Time trends in preeclampsia and gestational diabetes in Denmark and Alberta, Canada, 2005–2018—A population‐based cohort study
Bibliographic record
Abstract
INTRODUCTION: Preeclampsia and gestational diabetes mellitus share risk factors such as obesity and increased maternal age, which have become more prevalent in recent decades. We examined changes in the prevalence of preeclampsia and gestational diabetes between 2005 and 2018 in Denmark and Alberta, Canada, and investigated whether the observed trends can be explained by changes in maternal age, parity, multiple pregnancy, comorbidity, and body mass index (BMI) over time. MATERIAL AND METHODS: This study was a register-based cohort study conducted using data from the Danish National Health Registers and the provincial health registers of Alberta, Canada. We included in the study cohort all pregnancies in 2005-2018 resulting in live-born infants and used binomial regression to estimate mean annual increases in the prevalence of preeclampsia and gestational diabetes in the two populations across the study period, adjusted for maternal characteristics. RESULTS: The study cohorts included 846 127 (Denmark) and 706 728 (Alberta) pregnancies. The prevalence of preeclampsia increased over the study period in Denmark (2.5% to 2.9%) and Alberta (1.7% to 2.5%), with mean annual increases of 0.03 (95% confidence interval [CI] 0.02-0.04) and 0.06 (95% CI 0.05-0.07) percentage points, respectively. The prevalence of gestational diabetes also increased in Denmark (1.9% to 4.6%) and Alberta (3.9% to 9.2%), with average annual increases of 0.20 (95% CI 0.19-0.21) and 0.44 (95% CI 0.42-0.45) percentage points. Changes in the distributions of maternal age and BMI contributed to increases in the prevalence of both conditions but could not explain them entirely. CONCLUSIONS: The prevalence of both preeclampsia and gestational diabetes increased significantly from 2005 to 2018, which portends future increases in chronic disease rates among affected women. Increasing demand for long-term follow up and care will amplify the existing pressure on healthcare systems.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".