Burdens of Breast Cancer and Projections for 2030 Among Women in Asia: Findings from the 2021 Global Burden of Disease Study
Bibliographic record
Abstract
Background: Employing the most recent dataset from the Global Burden of Disease (GBD) Study 2021, this report sought to delineate the current epidemiologic landscape of breast cancer in Asian women. Methods: We examined the evolving trends in disease prevalence and explored the correlations between breast cancer and factors such as age, temporal periods, and generational cohorts. We utilized an autoregressive integrated moving average (ARIMA) model to predict the incidence and deaths of breast cancer in Asia. Results: From 1990 to 2021, the age-standardized incidence rate (ASIR), age-standardized DALYs rate (ASDR), and age-standardized mortality rate showed an overall upward trend for Asian women with breast cancer. In 2021, the high-income Asia Pacific region had the highest ASIR value, while South Asia had the lowest ASIR value. The highest age-standardized mortality rate and ASDR values in 2021 occurred in Southeast Asia, while the lowest values for these metrics were in East Asia. In 2021, breast cancer incidence and DALYs were highest in the 50–54 age group, with deaths peaking in the 55–59 age group. The leading risk factor attributed to breast cancer deaths in Asia in 1990 and 2021 was a “diet high in red meat”. Breast cancer incidence and mortality rates are expected to continue to rise in Asia over the next 10 years. Conclusions: The burden of breast cancer in Asian women is increasing, especially in low SDI countries. This study highlighted the differences between populations and regions and predicted the incidence and mortality rates of breast cancer in Asia over the next decade using an ARIMA model. An increased awareness of breast cancer risk factors and prevention strategies is necessary to reduce breast cancer burden in the future.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 0.001 |
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".