Algorithms to estimate the beginning of pregnancy in administrative databases

Authors

  • Andrea V. Margulis,

    Corresponding author
    1. Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women's Hospital, Boston, MA, USA
    • Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA
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  • Soko Setoguchi,

    1. Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA
    2. Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women's Hospital, Boston, MA, USA
    3. Duke Clinical Research Institute and Department of Medicine, Duke University School of Medicine, NC, USA
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  • Murray A. Mittleman,

    1. Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA
    2. Cardiovascular Epidemiology Research Unit, Beth Israel Deaconess Medical Center, Boston, MA, USA
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  • Robert J. Glynn,

    1. Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA
    2. Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women's Hospital, Boston, MA, USA
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  • Colin R. Dormuth,

    1. Department of Anesthesiology, Pharmacology & Therapeutics, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
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  • Sonia Hernández-Díaz

    1. Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA
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  • Previous presentations: An abstract based on this research was presented as poster in the International Conference on Pharmacoepidemiology and Risk Management 2010 (abstract ID 709, A Claims-based Algorithm to Estimate the Date of the Last Menstrual Period). This research was part of the corresponding author's doctoral thesis; an earlier version of this manuscript has been printed to fulfill the University's requirements for graduation and was presented in the dissertation defense. The thesis is not available online.

A. V. Margulis, Harvard School of Public Health, Department of Epidemiology, 677 Huntington Avenue, Kresge Building, Boston, MA 02115, USA. E-mail: andreamargulis@post.harvard.edu

ABSTRACT

Purpose

The role of administrative databases for research on drug safety during pregnancy can be limited by their inaccurate assessment of the timing of exposure, as the gestational age at birth is typically unavailable. Therefore, we sought to develop and validate algorithms to estimate the gestational age at birth using information available in these databases.

Methods

Using a population-based cohort of 286,432 mother–child pairs in British Columbia (1998–2007), we validated an ICD-9/10-based preterm-status indicator and developed algorithms to estimate the gestational age at birth on the basis of this indicator, maternal age, singleton/multiple status, and claims for routine prenatal care tests. We assessed the accuracy of the algorithm-based estimates relative to the gold standard of the clinical gestational age at birth recorded in the delivery discharge record.

Results

The preterm-status indicator had specificity and sensitivity of 98% and 91%, respectively. Estimates from an algorithm that assigned 35 weeks of gestational age at birth to deliveries with the preterm-status indicator and 39 weeks to those without them were within 2 weeks of the clinical gestational age at birth in 75% of preterm and 99% of term deliveries.

Conclusions

Subtracting 35 weeks (245 days) from the date of birth in deliveries with codes for preterm birth and 39 weeks (273 days) in those without them provided the optimal estimate of the beginning of pregnancy among the algorithms studied. Copyright © 2012 John Wiley & Sons, Ltd.

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