoma in many countries. CONCLUSIONS: Our results highlight the future health-care needs related to treatment as well as the urgency for public health initiatives that can mitigate the increasing burden of cancer in AYAs.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".