Temporal changes in <scp>pre‐existing</scp> health conditions five years prior to pregnancy in British Columbia, Canada, 2000–2019
Bibliographic record
Abstract
BACKGROUND: Pre-existing health conditions increase the risk of obstetric complications during pregnancy and birth. However, the prevalence and recent changes in the frequency of pre-existing health conditions in the childbearing population remain unknown. OBJECTIVES: To estimate the temporal changes in the prevalence of pre-existing health conditions among pregnant women in British Columbia, Canada. METHODS: We carried out a population-based cross-sectional study of 825,203 deliveries in BC between 2000 and 2019 and examined 17 categories of physical and psychiatric health conditions recorded within 5 years before childbirth. We also undertook age-period-cohort analyses to evaluate temporal changes in pre-existing health conditions. RESULTS: The prevalence of any pre-existing health condition was 26.2% (n = 216,214) with overall trends remaining stable during the study period. Between 2000 and 2019, the prevalence rates of anxiety (5.6%-9.6%), bipolar (1.6%-3.4%), psychosis (0.7%-0.8%), and eating disorders (0.2%-0.3%) increased. The prevalence of hypertension increased sharply from 0.06% in 2000 to 0.3% in 2019. Diabetes mellitus and stroke rates increased, as did the prevalence of systemic lupus, multiple sclerosis, and chronic kidney disease. Advanced maternal age was strongly associated with both psychiatric and circulatory/metabolic conditions. A strong birth cohort effect was evident, with rates of psychiatric conditions increasing among women born after 1985. CONCLUSIONS: In British Columbia, Canada, 1 in 4 mothers had a pre-existing health condition 5 years prior to pregnancy. These findings underscore the need for multi-disciplinary care for women with pre-existing health conditions to improve maternal, foetal, and infant health.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".