Head and Neck Cancer in Southeast Asia: 2022 Incidence, Mortality, and Projections to 2050
Bibliographic record
Abstract
OBJECTIVES: To characterize the incidence, mortality, and disparities in head and neck cancer (HNC) across Southeast Asia (SEA) in 2022 and project trends to 2050 to inform cancer planning. METHODS: We conducted a population-based analysis using 2022 Global Cancer Observatory data from 11 SEA countries. We analyzed cancers of the lip and oral cavity, salivary glands, oropharynx, nasopharynx, hypopharynx, larynx, and thyroid. Age-standardized incidence and mortality rates (ASIR and ASMR) were calculated using the Segi-Doll world standard. Projections to 2050 were based on demographic changes using UN World Population Prospects data, assuming stable incidence and mortality rates. RESULTS: Myanmar had the highest oral cancer rates (ASIR: 6.6 males, 2.6 females; ASMR: 3.9 males, 1.6 females). Elevated salivary gland cancer incidence was observed in Indonesia, the Philippines, and Singapore. Oropharyngeal cancers showed strong male predominance in Myanmar (ASIR ratio: 10:1). Nasopharyngeal cancer incidence in Brunei and Indonesia exceeded global averages (ASIR: 9.8 and 9.6 males). Projections to 2050 estimate 47,000 new male cases and 28,200 new female cases in Indonesia, with 30,600 male deaths and 12,200 female deaths. While SEA's overall ASIR for HNC (18.0) was comparable to the global average (18.9), the ASMR was significantly higher (9.5 vs. 5.3). CONCLUSIONS: The rising burden of HNC in SEA highlights urgent disparities in incidence and mortality. Targeted prevention, early detection, and investment in cancer care systems are essential to mitigate future disease burden and improve outcomes. LEVEL OF EVIDENCE: Level III-Epidemiologic study using population-based registry data.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".