Global burden and trends in premenopausal and postmenopausal breast cancer: a population-based study
Bibliographic record
Abstract
BACKGROUND: Breast cancer has distinct causes, prognoses, and outcomes and effects in patients at premenopausal and postmenopausal ages. We sought to assess the global burden and trends in breast cancer by menopausal status. METHODS: We did a population-based analysis of global breast cancer incidence and mortality among premenopausal and postmenopausal women. Menopausal status was defined using age as a proxy, whereby breast cancer cases or deaths at age 50 years or older were regarded as postmenopausal. Age-standardised breast cancer incidence and mortality in 2018 were calculated using GLOBOCAN data. Incidence trends for 1998-2012 were assessed in 44 populations from 41 countries using the Cancer in Five Continents plus database, by calculating the annual average percent change. FINDINGS: Approximately 645 000 premenopausal and 1·4 million postmenopausal breast cancer cases were diagnosed worldwide in 2018, with more than 130 000 and 490 000 deaths occurring in each menopausal group, respectively. Proportionally, countries with a low UNDP human development index (HDI) faced a greater burden of premenopausal breast cancer for both new cases and deaths compared with higher income countries. Countries with a very high HDI had the highest premenopausal and postmenopausal breast cancer incidence (30·6 and 253·6 cases per 100 000, respectively), whereas countries with low and medium HDI had the highest premenopausal and postmenopausal mortality, respectively (8·5 and 53·3 deaths per 100 000, respectively). When examining breast cancer trends, we noted significantly increasing age-standardised incidence rates (ASIRs) for premenopausal breast cancer in 20 of 44 populations and significantly increasing ASIRs for postmenopausal breast cancer in 24 of 44 populations. The growth exclusively at premenopausal ages largely occurred in high-income countries, whereas the increasing postmenopausal breast cancer burden was most notable in countries under transition. INTERPRETATION: We provide evidence of a rising burden of both premenopausal and postmenopausal breast cancer worldwide. Although early diagnosis and access to treatment remain crucial in low-income and middle-income countries, primary prevention efforts seeking to decrease exposure to known breast cancer risk factors are warranted in all world regions to curb the future breast cancer burden. FUNDING: None.
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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 | 0.001 | 0.004 |
| 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) | 0.001 | 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".