Schizophrenia around the time of pregnancy: leveraging population-based health data and electronic health record data to fill knowledge gaps.

Schizophrenia epidemiology information technologies perinatal psychiatry pregnancy

Journal

BJPsych open
ISSN: 2056-4724
Titre abrégé: BJPsych Open
Pays: England
ID NLM: 101667931

Informations de publication

Date de publication:
28 Aug 2020
Historique:
entrez: 29 8 2020
pubmed: 29 8 2020
medline: 29 8 2020
Statut: epublish

Résumé

Research in schizophrenia and pregnancy has traditionally been conducted in small samples. More recently, secondary analysis of routine healthcare data has facilitated access to data on large numbers of women with schizophrenia. To discuss four scientific advances using data from Canada, Denmark and the UK from population-level health registers and clinical data sources. Narrative review of research from these three countries to illustrate key advances in the area of schizophrenia and pregnancy. Health administrative and clinical data from electronic medical records have been used to identify population-level and clinical cohorts of women with schizophrenia, and follow them longitudinally along with their children. These data have demonstrated that fertility rates in women with schizophrenia have increased over time and have enabled documentation of the course of illness in relation with pregnancy, showing the early postpartum as the time of highest risk. As a result of large sample sizes, we have been able to understand the prevalence of and risk factors for rare outcomes that would be difficult to study in clinical research. Advanced pharmaco-epidemiological methods have been used to address confounding in studies of antipsychotic medications in pregnancy, to provide data about the benefits and risks of treatment for women and their care providers. Use of these data has advanced the field of research in schizophrenia and pregnancy. Future developments in use of electronic health records include access to richer data sources and use of modern technical advances such as machine learning and supporting team science.

Sections du résumé

BACKGROUND BACKGROUND
Research in schizophrenia and pregnancy has traditionally been conducted in small samples. More recently, secondary analysis of routine healthcare data has facilitated access to data on large numbers of women with schizophrenia.
AIMS OBJECTIVE
To discuss four scientific advances using data from Canada, Denmark and the UK from population-level health registers and clinical data sources.
METHOD METHODS
Narrative review of research from these three countries to illustrate key advances in the area of schizophrenia and pregnancy.
RESULTS RESULTS
Health administrative and clinical data from electronic medical records have been used to identify population-level and clinical cohorts of women with schizophrenia, and follow them longitudinally along with their children. These data have demonstrated that fertility rates in women with schizophrenia have increased over time and have enabled documentation of the course of illness in relation with pregnancy, showing the early postpartum as the time of highest risk. As a result of large sample sizes, we have been able to understand the prevalence of and risk factors for rare outcomes that would be difficult to study in clinical research. Advanced pharmaco-epidemiological methods have been used to address confounding in studies of antipsychotic medications in pregnancy, to provide data about the benefits and risks of treatment for women and their care providers.
CONCLUSIONS CONCLUSIONS
Use of these data has advanced the field of research in schizophrenia and pregnancy. Future developments in use of electronic health records include access to richer data sources and use of modern technical advances such as machine learning and supporting team science.

Identifiants

pubmed: 32854798
doi: 10.1192/bjo.2020.78
pii: S2056472420000782
pmc: PMC7488329
doi:

Types de publication

Journal Article

Langues

eng

Pagination

e97

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Auteurs

Clare L Taylor (CL)

Women's College Hospital, Canada.

Trine Munk-Olsen (T)

Department of Economics and Business Economics, Aarhus University, Denmark.

Louise M Howard (LM)

Women's Mental Health, Institute of Psychiatry, Psychology and Neuroscience, Kings College London, UK.

Simone N Vigod (SN)

Women's College Research Institute, Women's College Hospital, Canada.

Classifications MeSH