Oral Presentation
Medications in Pregnancy & Lactation (MiPaL)
Anna-Belle Beau, PhD
Postdoctoral researcher
Toulouse University Hospital, CERPOP-SPHERE Team, Inserm UMR 1295, Toulouse University, Toulouse, France
Toulouse, France
Xavier Moisset
Medical Doctor
Université Clermont Auvergne, CHU de Clermont-Ferrand, Inserm, Neuro-Dol, Clermont-Ferrand
Clermont-Ferrand, France
Justine Benevent
Researcher
PharmacoEpidemiology and Drug Safety Research Group, Department of Pharmacy, and PharmaTox Strategic Initiative, Faculty of Mathematics and Natural Sciences, University of Oslo, Oslo, Norway, Norway
Marie Beslay, PhD
Postdoctoral researcher
Toulouse University Hospital, CERPOP-SPHERE Team, Inserm UMR 1295, Toulouse University, France., France
Claudia C. Bartolini
Researcher
ARS Toscana
Florence, Toscana, Italy
Marie-Agnès Bernard
Statistician
Bordeaux PharmacoEpi, INSERM CIC-P1401, Univ. Bordeaux, 33000 Bordeaux, France, France
.jpg "Rosa Gini, PhD (she/her/hers) photo")
Rosa Gini, PhD (she/her/hers)
Head of Unit at ARS Toscana, Florence, Italy
ARS Toscana, Florence, Italy
Florence, Toscana, Italy
Mika Gissler, PhD
Professor
Department of Knowledge Brokers, Finnish Institute for Health and Welfare, Helsinki, Finland
Sue Jordan, MB.BCh., PhD. PGCE (FE),
professor
Faculty of Medicine, Health and Life Sciences, Swansea University, Swansea, United Kingdom
Régis Lassalle, MSc
Head of Statistics and Data management
Bordeaux PharmacoEpi, INSERM CIC-P1401, Univ. Bordeaux, 33000 Bordeaux, France
Bordeaux, Aquitaine, France
Maarit K. Leinonen, MD, PhD
Associate professor
Data and Analytics, Knowledge Brokers, Finnish Institute for Health and Welfare, Helsinki, Finland
Helsinki, Uusimaa, Finland
Angela Lupattelli, MSc
Associate Professor
Pharmacoepidemiology and Drug Safety Research Group, Department of Pharmacy, and PharmaTox Strategic Initiative, Faculty of Mathematics and Natural Sciences, University of Oslo, Oslo, Norway
University of Oslo
Oslo, Oslo, Norway
Marco Manfrini
Specialist in health statistics and biometrics
Department of Medical Sciences, Centre for Clinical and Epidemiological Research, Ferrara University, Italy, Italy
Visa Martikainen
Data manager, statistician
Department of Knowledge Brokers, Finnish Institute for Health and Welfare, Helsinki, Finland, Finland
Joan K. Morris
Professor
St George's, University of London, London, United Kingdom
Amanda j. Neville
Registry Leader, IMER Registry
Registro IMER University of Ferrara and Azienda Ospedaliero Universitario S Anna, Ferrara, Italy
ferrara, Emilia-Romagna, Italy
Hedvig Nordeng, PhD
Professor
Pharmacoepidemiology and Drug Safety Research Group, Department of Pharmacy, and PharmaTox Strategic Initiative, Faculty of Mathematics and Natural Sciences, University of Oslo, Oslo, Norway
Oslo, Oslo, Norway
Olga Paoletti
Researcher
Regional Health Agency of Tuscany, Pharmacoepidemiology Unit, Florence, Italy, Italy
Jingping Mo
Professor
Worldwide Medical and Safety, Pfizer Inc., New York, NY
New York, NY, United States
Christine Damase-Michel, PhD, PharmD
Associate Professor in Pharmacology
Université Toulouse III
Toulouse, Midi-Pyrenees, France
Introduction For drug safety studies in pregnancy, it is important to distinguish the effect of drug from the effect of maternal disease. However, prescription data do not necessarily contain structured information on the indication for the prescription. This is problematic for drugs with multiple indications, such as antiepileptic agents and analgesics. Our objective was to describe an algorithm to disentangle the different indications. We tested it using the example of gabapentinoids (pregabalin/gabapentin), approved in Europe for epilepsy, neuropathic pain, and generalized anxiety disorder. Material and Methods The algorithm was developed in six European health care data sources. It identified data components used as markers of prescribing: indication, prescriber specialty, primary care and/or specialized care diagnoses, procedures, reimbursement status, and prescribing/dispensing data. Each component estimated one, several or no reason for prescribing. Results from all components were then aggregated to estimate the indication for each individual. Sensitivity analyses explored multiple assessment windows and values for the codes. Results We present results on data from EFEMERIS (a French cohort of pregnant women): 238 pregnancies in which the woman received a gabapentinoid prescription were included. The algorithm indicated that the reason for prescribing was pain in 34% of pregnancies, anxiety in 7%, and epilepsy in 2 %. Multiple indications were identified in 18% of pregnancies, 80% of which were pain and anxiety. However, in almost 39% of pregnancies, no reason for prescribing could be detected. Discussion/Conclusion The results show it is possible to utilize the richness of large administrative healthcare data sources to improve information on reasons for prescribing. Our algorithm will be tested in five other large population data sources in the coming months.