Head and neck cancers in Southeast Asia: 2022 incidence and mortality estimates for 2022 and projections for 2050.
Bibliographic record
Abstract
e23281 Background: Head and neck cancers pose challenges due to their aggressive nature and complex treatment needs. In Southeast Asia (SEA), with over 690 million people, rising cancer rates are driven by tobacco, betel quid, alcohol use, and genetic factors. Aging populations, urbanization, and healthcare disparities further worsen the burden. This study examines the incidence, mortality, and regional disparities of head and neck cancers in SEA for 2022 and projects trends through 2050 to guide cancer care planning. Methods: We performed a population-based cross-sectional analysis using 2022 data from the Global Cancer Observatory (GCO), derived from national cancer registries. Age-standardized incidence rates (ASIR per 100,000) and mortality rates (ASMR per 100,000) were calculated using the Segi-Doll world reference population. Projections for 2050 incorporated population growth and changes in age distribution, assuming constant ASIR and ASMR. We analyzed data by country, sex, and cancer subsite. Results: The SEA incidence rate of all head and neck cancer is similar to the global rate (ASIR 18.0 vs. 18.9). However, the SEA mortality rate is notably higher than the global average (ASMR 9.5 vs. 5.3). For lip and oral cavity cancers, Myanmar had the highest rates of incidence (ASIR: 6.6 [per 100,000] for males, 2.6 for females) and mortality (ASMR: 3.9 for males, 1.6 for females). Salivary gland cancer incidence was elevated in Indonesia (1.0 for males, 0.59 for females) and the Philippines (0.81 for males, 0.55 for females). In terms of oropharyngeal cancer, males had higher incidence and mortality rates than females across SEA. In Myanmar, for example, the ASIR for males was 2.4 (ten times higher than females), with an ASMR of 1.5 (twelve times higher than females). Nasopharyngeal cancer incidence in Brunei and Indonesia far exceeded global averages (9.8 and 9.6 for males; 4.0 and 2.8 for females, compared to global ASIRs of 1.9 for males and 0.73 for females). Myanmar also reported the highest incidence of hypopharyngeal (4.9 for males, 0.38 for females) and laryngeal cancer (4.5 for males, 0.51 for females). For thyroid cancer, female rates were higher across all countries, with Singapore exhibiting the highest ASIR (10.7 for females, 3.8 for males), though still below the global average for females of 13.6 per 100,000. Conclusions: The growing burden of head and neck cancers in SEA calls for urgent action to address regional healthcare disparities. Enhancing healthcare infrastructure, improving early detection and prevention, and fostering regional collaboration are critical for mitigating the rising cancer burden. Future research should focus on identifying cost-effective, region-specific interventions tailored towards the local incidence and mortality of specific types of cancer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| 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 | 0.002 |
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".