The effect of changing screening practices and demographics on the incidence of gestational diabetes in British Columbia, 2005–2019
Bibliographic record
Abstract
BACKGROUND: Rates of gestational diabetes are reported to be increasing in many jurisdictions, but the reasons for this are poorly understood. We sought to evaluate the relative contribution of screening practices for gestational diabetes (including completion and methods of screening) and population characteristics to risk of gestational diabetes in British Columbia, Canada, from 2005 to 2019. METHODS: We used a population-based cohort from a provincial registry of perinatal data, linked to laboratory billing records. We used data on screening completion, screening method (1-step 75-g glucose test or 2-step approach of 50-g glucose screening test, followed by a diagnostic test for patients who screen positive) and demographic risk factors. We modelled predicted annual risk for gestational diabetes, sequentially adjusted for screening completion, screening method and risk factors. RESULTS: We included 551 457 pregnancies in the study cohort. The incidence of gestational diabetes more than doubled over the study period, from 7.2% in 2005 to 14.7% in 2019. Screening completion increased from 87.2% in 2005 to 95.5% in 2019. Use of 1-step screening methods increased from 0.0% in 2005 to 39.5% in 2019 among those who were screened. Unadjusted models estimated a 2.04 (95% confidence interval [CI] 1.94-2.13) increased risk of gestational diabetes in 2019 (v. 2005). This increase was 1.89 (95% CI 1.81-1.98) after accounting for the rise in screening completion and 1.34 (95% CI 1.28-1.40) after accounting for changes in screening methods. Further accounting for demographic risk factors (e.g., age, body mass index, prenatal care) had a small impact (increase of 1.25, 95% CI 1.19-1.31). INTERPRETATION: Most of the observed increase in the incidence of gestational diabetes was attributable to changes in screening practices (primarily changes in screening methods) rather than changing population factors. Our findings highlight the importance of understanding variation in screening practices when monitoring incidence rates for gestational diabetes.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".