Trends and Projections of the Prevalence of Diabetes Mellitus in Pregnancy and Fetal–Neonatal Metabolic Disorders, 2010–2035: A Nationwide Population-Based Study from Hungary
Bibliographic record
Abstract
Objectives: Diabetes in pregnancy represents a significant public health concern with established impacts on both maternal and fetal health outcomes. Our aim was to evaluate the epidemiology of diabetes mellitus in pregnancy (DMP) and specific fetal and neonatal transient metabolic disorders (FNTMDs) in Hungary between 2010 and 2024, as well as to project future trends through to 2035. Methods: We carried out a quantitative, retrospective study using nationwide real-world data from the Hungarian ‘Pulvita’ Health Data Warehouse. ICD-10 codes O24.0–O24.9 (DMP) and P70.0–P70.9 (FNTMDs) were included. Annual patient numbers, the number of hospital days, and the number of DMP patients per 1000 women aged 15–49, as well as the number of FNTMD patients per 1000 live births, were analyzed with joinpoint regression analysis and different forecasting models to project future prevalence up to 2035. Results: Despite a 14.2% decrease in live births, DMP cases increased significantly (54.9% inpatient, 26.6% outpatient), with GDM incidence per thousand reproductive-age women rising by 85.7%. FNTMD cases showed similar trends, with GDM-related infant syndromes more than doubling (154% increase). Projections indicate that DMP prevalence could reach 4.60 per 1000 reproductive-age women by 2035, while FNTMD cases show varying trends between inpatient (increasing) and outpatient (stabilizing) care. Conclusions: These findings demonstrate a continuing upward trend in diabetes-related pregnancy complications, despite shorter hospital stays, suggesting an urgent need for enhanced preventive programs and specialized care service planning.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".