Global epidemiological trend of cancer incidence and death in adults aged 60 years and older: a systematic analysis of data from GLOBOCAN 2022 and GBD2021
Bibliographic record
Abstract
BACKGROUND: Updated and comprehensive global assessments of cancer statistics specifically targeting adults aged ≥ 60 years have remained limited. To address this gap, we conducted a systematic epidemiological analysis of cancer burden in this rapidly growing demographic worldwide. METHODS: Population-based data from the Global Cancer Observatory (GLOBOCAN) 2022 and the Global Burden of Diseases (GBD) 2021 were used and analyzed in this study. The main outcomes of this study were age-standardised incidence (ASIR) or mortality rate (ASMR), and average annual percent change (AAPC). A Bayesian age-period-cohort model was employed to project cancer trends through 2050. RESULTS: Globally, adults aged 60 years and older accounted for 65% of cancer diagnoses and 74% of cancer-related deaths in 2022. Among all cancer types, tracheal, bronchus, and lung (TBL) cancers had the highest incidence and mortality rates. Males and populations in high socio-demographic index (SDI) regions experienced a greater cancer burden compared to females and low-SDI populations. The ASIR and ASMR increased steadily with age, peaking in the over 85 age group. Geographically, ASIR was highest in the USA and Canada, followed by Australasia and Western Europe. Central Europe, East Asia, and Western Europe showed the highest ASMR. From 1992 to 2021, while both ASIR and ASMR declined globally, there were ongoing increases in the cancer burden for certain cancers, including breast, prostate, colorectal, pancreatic, and liver cancers. Projections indicate that, by 2050, the number of cases among this age group may rise to 22.2 million annually, with deaths possibly reaching 12.9 million. TBL cancers will remain the leading cause of cancer incidence and death, followed by breast cancer. CONCLUSIONS: The global cancer burden among adults aged ≥ 60 years showed a pronounced heterogeneity across sex, age strata, geographic regions, and SDI levels, and will suffer a sustained increase over the coming decades. Our study offers a comprehensive epidemiological overview for prioritizing precision interventions targeting high-risk populations and policy formulation for aging societies.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".