Disease Burden, Risk Factors, and Temporal Trends in Breast Cancer in Low‐ and Middle‐Income Countries: A Global Study
Bibliographic record
Abstract
ABSTRACT Introduction Breast cancer poses significant health risks to women and strains healthcare systems extensively. In low‐ and middle‐income countries (LMICs), limited resources and inadequate healthcare infrastructures further exacerbate the challenges of breast cancer prevention, treatment, and awareness. Methods We examined the prevalence, risk factors, and trends of breast cancer in LMICs. Data on disability‐adjusted life years (DALYs) and breast cancer risk factors were extracted from the Global Burden of Disease (GBD) databases for 203 countries or territories from 1990 to 2019. LMIC DALY rates were examined using joinpoint regression analysis. Results Among the income groups, the lower middle‐income category had the highest DALYs value, with 1787 years per 100,000 people. LMICs collectively accounted for 74% of the global burden of DALYs lost due to breast cancer in 2019. However, it remained relatively consistent in lower middle income countries (LMCs). In LMCs, the risk associated with metabolic syndromes was higher compared to that with behavioral factors alone. For the past three decades, breast cancer incidences increased significantly in LMCs (average annual percent change [AAPC]: 1.212, confidence intervals [CI]: 1.51–1.87, p < 0.001), upper middle income countries (AAPC: 1.701, CI: 1.12–1.48, p < 0.001), and low‐income countries (AAPC: 1.002, CI: 1.57–1.68, p < 0.001). Conclusion This research shows how breast cancer in LMICs is aggravated by low resources and healthcare infrastructure. To effectively combat breast cancer in these areas, future strategies must prioritize improvements in healthcare infrastructure, awareness campaigns, and early detection mechanisms.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".