Antibiotics and Preterm Delivery: The Prevalent New-user Cohort Design to Resolve Immortal Time Bias
Bibliographic record
Abstract
BACKGROUND: Observational studies of the association between antibiotics and preterm delivery report conflicting findings, with some potentially affected by immortal time bias. We assessed the effects of third-trimester antibiotic use on preterm delivery and low birthweight, using a study design that accounts for immortal time bias. METHODS: We used the UK's Clinical Practice Research Datalink to identify pregnant females aged 12-50, over the period 2002 to 2016, reaching 27 weeks of gestation without antibiotic use until that point. We applied the prevalent new-user design, matching each third-trimester antibiotic initiator with a reference nonuser at the same gestational day, using time-conditional propensity scores. The 2 matched groups were compared on the incidence of preterm delivery and low birthweight. The full cohort was also analyzed with antibiotic use considered as time-fixed and time-varying exposures. RESULTS: The cohort included 207,027 pregnancies, with 16,865 initiating antibiotics matched to 16,865 nonusers. The hazard ratio (HR) of preterm delivery with third-trimester antibiotic use was 1.14 [95% confidence interval (CI): 1.04, 1.24], compared with nonuse. With time-fixed exposure, subject to immortal time bias, the HR was 0.78 (95% CI: 0.73, 0.83), while with time-varying exposure, the HR was 1.23 (95% CI: 1.16, 1.32). The HR of low birthweight with antibiotic initiation was 1.07 (95% CI: 0.93, 1.25) compared with 0.91 (95% CI: 0.83, 1.00) under the time-fixed approach. CONCLUSIONS: Using the prevalent new-user design, which emulates a randomized trial, antibiotic use late in pregnancy was associated with a modestly increased incidence of preterm delivery. Previous inconclusive studies may have resulted from observational methods that introduced, or insufficiently addressed, immortal time bias.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".