Trends in Hospital Abortion During the First 2 Years of <scp>COVID</scp> ‐19 in Quebec, Canada: Results From a Population‐Based Observational Study
Bibliographic record
Abstract
PURPOSE: We investigated the extent to which the COVID-19 pandemic affected hospital abortion rates in a Canadian setting. METHODS: We obtained all abortions between 2012 and 2022 from hospital discharge data in the Maintenance and Use of Data for the Study of Hospital Clientele database for Quebec, Canada. The exposure was the pandemic (March 2020 to March 2022) compared with the preceding period (January 2012 to February 2020). The outcome included hospital-based abortions versus other pregnancy admissions. We examined if the pandemic affected monthly hospital abortion rates using interrupted time series regression. We estimated risk ratios (RRs) and 95% confidence intervals (CIs) for the association between the pandemic and hospital-based abortions, accounting for pandemic wave, abortion method, gestational age, and other patient characteristics. RESULTS: There were 21,675 hospital abortions in total, including 4177 (19.3%) during the pandemic. Abortion rates decreased by 1.2 per 1000 pregnancies the first month of the pandemic and continued to decrease by 0.15 per 1000 every month thereafter. Compared with the preceding year, patients were less likely to have a hospital abortion anytime during the pandemic (RR 0.88, 95% CI 0.84-0.93), particularly during the third (RR 0.81, 95% CI 0.74-0.88) and fourth (RR 0.82, 95% CI 0.75-0.89) waves. The decrease was most apparent for instrumentation abortions, abortions before 14 weeks of gestation, and abortions among patients aged ≥ 40 years or who were socioeconomically advantaged. CONCLUSIONS: The pandemic was associated with fewer hospital abortions before 14 weeks, as well as among older or socioeconomically advantaged patients.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".