Trends in rate of hypertensive disorders of pregnancy and associated morbidities in Canada: a population-based study (2012–2021)
Bibliographic record
Abstract
BACKGROUND: Hypertensive disorders of pregnancy (HDP) are a leading cause of severe maternal morbidity (SMM). We sought to explore trends in HDP and related morbidity outcomes in Canada. METHODS: In this retrospective population-based study, we used hospital discharge data from Canada, excluding Quebec, to identify females who had an HDP diagnosis during a birth admission between 2012 and 2021. We analyzed temporal and geographical trends in HDP, as well as temporal trends in adverse outcomes associated with HDP. RESULTS: Among 2 804 473 hospital admissions for birth between 2012 and 2021, the rate of any HDP increased from 6.1% to 8.5%, including pre-existing hypertension (0.6% to 0.9%), gestational hypertension (3.9% to 5.1%), and preeclampsia (1.6% to 2.6%). For 2017-2021 combined, relative to Ontario (6.9%), HDP were significantly more prevalent in nearly all other Canadian regions. For example, in Newfoundland and Labrador, the rate was 10.7% (unadjusted rate ratio 1.56, 95% confidence interval 1.49-1.63). Among females with any HDP, rates of cesarean delivery rose from 42.0% in 2012 to 44.3% in 2021, as did acute renal failure (0.4% to 0.6%), while rates of early preterm delivery, intrauterine fetal death, maternal hospital length of stay (≥ 7 d), admission to the maternal intensive care unit, severe hemorrhage, and SMM trended downward. INTERPRETATION: The rate of HDP has risen across Canada, with a concomitant decline in some HDP-associated morbidities. Ongoing surveillance of HDP is needed to assess the factors associated with temporal trends, including the effectiveness of evolving HDP prevention and management efforts.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".