Trends in disease burden from prostate cancer amongst different regions of the world and extensively the European Union 15+ countries, from 1990 to 2019: Estimates from the Global Burden of Disease study.
Bibliographic record
Abstract
187 Background: Prostate cancer was the third most commonly diagnosed cancer in 2020 and the fifth leading cause of cancer mortality worldwide. Global variations in the burden of prostate cancer have been observed in the past decades. This study aimed to assess the trends in incidence, mortality, and disability-adjusted life years from prostate cancer in the World Health Organization (WHO) regions and extensively in the European Union (EU) 15+ countries from 1990 to 2019. Methods: The Global Burden of Disease Study database was used to extract the mortality data of prostate cancer, based on the International Classification of Diseases versions 10 and 9. Data acquired for different WHO regions and each country of the EU15+ nations, per year from 1990 to 2019, included age-standardized incidence rates (ASIR), age-standardized death rates (ASDR), and disability-adjusted life years (DALYs). Mortality-to-incidence ratios (MIR) were then computed. Trends were assessed using Joinpoint regression. Results: Between 1990 and 2019, ASIRs increased worldwide (+13.16%), except in the American region, with the largest growth in the Eastern Mediterranean Region (EMR) (+72.43%). While ASDRs and DALYs increased in Africa, South-East Asia, and EMR, they decreased in the Americas, Europe, and Western Pacific Region (WPR). MIRs decreased universally (-25.52%), mainly in the WPR (-43.49%). In EU15+ countries, ASIRs increased in all countries, except for Canada (-11.27%) and the United States of America (USA) (-10.37%), with the largest rise in Finland (+75.11%). ASDRs decreased in all countries, with the widest drop in Luxembourg (-41.89%) and the lowest in Denmark (-13.96%). MIRs decreased in all countries, with the highest drop in Finland (-55.37%). Similarly, DALYs decreased in all countries, with the highest decrease in Luxembourg (-40.56%). In 2019, the highest ASIR was in the USA (118.24/100,000), whereas Denmark had the highest ASDR (31.35/100,000), MIR (0.38/100,000), and DALY (498.03/100,000). Conclusions: Over the 30 years, the incidence of prostate cancer has been rising worldwide, excluding Canada and the USA. However, the universal decrease of MIRs highlights improved outcomes and efficient screening and therapeutic strategies. All indices are represented per 100,000 population.[Table: see text]
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 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".