Global Burden of Head and Neck Cancer: Economic Consequences, Health, and the Role of Surgery
Bibliographic record
Abstract
Objective We aimed to describe the mortality burden and macroeconomic effects of head and neck cancer as well as delineate the role of surgical workforce in improving head and neck cancer outcomes. Study Design Statistical and economic analysis. Setting Research group. Subjects and Methods We conducted a statistical analysis on data from the World Development Indicators and the 2016 Global Burden of Disease study to describe the relationship between surgical workforce and global head and neck cancer mortality‐to‐incidence ratios. A value of lost output model was used to project the global macroeconomic effects of head and neck cancer. Results Significant differences in mortality‐to‐incidence ratios existed between Global Burden of Disease study superregions. An increase of surgical, anesthetic, and obstetric provider density by 10% significantly correlated with a reduction of 0.76% in mortality‐to‐incidence ratio ( P <. 0001; adjusted R 2 = 0.84). There will be a projected global cumulative loss of $535 billion US dollars (USD) in economic output due to head and neck cancer between 2018 and 2030. Southeast Asia, East Asia, and Oceania will suffer the greatest gross domestic product (GDP) losses at $180 billion USD, and South Asia will lose $133 billion USD. Conclusion The mortality burden of head and neck cancer is increasing and disproportionately affects those in low‐ and middle‐income countries and regions with limited surgical workforces. This imbalance results in large and growing economic losses in countries that already face significant resource constraints. Urgent investment in the surgical workforce is necessary to ensure access to timely surgical services and reverse these negative trends.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".