ArticleBMC medical informatics and decision making2020
Feasibility study to identify women of childbearing age at risk of pregnancy not using any contraception in The Health Improvement Network (THIN) database.
Article in BMC medical informatics and decision making, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed, 5 citations in OpenAlex.
- Methods to establish a Pregnancy Register in the QResearch Database.Communications medicine · 2025Article
- Women's health in focus: Real-world data on valproate prescriptions during pregnancy - a cohort study in Catalonia (Spain).BMJ open · 2024Article
- Drug Exposure During Pregnancy: A Case-Control Study from a Primary Care Database.Women's health reports (New Rochelle, N.Y.) · 2024Article
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Authors and funding
5 authors at 4 institutions in 3 countries.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundWorldwide the rate of unplanned pregnancies is more than 40%. Identifying women at risk of pregnancy can help prevent negative outcomes and also reduce healthcare costs of potential complications. It can also allow the investigation of the natural history of pregnancy outcomes, such as ectopic pregnancies or miscarriages. The use of medical records databases has been a crucial development in the field of pharmacoepidemiology - e.g. The Health Improvement Network (THIN) database is a validated database representative of the UK population. This project aimed to test the feasibility of identifying a population of women of childbearing age who are at risk of pregnancy not using any contraception in THIN database.
methodsFirst a cohort of women of childbearing age (15-45yo) was identified. By applying a computer-based algorithm, containing codes for contraception methods or other suggestion of contraception, the risk of pregnancy was then ascertained. Next, two validation steps were implemented: 1) Revision of medical records/free text and 2) Questionnaires were sent to primary care practitioners (PCP) of women whose medical records had been reviewed. Positive predicted values (PPV) were calculated.
resultsA total of 266,433 women were identified in THIN. For the first validation step, 123 records were reviewed, with a PPV of 99.2% (95%CI: 95.5-99.9). For the questionnaires step, the PPV was of 82.3% (95%CI: 70-91.1). Information on sexual behaviour and attitudes towards conception was not captured by THIN.
conclusionThis study shows that by applying a comprehensive computer-based algorithm, THIN can be used to identify women at risk of pregnancy.
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