title: The identification of pregnancies within the general practice research database.
abstract: BACKGROUND
  The United States is moving toward active drug safety surveillance using sources such as administrative claims and electronic medical records, but use of these data for studying teratogenicity has been challenging, as they typically do not allow for the easy identification of pregnancies. Our goal was to develop and validate an algorithm for the identification of pregnancies in the general practice research database (GPRD) that could be used to study pregnancy outcomes.
  
  
  METHODS
  The algorithm identified pregnancies in women 15-45-year-old that were pregnant between 1 January 1987 and 31 December 2006. We identified live births, stillbirths, and spontaneous and elective terminations within a woman's record. We validated the algorithm using the additional clinical details maternity (ACDM) file and de-identified free-text records.
  
  
  RESULTS
  We analyzed 16,035,394 records from 3,093,927 individuals and identified 383,184 women who had a total of 580,356 pregnancies. There were 415,221 full-term live births, 3080 pre- or post-term births, 1834 multi-fetus deliveries, 86,408 spontaneous abortions or miscarriages, 72 164 elective terminations, and 1649 stillbirths or fetal deaths. A marker of pregnancy care was identifiable for 86.3% of the 580,356 pregnancies. The internal validation steps indicated that the algorithm produced consistent results with the ACDM file.
  
  
  CONCLUSIONS
  We were successful in identifying a large number of pregnancies in the GPRD. Our use of a hierarchical approach to identify pregnancy outcomes builds upon the methods suggested in previous work, while implementing additional steps to minimize potential misclassification of pregnancy outcomes.
authors.affiliation: University of NC School of Public Health, Chapel Hill, NC, USA. sdevine@unc.edu
authors.forename: Scott
authors.initials: S
authors.lastname: Devine
authors.forename: Suzanne
authors.initials: S
authors.lastname: West
authors.forename: Elizabeth
authors.initials: E
authors.lastname: Andrews
authors.forename: Pat
authors.initials: P
authors.lastname: Tennis
authors.forename: Tarek A
authors.initials: TA
authors.lastname: Hammad
authors.forename: Susan
authors.initials: S
authors.lastname: Eaton
authors.forename: John
authors.initials: J
authors.lastname: Thorp
authors.forename: Andrew
authors.initials: A
authors.lastname: Olshan
mesh_terms: D000293:Adolescent; D000328:Adult; D000465:Algorithms; D016208:Databases, Factual; D064420:Drug-Related Side Effects and Adverse Reactions; D005194:Family Practice / Q000706:statistics & numerical data*; D005260:Female; D006801:Humans; D016347:Medical Records Systems, Computerized; D011247:Pregnancy; D011248:Pregnancy Complications / Q000453:epidemiology; D011256:Pregnancy Outcome; D011295:Prenatal Care; D012106:Research; D014481:United States / Q000453:epidemiology