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Administrative Claims Data Versus Augmented Pregnancy Data for the Study of Pharmaceutical Treatments in Pregnancy
Susan E Andrade1, Anick Bérard2, Hedvig M E Nordeng3,4
11Meyers Primary Care Institute, Fallon Community Health Plan, Reliant Medical Group, University of Massachusetts Medical School, 425 North Lake Avenue, Worcester, MA 01605 USA.
Purpose Of Review:
Administrative claims databases, which collect reimbursement-related information generated from healthcare encounters, are increasingly used to evaluate medication safety in pregnancy. We reviewed the strengths and limitations of claims-only databases and how other data sources may be used to improve the accuracy and completeness of information critical for studying medication safety in pregnancy.
Recent Findings:
Research on medication safety in pregnancy requires information on pregnancy episodes, mother-infant linkage, medication exposure, gestational age, maternal and birth outcomes, confounding factors, and (in some studies) long-term follow-up data. Claims data reliably identifies live births and possibly other pregnancies. It allows mother-infant linkage and has prospectively collected prescription medication information. Its diagnosis and procedure information allows estimation of gestational age. It captures maternal medical conditions but generally has incomplete data on reproductive and lifestyle factors. It has information on certain, typically short-term maternal and infant outcomes that may require chart review confirmation. Other data sources including electronic health records and birth registries can augment claims data or be analyzed alone. Interviews, surveys, or biological samples provide additional information. Nationwide and regional birth and pregnancy registries, such as those in several European and North American countries, generally contain more complete information essential for pregnancy research compared to claims-only databases.
Summary:
Claims data offers several advantages in medication safety in pregnancy research. Its limitations can be partially addressed by linking it with other data sources or supplementing with primary data collection. Rigorous assessment of data quality and completeness is recommended regardless of data sources.
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