Evidence map›Paper›PMID 42189833›Full record

ArticlePloS one2026

Medicines prescribed in pregnancy: Protocol for a signal detection study using routinely collected data in England.

Liza Bowen, Hannah Johnson, Iain M Carey, Tess Harris, Emma Baker, Derek G Cook, Pensee Wu, Michael R Perkin, Alex Barr, Joan Morris

Abstract read
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Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Liza BowenDepartment of Population Health and Policy, City St George's University of London, London, United Kingdom.ORCID https://orcid.org/0000-0001-8318-8421
Hannah JohnsonDepartment of Population Health and Policy, City St George's University of London, London, United Kingdom.ORCID https://orcid.org/0000-0002-3055-769X
Iain M CareyDepartment of Population Health and Policy, City St George's University of London, London, United Kingdom.ORCID https://orcid.org/0000-0003-1099-8460
Tess HarrisDepartment of Population Health and Policy, City St George's University of London, London, United Kingdom.
Emma BakerDepartment of Population Health and Policy, City St George's University of London, London, United Kingdom.
Derek G CookDepartment of Population Health and Policy, City St George's University of London, London, United Kingdom.
Pensee WuSchool of Medicine, Keele University, Keele, United Kingdom.ORCID https://orcid.org/0000-0003-0011-5636
Michael R PerkinDepartment of Population Health and Policy, City St George's University of London, London, United Kingdom.ORCID https://orcid.org/0000-0001-9272-2585
Alex BarrPublic contributor.
Joan MorrisDepartment of Population Health and Policy, City St George's University of London, London, United Kingdom.ORCID https://orcid.org/0000-0002-7164-612X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionMedication use in pregnancy is common and increasing. Pregnant women are often excluded from drug trials, therefore the safety to mother and baby of many medications used in pregnancy is unknown. Routine health data have been used to look for evidence of harm and safety of specific medications, but not to systematically look across all prescribed medications in pregnancy.

aimsTo systematically identify medications prescribed during pregnancy requiring further research into their potential harms to mother or baby.

methodsWe will describe trends in primary care prescribing in pregnancy over time, and socio-demographic patterning of prescribing, using data from pregnancies in the Clinical Practice Research Datalink (CPRD). We will identify and categorise maternal, fetal, and infant adverse outcomes reported in the CPRD and linked hospital and mortality data. We will use Bayesian signal detection models to analyse all medication/outcome pairs to identify where an outcome occurs significantly more frequently in mothers prescribed a specific medication than in mothers not prescribed it. Potential confounders will not be accounted for at this stage. Published evidence on the identified medication/outcome pairs will be reviewed and incorporated with the results from trends and signal detection analyses. These data will be used by Patient Involvement and Study Advisory Board workshops to prioritise medications for further research, with medications more commonly prescribed for underserved groups being given precedence. For three prioritised signals, cohort analyses will compare the occurrence of adverse outcomes in those prescribed compared to those not prescribed the medication of interest, adjusting for maternal morbidity, co-medications, and other measured confounders. DISCUSSION: This project will provide a list of medications used in pregnancy that should be prioritised for further safety research. Results of three detailed retrospective cohort studies will contribute to informed decision making for patients and clinicians. Study grant number: NIHR207172; CPRD accepted protocol number: 24_004518.

Indexed as

Drug PrescriptionsPrescription DrugsBayes TheoremEnglandFemaleHumansPregnancyRoutinely Collected Health DataPrescription Drugs

Identifiers

PMID42189833
PMCPMC13210384

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.