Breast Cancer in Europe: Epidemiology, Risk Factors, Policies and Strategies. A Literature Review
Bibliographic record
Abstract
INTRODUCTION: Breast cancer is the most commonly diagnosed cancer in European women, accounting for 28.8% of the total number of cancer diagnosis in Europe. Over the past few decades, several factors have led to remarkable improvements in overall breast cancer outcomes in Europe, such as early detection, screening, and progresses in treatment. However, breast cancer remains the most common cancer in females in Europe, yet more there is an increase in the incidence and mortality rates in some European countries. This research summarizes the current status of breast cancer in Europe, including epidemiology, risk factors, policies, and strategies. The results can help to better identify this health problem, which could help in developing better breast cancer control or prevention strategies and policies. METHODS: A literature review was conducted using five electronic databases (PubMed, Google Scholar, ProQuest, Eurostat, BioMed) and the European Commission reports for studies that reported on epidemiology and risk factors of breast cancer, or policies and strategies that target it in Europe. In addition, the Social Ecological Model (SEM) was used in categorizing the breast cancer risk factors into different levels. RESULTS: Europe and North America have the highest rate of breast cancer globally. Incidence and prevalence of breast cancer vary across Europe, and so does its mortality rates. Breast cancer was found to be associated with a number of risk factors, including but not limited to aging, use of hormone therapy, older age at the first birth, and smoking which are common in Europe. Cancer in general, and breast cancer have attracted specific attention, and the European Union has focused many of the resources and expertise on the topic. CONCLUSION & RECOMMENDATIONS: Breast cancer remains a major public health issue in Europe, despite the efforts and actions being taken. Therefore, current efforts against breast cancer in Europe need to be revisited and improved.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".