Identification of Risk-Related Prenatal Medication Exposure Profiles for Childhood Asthma Using the Bayesian Profile Regression: A Cohort Study Using the POMME Cohort.
- 1CERPOP, Inserm U1295, Toulouse, France.
- 2CERPOP, Inserm U1295, Université de Toulouse, Service de Pharmacologie Clinique, CHU de Toulouse, Toulouse, France.
Abstract
Purpose
To identify prenatal medication exposure profiles associated with childhood asthma.Methods
This population-based cohort study used data from the POMME (PrescriptiOn Médicaments Mères Enfants) cohort, including 18 851 children born in the Haute-Garonne area (France) in 2010 and 2015, and followed until 2023. Prenatal exposure was defined as medications dispensed to mothers during pregnancy, classified by Anatomical Therapeutic Chemical subclasses, assessed separately for each trimester as none, one dispensing, or at least two dispensings. Childhood asthma from Age 4 years onward was identified using an algorithm based on dispensing of asthma medications. Bayesian profile regression was applied to simultaneously cluster children according to prenatal medication exposure profiles and asthma occurrence. High-risk profiles were characterised by predominant medication classes, timing of exposure and maternal and infant characteristics.Results
Seventeen prenatal medication exposure profiles were identified. Six profiles were associated with a higher average risk of childhood asthma compared with the overall population risk (15.2%). High-risk profiles were characterised by repeated exposure throughout pregnancy to medications for acid-related disorders, thyroid hormones, antibiotics, asthma medications and antihistamines, as well as by combinations involving antibiotics, nasal medications and cold medicines in late pregnancy. Mothers in high-risk profiles more frequently presented chronic conditions and pregnancy-related complications.Conclusion
This exploratory study identified prenatal medication exposure profiles associated with childhood asthma using a semi-supervised Bayesian approach. This method highlights combinations and critical windows that may warrant further investigation and supports Bayesian profile regression as a useful tool for signal detection in perinatal pharmacoepidemiology.
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BibTeX
@article{russo2026identification,
title = {{Identification of Risk-Related Prenatal Medication Exposure Profiles for Childhood Asthma Using the Bayesian Profile Regression: A Cohort Study Using the POMME Cohort.}},
author = {Solène Russo and Justine Bénévent and Anna-Belle Beau},
journal = {Pharmacoepidemiology and drug safety},
year = {2026},
month = oct,
doi = {10.1002/pds.70509},
url = {https://europepmc.org/article/MED/42896866},
note = {CC BY},
}
RIS
TY - JOUR
TI - Identification of Risk-Related Prenatal Medication Exposure Profiles for Childhood Asthma Using the Bayesian Profile Regression: A Cohort Study Using the POMME Cohort.
AU - Solène Russo
AU - Justine Bénévent
AU - Anna-Belle Beau
PY - 2026
DA - 2026/10/30
JO - Pharmacoepidemiology and drug safety
DO - 10.1002/pds.70509
UR - https://europepmc.org/article/MED/42896866
AB - Purpose To identify prenatal medication exposure profiles associated with childhood asthma.Methods This population-based cohort study used data from the POMME (PrescriptiOn Médicaments Mères Enfants) cohort, including 18 851 children born in the Haute-Garonne area (France) in 2010 and 2015, and followed until 2023. Prenatal exposure was defined as medications dispensed to mothers during pregnancy, classified by Anatomical Therapeutic Chemical subclasses, assessed separately for each trimester as none, one dispensing, or at least two dispensings. Childhood asthma from Age 4 years onward was identified using an algorithm based on dispensing of asthma medications. Bayesian profile regression was applied to simultaneously cluster children according to prenatal medication exposure profiles and asthma occurrence. High-risk profiles were characterised by predominant medication classes, timing of exposure and maternal and infant characteristics.Results Seventeen prenatal medication exposure profiles were identified. Six profiles were associated with a higher average risk of childhood asthma compared with the overall population risk (15.2%). High-risk profiles were characterised by repeated exposure throughout pregnancy to medications for acid-related disorders, thyroid hormones, antibiotics, asthma medications and antihistamines, as well as by combinations involving antibiotics, nasal medications and cold medicines in late pregnancy. Mothers in high-risk profiles more frequently presented chronic conditions and pregnancy-related complications.Conclusion This exploratory study identified prenatal medication exposure profiles associated with childhood asthma using a semi-supervised Bayesian approach. This method highlights combinations and critical windows that may warrant further investigation and supports Bayesian profile regression as a useful tool for signal detection in perinatal pharmacoepidemiology.
SN - 1053-8569
SN - 1099-1557
ER -