Cancer Incidence and Mortality Estimates in Latin America and the Caribbean: A Systematic Analysis of the GLOBOCAN 2022
Bibliographic record
Abstract
Cancer is a leading cause of death in Latin America and the Caribbean (LAC), and up-to-date estimates are essential to guide cancer policy. Using GLOBOCAN 2022 data, we analyzed cancer incidence and mortality across 32 LAC countries, calculated age-standardized rates, and assessed early-onset cancer (diagnosed at ages 15-50 years). Mortality-to-incidence ratios were used as a proxy for survival, joinpoint regression estimated annual percent change, and linear regression evaluated correlation between the Human Development Index and cancer indicators. In 2022, LAC recorded 1,551,060 new cancer cases (age-standardized incidence rate, 186.6 per 100,000) and 749,242 deaths (age-standardized mortality rate, 85.2 per 100,000). Prostate and breast cancers were the most common malignancies, whereas lung and breast cancers caused the highest mortality. Mortality declined for prostate cancer [annual percent change, -1.52; 95% confidence interval (CI), -1.80 to -1.24] and male lung cancer (-2.50; 95% CI, -2.68 to -2.33) but increased for female lung (+1.88; 95% CI, 1.71-2.05) and colorectal cancer (+2.48; 95% CI, 2.30-2.67). Human Development Index showed an inverse correlation with mortality-to-incidence ratio (P < 0.001), suggesting improved survival with higher development. Early-onset cancers represented 17% of new cases and 11% of deaths. These findings reveal a growing cancer burden and that persistent disparities in cancer epidemiology persist across LAC, highlighting the urgent need for targeted cancer control strategies and regional cancer control plans. SIGNIFICANCE: These findings highlight the urgent need for equity-focused cancer control policies, improved early detection, and expanded access to essential cancer care in the region.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.014 | 0.026 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".