SAT-568 National Trends in the Incidence of Chronic Kidney Disease Due to Type 1 Diabetes Among Adults Aged 55 and Older Across 204 Countries (1990-2021): A Global Burden Of Disease Study
Bibliographic record
Abstract
Abstract Disclosure: M. Munir: None. G. Flocco: None. H. Ahmad: None. M. Kakakhel: None. F. Ahmed: None. M. Ali: None. A. Zahid: None. J. Ikram: None. A. Cheema: None. Over the last 30 years, significant population growth, aging, and shifts in epidemiologic trends have likely influenced the burden of chronic kidney disease (CKD) due to type 1 diabetes mellitus (T1DM). In this study, we utilized data and methodologies from the Global Burden of Disease (GBD) Study to examine changes in the burden of CKD due to T1DM from 1990 to 2021, focusing on incidence rates in adults aged 55 and older across 204 nations. GBD study data were analyzed to assess changes in the incidence rates of CKD due to T1DM. Estimated Annual Percent Changes (EAPCs) were calculated to capture national trends in adults aged 55 and older from 1990 to 2021, with corresponding 95% confidence intervals (CIs). The incidence of CKD due to T1DM has increased dramatically from 1990 to 2021. In 2021, the United States of America (USA) reported the highest number of new cases, followed by India and China. Notably, China surpassed Germany, which ranked fourth in 2021, marking a shift from the 1990 distribution of cases. The analysis revealed significant shifts in the incidence of CKD attributable to T1DM across nations over the past three decades. In 1990, Canada reported the highest incidence rate (3.92 per 100,000), but by 2021, Ireland emerged at the forefront with an incidence of 5.09 per 100,000, while Canada dropped to 4th place (3.90 per 100,000). Finland (4.41 per 100,000) and Austria (4.29 per 100,000) followed closely. In contrast, the lowest incidence rates in 2021 were observed in Papua New Guinea (0.19 per 100,000), followed by Tokelau (0.19 per 100,000) and Nauru (0.21 per 100,000), highlighting the marked global disparities in the burden of CKD due to T1DM. Despite the observed incidence rates from 1990 to 2021, Cyprus exhibited the most significant increase in incidence of CKD due to T1DM (EAPC: 3.32, 95% CI: 4.31, 2.47), followed by Hungary (EAPC: 3.01, 95% CI: 4.04, 2.20) and Slovakia (EAPC: 2.72, 95% CI: 3.98, 1.94). In contrast, 14 nations experienced declining incidence rates, with the most notable reductions occurring in Greenland (EAPC: -0.31, 95% CI: -0.16, -0.44), followed by Timor-Leste (EAPC: -0.31, 95% CI: -0.13, -0.44) and Egypt (EAPC: -0.19, 95% CI: -0.005, -0.32). The incidence of CKD due to T1DM has risen sharply in countries like Cyprus, Hungary, and Slovakia, while regions such as Greenland, Timor-Leste, and Egypt have experienced significant decreases. These changes point to varying trends in disease burden worldwide, emphasizing the importance of region-specific healthcare approaches to address this growing challenge. Presentation: Saturday, July 12, 2025
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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 | 0.002 | 0.003 |
| 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) | 0.002 | 0.001 |
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.
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".