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National trends in prevalence of pancreatic cancer in adults aged 70 and older: A global burden of disease analysis (1990–2021).

2025· article· en· W4410815607 on OpenAlexaboutno aff
Salman Ayub Jajja, Muhammad Shaheer Mannan, Manzoor Mahmood, Mian Zahid Jan Kakakhel, Abdul Qadeer, Muneeb Khawar, Husnain Ahmad, Muhammad Faizan Ali, Jibran Ikram, Faizan Ahmed, Abdul Subhan Talpur, Robert W. Kirchoff

Bibliographic record

VenueJournal of Clinical Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicPancreatic and Hepatic Oncology Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePancreatic cancerDiseaseCancerGerontologyDemographyEnvironmental healthInternal medicine

Abstract

fetched live from OpenAlex

e16418 Background: Pancreatic cancer remains one of the most fatal malignancies globally, posing a significant burden on older populations. Despite numerous reports on its prevalence, a detailed comparative analysis of national trends among adults aged 70 and older from 1990 to 2021 is scarce. This study seeks to bridge this gap by evaluating prevalence rates using data from the Global Burden of Disease (GBD) study. Methods: Prevalence data for pancreatic cancer in individuals aged 70 years and older were obtained from the GBD-2021 dataset, encompassing 204 countries and territories for the years 1990 and 2021. The Estimated Annual Percent Change (EAPC) and corresponding 95% confidence intervals (CI) were calculated to assess trends over the period from 1990 to 2021. Results: The prevalence of pancreatic cancer has shown a significant increase since 1990, reaching its highest levels in 2021. In 1990, the United States of America (USA) reported the highest prevalence, followed by Japan and China. However, by 2021, Japan ranked first in prevalence, followed by China and the USA. The analysis revealed that in 1990, the highest prevalence rates of pancreatic cancer were observed in Greenland (91.04 per 100,000), followed by Finland (82.57 per 100,000) and Japan (76.30 per 100,000). By 2021, Japan reported the highest prevalence (126.73 per 100,000), followed by Finland (119.20 per 100,000) and Canada (109.22 per 100,000). Despite the observed prevalence rates from 1990 to 2021, Turkmenistan exhibited the most significant increase (EAPC: 15.41, 95% CI: 10.91, 21.41), followed by the Republic of Cabo Verde (EAPC: 13.61, 95% CI: 8.67, 19.59) and Mongolia (EAPC: 4.81, 95% CI: 3.11, 7.20). In contrast, the most significant declines were recorded in the Republic of San Marino (EAPC: -0.308, 95% CI: -0.51, -0.03), followed by Guam (EAPC: -0.24, 95% CI: -0.37, -0.08) and Cuba (EAPC: -0.21, 95% CI: -0.33, -0.09). Conclusions: The prevalence of pancreatic cancer among adults aged 70 and older has increased significantly over the past three decades, with distinct regional patterns. Countries like Japan, China, and the USA continue to report the highest prevalence rates. These findings highlight the urgent need for targeted prevention, screening, and treatment strategies, particularly in high-burden regions, to mitigate the growing impact of pancreatic cancer in aging populations. Prevalence percent changes. Metric Region 1990 2021 EAPC with 95% CI (1990-2021) Highest prevalence percent change Turkmenistan 0.62 10.16 15.41 (10.91, 21.41) Republic of Cabo Verde 3.23 47.18 13.61 (8.67, 19.59) Mongolia 5.25 30.53 4.81 (3.11, 7.20) Lowest prevalence percent change Republic of San Marino 52.61 36.36 -0.308 (-0.51, -0.03) Guam 16.96 12.87 -0.24 (-0.37, -0.08) Cuba 32.65 25.52 -0.21 (-0.33, -0.09)

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.084

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.004
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.063
GPT teacher head0.505
Teacher spread0.442 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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