Global, Regional and National Burden of Cancers Attributable to High Fasting Plasma Glucose in 204 Countries and Territories, 1990-2019
Bibliographic record
Abstract
Background: To report the burden of cancers attributable to high fasting plasma glucose (HFPG) by sex, age, location, cancer type and Socio-demographic Index (SDI) over the period 1990 to 2019 for 204 countries and territories. Methods: Using the Comparative Risk Assessment approach of Global Burden of Disease (GBD) study 2019, the burden of cancers attributable to HFPG was reported in 1990 and 2019. Results: Globally, in 2019 there were an estimated 419.3 thousand cancer deaths (95% UI: 115.7 to 848.5) and 8.6 million cancer DALYs (2.4 to 17.6) attributable to HFPG. By sex, 4.6 (1.1 to 9.9) and 4.0 (1.1 to 8.4) million global cancer DALYs were attributable to HFPG in men and women, respectively. The global age-standardized death and DALY rates of cancers attributable to HFPG (per 100,000) have increased by 27.8% (20.5 to 38.7%) and 24.5% (16.4 to 35.6%), respectively, since 1990. High-income North America (9.5 [2.7 to 18.8]) and Eastern Sub-Saharan Africa (2.0 [0.5 to 4.2]) had the highest and lowest regional age-standardized death rates, respectively, for cancers attributable to HFPG. In 2019, the global number of attributable cancer DALYs were highest in 65-69 age group. Moreover, there was an overall positive association between SDI and the regional age-standardized DALY rate for HFPG-attributable cancers. Conclusions: HFPG was associated with more burden in 2019. Preventive programs for diabetes and screening of individuals with diabetes for cancers, especially in older males living in developed countries, are required to arrest the large increases in HFPG-attributable cancers.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".