The identification and validation of neural tube defects in the General Practice Research Database Public Deposited

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Last Modified
  • March 22, 2019
Creator
  • Devine, Scott T.
    • Affiliation: Gillings School of Global Public Health, Department of Epidemiology
Abstract
  • Background: Our objectives were to develop an algorithm for the identification of pregnancies in the General Practice Research Database (GPRD) that could be used to study birth outcomes and pregnancy and to determine if the GPRD could be used to identify cases of neural tube defects (NTDs). Methods: We constructed a pregnancy identification algorithm to identify pregnancies in 15 to 45 year old women between January 1, 1987 and September 14, 2004. The algorithm was evaluated for accuracy through a series of alternate analyses and reviews of electronic records. We then created electronic case definitions of anencephaly, encephalocele, meningocele and spina bifida and used them to identify potential NTD cases. We validated cases by querying general practitioners (GPs) via questionnaire. Results: We analyzed 98,922,326 records from 980,474 individuals and identified 255,400 women who had a total of 374,878 pregnancies. There were 271,613 full-term live births, 2,106 pre- or post-term births, 1,191 multi-fetus deliveries, 55,614 spontaneous abortions or miscarriages, 43,264 elective terminations, 7 stillbirths in combination with a live birth, and 1,083 stillbirths or fetal deaths. A marker of pregnancy care was identifiable for 330,153 pregnancies, eighty-four percent of which had data available at least 180 days prior to the first marker of pregnancy care. From the same population of 980,474 individuals, 217 NTD cases were identified. We attempted to validate all 217 NTD cases and 165 GP questionnaires were returned. We validated a NTD diagnosis for 117 cases, giving our electronic case definitions a positive predictive value of 0.71. The positive predictive value varied by NTD type: 0.81 for anencephaly, 0.83 for cephalocele, 0.64 for meningocele, and 0.47 for spina bifida. 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. Our NTD identification algorithm was useful in identifying three of the four types of NTDs studied. Additional information is necessary to accurately identify cases of spina bifida.
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  • In Copyright
Advisor
  • West, Suzanne L.
Degree granting institution
  • University of North Carolina at Chapel Hill
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  • Open access
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