Disease Burden, Risk Factors, and Trends of Leukaemia: A Global Analysis
Bibliographic record
Abstract
Leukaemia accounted for approximately 2.5% of all new cancer incidence and 3.1% of cancer-related mortality. The investigation of its risk factors and epidemiologic trends could help describe the geographical distribution and identify high-risk population groups. This study aimed to evaluate the global incidence, mortality, associated risk factors, and temporal trends of leukaemia by sex, age, and country. We extracted incidence and mortality of leukaemia from GLOBOCAN, CI5, WHO mortality database, NORDCAN, and SEER. We searched the WHO Global Health Observatory data repository for the age-standardised prevalence of lifestyle and metabolic risk factors. We tested the trends by calculating Average Annual Percentage Change (AAPC) from Joinpoint regression. The age-standardized rate of incidence and mortality were 5.4 and 3.3 per 100,000 globally. The incidence and mortality of leukaemia were associated with Human Development Index, Gross Domestics Products per capita, prevalence of smoking, physical activity, overweight, obesity, and hypercholesterolaemia at the country level. Overall, more countries were showing decreasing trends than increasing trends in incidence and mortality. However, an increasing trend of leukaemia incidence was found in Germany, Korea, Japan, Canada and the United Kingdom (AAPC, 2.32-0.98) while its mortality increased in the Philippines, Ecuador, Belarus, and Thailand (AAPC, 2.49-1.23). There was a decreasing trend of leukaemia for the past decade while an increase in incidence and mortality was observed in some populations. More intensive lifestyle modifications should be implemented to control the increasing trends of leukaemia in regions with these trends. Future studies may explore the reasons behind these epidemiological transitions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".