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Background <p>Electronic health records are increasingly used to conduct pregnancy-related research as pregnant women are under-represented in research. Creating a register of pregnancies by combining data from primary and secondary care will further facilitate research in pregnancy. This work describes the construction of an algorithm to create a unified pregnancy cohort in the QResearch database during the emergency phase of the COVID-19 pandemic.</p> Methods <p>National primary care records in the QResearch® database were linked to patient-level data from Hospital Episode Statistics (HES) datasets. Females aged 15-50 years with a pregnancy outcome recorded between 30 December 2020 and 30 September 2022 were included. Pregnancy (delivery/loss) episodes were identified and cohort demographics reported using a three-stage algorithm. Pregnancy start dates were derived using a combination of HES and primary care data, or individually estimated where no corresponding date could be identified.</p> Results <p>266,758 women with 279,027 pregnancies are captured in the register. 232,673 pregnancies (83.4%) are deliveries (99.5% livebirths and 0.5% stillbirths) and 46,354 (16.6%) pregnancies are pregnancy losses. Pregnancy losses are highest amongst those of Caribbean (23.1%; n = 781) ethnicity and lowest in those of Pakistani ethnicity (13.9%, n = 1,579). 82.4% of pregnancies are derived from HES maternity records, 10.6% from primary care records, 3.4% from HES Admissions, and 3.6% from HES Procedures.</p> Conclusion <p>The construction of a pregnancy register in QResearch® offers a valuable resource for future research. Its methodology can be adapted to construct new cohorts over any period, providing a comprehensive resource on pregnancy outcomes and events.</p>

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Methods to establish a Pregnancy Register in the QResearch Database

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月23日

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  • Andrew JHL Snelling,
  • Emma Copland,
  • Winnie X. Mei,
  • Wema M. Mtika,
  • Tom Ranger,
  • Carol Coupland,
  • Marian Knight,
  • Kenneth Hodson,
  • Anthony Harnden,
  • Jonathan Van-Tam,
  • Carol Dezateux,
  • Brenda Kelly,
  • Alessandra Morelli,
  • Joanne Enstone,
  • Sharon Dixon,
  • Aziz Sheikh,
  • Julia Hippisley-Cox,
  • Jennifer A. Hirst

摘要

Background

Electronic health records are increasingly used to conduct pregnancy-related research as pregnant women are under-represented in research. Creating a register of pregnancies by combining data from primary and secondary care will further facilitate research in pregnancy. This work describes the construction of an algorithm to create a unified pregnancy cohort in the QResearch database during the emergency phase of the COVID-19 pandemic.

Methods

National primary care records in the QResearch® database were linked to patient-level data from Hospital Episode Statistics (HES) datasets. Females aged 15-50 years with a pregnancy outcome recorded between 30 December 2020 and 30 September 2022 were included. Pregnancy (delivery/loss) episodes were identified and cohort demographics reported using a three-stage algorithm. Pregnancy start dates were derived using a combination of HES and primary care data, or individually estimated where no corresponding date could be identified.

Results

266,758 women with 279,027 pregnancies are captured in the register. 232,673 pregnancies (83.4%) are deliveries (99.5% livebirths and 0.5% stillbirths) and 46,354 (16.6%) pregnancies are pregnancy losses. Pregnancy losses are highest amongst those of Caribbean (23.1%; n = 781) ethnicity and lowest in those of Pakistani ethnicity (13.9%, n = 1,579). 82.4% of pregnancies are derived from HES maternity records, 10.6% from primary care records, 3.4% from HES Admissions, and 3.6% from HES Procedures.

Conclusion

The construction of a pregnancy register in QResearch® offers a valuable resource for future research. Its methodology can be adapted to construct new cohorts over any period, providing a comprehensive resource on pregnancy outcomes and events.