Nasopharyngeal Cancer Incidence and Mortality in 185 Countries in 2020 and the Projected Burden in 2040: Population-Based Global Epidemiological Profiling
Bibliographic record
Abstract
BACKGROUND: Nasopharyngeal cancer (NPC) is one of the most common head and neck cancers. OBJECTIVE: This study describes the global epidemiological profiles of NPC incidence and mortality in 185 countries in 2020 and the projected burden in 2040. METHODS: The estimated numbers of NPC cases and deaths were retrieved from the GLOBOCAN 2020 data set. Age-standardized incidence rates (ASIRs) and age-standardized mortality rates (ASMRs) were calculated using the world standard. The future number of NPC cases and deaths by 2040 were estimated based on global demographic projections. RESULTS: Globally, approximately 133,354 cases and 80,008 deaths from NPC were estimated in 2020 corresponding to ASIRs and ASMRs of 1.5 and 0.9 per 100,000 person-years, respectively. The largest numbers of both global cases and deaths from NPC occurred in Eastern Asia (65,866/133,354, 49.39% and 36,453/80,008, 45.56%, respectively), in which China contributed most to this burden (62,444/133,354, 46.82% and 34,810/80,008, 43.50%, respectively). The ASIRs and ASMRs in men were approximately 3-fold higher than those in women. Incidence rates varied across world regions, with the highest ASIRs for both men and women detected in South-Eastern Asia (7.7 and 2.5 per 100,000 person-years, respectively) and Eastern Asia (3.9 and 1.5 per 100,000 person-years, respectively). The highest ASMRs for both men and women were found in South-Eastern Asia (5.4 and 1.5 per 100,000 person-years, respectively). By 2040, the annual number of cases and deaths will increase to 179,476 (46,122/133,354, a 34.58% increase from the year 2020) and 113,851 (33,843/80,008, a 42.29% increase), respectively. CONCLUSIONS: Disparities in NPC incidence and mortality persist worldwide. Our study highlights the urgent need to develop and accelerate NPC control initiatives to tackle the NPC burden in certain regions and countries (eg, South-Eastern Asia, China).
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".