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Identifying multiple gestation groups using state-level birth and fetal death certificate data.

Jane Lazar1, Milton Kotelchuck, Angela Nannini

  • 1Department of Maternal and Child Health, Boston University School of Public Health, Boston, Massachusetts 01760, USA. janemlazar@yahoo.com

Maternal and Child Health Journal
|December 6, 2005
PubMed
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A new algorithm accurately identifies multiple gestation groups from birth records, improving data for maternal and child health research. This method enhances the understanding of multiple births and their associated health outcomes.

Area of Science:

  • Maternal and Child Health
  • Biostatistics
  • Public Health Surveillance

Background:

  • Multiple births have increased significantly since the 1980s.
  • Existing birth and fetal death certificates lack methods to identify multiple gestation groups.
  • Exclusion of multiple gestations from research limits understanding of associated health risks.

Purpose of the Study:

  • To propose and validate a standardized methodology for identifying multiple gestation groups.
  • To enable researchers to account for multiple gestations in analyses.
  • To improve the accuracy of multiple gestation incidence and outcomes research.

Main Methods:

  • Utilized 3 years of Massachusetts birth and fetal death certificate data (1998-2000).
  • Developed an algorithm to assign matching multiple gestation group numbers based on mother's identifying information and delivery month.

Related Experiment Videos

  • Validated the methodology by comparing calculated plurality to existing data.

    • Main Results:

    • The algorithm correctly identified 99.8% of validated multiple gestation deliveries.
    • Identified 71 additional multiple gestation deliveries missed by existing files.
    • Demonstrated low error rates with only 4 false positives and 51 false negatives over 3 years.

    Conclusions:

    • The developed algorithm effectively identifies multiple gestation groupings.
    • This method enhances the identification of multiple gestation deliveries.
    • The approach is user-friendly, utilizes state-level data, and opens new analytic possibilities for maternal and child health research.