Patterns of antidepressant prescribing in and around pregnancy: a descriptive analysis in the UK Clinical Practice Research Datalink
Bibliographic record
Abstract
Abstract Objective To describe the prevalence and patterns of antidepressant prescribing in and around pregnancy. Design Drug utilisation study. Setting Primary care in the United Kingdom (UK). Population Women with a pregnancy between 1996 and 2018 in the UK Clinical Practice Research Datalink (CPRD) GOLD Pregnancy Register. Methods Using primary care prescription records, we identified individuals who had been prescribed antidepressants in and around pregnancy and described changing prevalence of prescribing during pregnancy over time. We defined ‘prevalent’ or ‘incident’ antidepressant use, where ‘prevalent’ users were prescribed antidepressants both before and during pregnancy, and ‘incident’ users were newly prescribed antidepressants during pregnancy, then compared patterns of prescribing between these two groups. We also investigated characteristics associated with antidepressant discontinuation anytime during pregnancy and post-pregnancy prescribing. Main outcome measures Antidepressant prescribing during pregnancy. Results A total of 1,033,783 pregnancies were identified: 79,144 (7.7%) were prescribed antidepressants during pregnancy and 15,733 of these (19.9%) were ‘incident’ users. Antidepressant prescribing during pregnancy increased from 3.2% in 1996 to 13.4% in 2018. Most women, both ‘prevalent’ and ‘incident’ users, discontinued antidepressants anytime during pregnancy (54.8% and 59.9%, respectively). The majority of those who discontinued during pregnancy resumed in the 12 months after pregnancy (53.0%). Younger age, previous stillbirth, and higher deprivation were associated with more frequent discontinuation anytime during pregnancy. Conclusions Antidepressant use during pregnancy appears to be increasing in the UK. Most women discontinued antidepressants at some point before the end of pregnancy, but post-pregnancy resumption of antidepressants was common. Funding Wellcome Trust 218495/Z/19/Z.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".