1504-P: National Trends in Incidence of Type 1 Diabetes Mellitus among Adolescents and Young Adults (15–39y) across 204 Countries (1990–2021)
Bibliographic record
Abstract
Introduction and Objective: The global burden of non-communicable diseases continues to rise, with type 1 diabetes mellitus (T1DM)—a predominantly autoimmune condition—emerging as a significant public health concern. This study leverages data from the Global Burden of Diseases (GBD) Study-2021 to assess trends in T1DM incidence across 204 countries from 1990 to 2021. Methods: T1DM incidence data for 1990 and 2021 were extracted for all countries included in the GBD-2021 database. Trends were analyzed by calculating the estimated annual percentage change (EAPC) and corresponding 95% confidence intervals (CIs). Results: In 2021, Finland reported the highest T1DM incidence among the 15-39 years cohort (44.9 cases per 100,000 population), followed by Canada and Italy. European and North American nations consistently ranked highest in both 1990 and 2021. Cyprus exhibited the largest increase in incidence (EAPC: 1.52, 95% CI: 1.31-1.75), followed by South Korea and Argentina. Conversely, 24 nations demonstrated a decline in T1DM incidence, with the Maldives showing the steepest reduction (EAPC: -0.13, 95% CI: -0.34-0.16). Conclusion: The persistent rise in T1DM incidence, particularly in Western countries, underscores the growing burden of autoimmune diseases. Further research is essential to identify the drivers of these trends and inform effective prevention and management strategies. Disclosure H. Ahmad: None. M. Kakakhel: None. S. Rath: None. A. Qadeer: None. A. Khan: None. M.Z. Haider: None. R.W. Kirchoff: None.
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 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".