ArticleBMJ health & care informatics2019
Ontology to identify pregnant women in electronic health records: primary care sentinel network database study.
Article in BMJ health & care informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Identifying pregnancies in routinely collected health data: a scoping review of methods.BMC medical informatics and decision making · 2026Article
- Article
- COVID-19 and influenza vaccine uptake among pregnant women in national cohorts of England and Wales.NPJ vaccines · 2024Article
- Phenotype execution and modeling architecture to support disease surveillance and real-world evidence studies: English sentinel network evaluation.JAMIA open · 2024Article
- Data resource profile: Scottish Linked Pregnancy and Baby Dataset (SLiPBD).International journal of population data science · 2024Article
- Article
- Valproate prescription to women of childbearing age in English primary care: repeated cross-sectional analyses and retrospective cohort study.BMC pregnancy and childbirth · 2022Article
- Article
- Medication history-wide association studies for pharmacovigilance of pregnant patients.Communications medicine · 2022Article
- Household presentation of influenza and acute respiratory illnesses to a primary care sentinel network: retrospective database studies (2013-2018).BMC public health · 2020Article
- COVID-19 Surveillance in a Primary Care Sentinel Network: In-Pandemic Development of an Application Ontology.JMIR public health and surveillance · 2020Article
- Emergence of a Novel Coronavirus (COVID-19): Protocol for Extending Surveillance Used by the Royal College of General Practitioners Research and Surveillance Centre and Public Health England.JMIR public health and surveillance · 2020Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo develop an ontology to identify pregnant women from computerised medical record systems with dissimilar coding systems in a primary care sentinel network. MATERIALS AND
methodsWe used a three-step approach to develop our pregnancy ontology in two different coding schemata, one hierarchical and the other polyhierarchical. We developed a coding system-independent pregnancy case identification algorithm using the Royal College of General Practitioners Research and Surveillance Centre sentinel network database which held 1.8 million patients' data drawn from 150 primary care providers. We tested the algorithm by examining individual patient records in a 10% random sample of all women aged 29 in each year from 2004 to 2016. We did an external comparison with national pregnancy data. We used χ
results243 005 women (median age 29 years at start of pregnancy) had 405 591 pregnancies from 2004 to 2016 of which 333 689 went to term. We found no significant difference between results obtained for two populations using different coding schemata. Pregnancy mean ages did not differ significantly from national data. DISCUSSION: This ontologically driven algorithm enables consistent analysis across data drawn from populations using different coding schemata. It could be applied to other hierarchical coding systems (eg, International Classification of Disease) or polyhierarchical systems (eg, SNOMED CT to which our health system is currently migrating).
conclusionThis ontological approach will improve our surveillance in particular of influenza vaccine exposure in pregnancy.
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