Medications used among Non-Hospitalized Pregnant Women with COVID-19: a Prospective Individual Patient Data Meta-analysis in Europe and North America
Bibliographic record
Abstract
ABSTRACT Aim To estimate the prevalence of medication use in non-hospitalized pregnant women with COVID-19 Methods A prospective two-stage individual patient meta-analysis across 10 data sources in Europe and North America studied medication use among non-hospitalized pregnant women with COVID-19 between January 2020 to December 2022. Comparisons were made between medication use within 30 days pre- and post-COVID-19 diagnosis in this cohort and two comparator groups: pregnant women without COVID-19, and non-pregnant women with COVID-19. Prevalence estimates were pooled using a random-effects model stratified by trimester. Results 50,335 non-hospitalized pregnant women with COVID-19 were identified. The pooled prevalence of antibacterial use in 3 rd trimester was higher post-COVID-19 diagnosis (6.8%, 95%CI 5.5-8.4, I 2 =94%) compared with the same women pre-COVID-19 (3.9%, 95%CI 3.1-4.9, I 2 =89%). Overall, pregnant women with COVID-19 had higher medication use compared to pregnant women without COVID-19, although these differences were not statistically significant. Post-COVID-19, antithrombotics prevalence was 4.5% (95%CI 1.1-16.5, I 2 =100%) among pregnant women with COVID-19 in 3 rd trimester, compared to 2.1% (95%CI 1.2-3.6, I 2 =99%) among those without COVID-19 in 3 rd trimester. Compared to non-pregnant women with COVID-19, pregnant women with COVID-19 were less likely to be prescribed analgesics, antiprotozoals, corticosteroids, psychoanaleptics and psycholeptics, and more likely to be prescribed antithrombotics, cough and cold and nasal preparations across all trimesters. High heterogeneity existed in nearly all analyses. Conclusion This international meta-analysis reveals low medication use and country-specific variations, enhancing insight into the management of COVID-19 in non-hospitalized pregnant women. Higher antithrombotics use post-COVID-19 suggests prophylactic treatment in this population.
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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 | 0.014 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.050 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".