AB014. The disease burden, risk factors and temporal trends in breast cancer in low- and middle-income countries: a global study
Bibliographic record
Abstract
Background: Breast cancer poses a significant threat to women’s health and places a burden on healthcare systems worldwide. However, low- and middle-income countries (LMICs) often have insufficient breast cancer prevention, treatment, and understanding of risk factors. This study aims to investigate the disease burden, risk factors, and temporal trends of breast cancer specifically in LMICs. Methods: From 1990 to 2019, this study extracted incidence, prevalence, disability-adjusted life years (DALYs) and breast cancer risk factors from the Global Burden of Disease (GBD) databases for 204 countries or territories. Temporal trends were examined using joinpoint regression analysis. Results: Among the income groups, the lower middle-income category had the highest DALYs value, with 1,787 years per 100,000 people. In the map analysis, 91% of African and Middle Eastern countries had age-standardized DALYs rates higher than the crude rate. LMICs countries collectively accounted for 74% of the global burden of DALYs lost due to breast cancer in 2019. Between 1990 and 2019, the prevalence of behavior-related risk factors for breast cancer increased by 47% in upper-middle income countries and 19% in low-income countries. However, it remained relatively consistent in lower-middle income countries. In lower-middle income countries, the risk associated with metabolic syndromes was higher compared to the risk associated with behavioral factors alone. For the recent past decade, breast cancer incidences increased significantly in lower-middle income countries [average annual percentage change (AAPC): 1.69, 95% confidence interval (CI): 1.51–1.87, P<0.001], upper-middle income countries (AAPC: 1.32, 95% CI: 1.12–1.48, P<0.001), and low-income countries (AAPC: 1.62, 95% CI: 1.57–1.68, P<0.001). Conclusions: Breast cancer affects women globally, particularly in LMICs. This research shows how breast cancer in LMICs is aggravated by low resources and healthcare infrastructure. To successfully reduce breast cancer in these contexts, future studies must emphasize healthcare resource allocation.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".