Global, regional and national burden of trachea, bronchus, and lung cancer in middle-aged and elderly people aged 55+ years from 1990 to 2021, with projections to 2036: a systematic analysis of the Global Burden of Disease Study 2021
Bibliographic record
Abstract
Background: Tracheal, bronchus, and lung (TBL) cancer remains a major global health burden, particularly among adults aged ≥55 years. Despite medical advancements, rising incidence in middle-aged adults and persistent regional disparities underscore the need for targeted public health strategies. Comprehensive analysis integrating global burden, socio-demographic factors, and future projections is essential to guide public health interventions and resource allocation. This study aims to assess the global, regional, and national patterns of TBL cancer burden, identify the impact of socio-demographic factors, and project future trends to inform effective prevention and control strategies. Methods: Using Global Burden of Disease (GBD) 2021 data, we analyzed TBL cancer incidence, mortality, and disability-adjusted life years (DALYs) across 204 countries from 1990 to 2021. Data were sourced from national cancer registries, health surveys, and statistical estimates. Temporal trends were assessed using join-point regression to identify significant inflection points in disease burden. Healthcare system efficiency was evaluated with data envelopment analysis (DEA) and stochastic frontier analysis (SFA). Projections for 2036 were made using an autoregressive integrated moving average (ARIMA) model, incorporating historical trends and population data obtained from GBD. Results: In 2021, TBL cancers resulted in 2.02 million new cases, 1.81 million deaths, and 37.63 million DALYs globally among adults aged ≥55 years. East Asia bore the highest burden, while Sub-Saharan Africa had the lowest. Men had significantly higher incidence and mortality than women, with DALY rates peaking in high-middle Socio-Demographic Index (SDI) regions. High-income nations exhibited declining trends, whereas low-middle SDI countries showed rising burdens, particularly among males. Join-point regression analysis results revealed that incidence rates declined in more than half of the countries, including Australia [average annual percentage change (AAPC), -0.56] and Canada (AAPC, -0.81), with the most pronounced reductions in Greenland (AAPC, -1.25) and Kazakhstan (AAPC, -2.46). In contrast, Egypt exhibited the highest growth (AAPC, +3.45). The ARIMA model projected continued decline in mortality in high-SDI regions, stabilization in middle-SDI regions, and persistent or increasing burden in low-SDI countries, especially for males. Conclusions: The global TBL cancer burden reflects a complex interplay of socioeconomic development, tobacco control, and environmental risk factors. Forecasts suggest widening disparities, with lower SDI regions expected to face a continued rise in mortality. Addressing gender disparities, expanding genomic and early detection programs in high-burden regions, and implementing scalable environmental policies in resource-limited settings are critical for improving outcomes. Aligning SDI growth with healthcare reforms and optimizing resource allocation through predictive modeling will help mitigate inefficiencies and enhance long-term cancer control efforts.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".