Can we Generate and Validate a Pregnancy Register for Research in the UK Greater Manchester Care Record Database Using Routine Primary Care Records
- 1University of Manchester Faculty of Medical and Human Sciences: The University of Manchester Faculty of Biology Medicine and Health
Abstract
BackgroundThe Greater Manchester Care Record (GMCR) integrates health and social care data for 2.8 million individuals. While it contains maternity information, identifying individual pregnancies, including timing and outcomes, is complex due to variations in healthcare interactions and recording practices. This study develops open-source code to create a pregnancy register within the GMCR using diagnosis- and procedure-based algorithm established in other databases.
Methods1,448 maternity SNOMED clinical codes identified pregnancy-related primary care records among women aged 14-49 years between March 2012 and August 2023. Codes were grouped by pregnancy outcome, with a hierarchical ruling approach where multiple outcomes appeared within one episode. Pregnancy start dates were derived from hierarchical coded events or imputed using biologically plausible duration based on outcome type. Internal validation compared GMCR register episodes against Hospital Episode Statistics (HES) hospital maternity admissions; external validation compared outcome rates against published UK estimates.
ResultsThe algorithm was developed across 379,832 women, identifying 880,926 pregnancies. Internal validation showed nearly half of register deliveries had a corresponding HES record, with a positive predictive value (PPV) of 48.1%and a median date difference of 17 days (IQR 1-30). External comparison revealed live birth (927.8 vs. 995.6 per 1,000 deliveries) and stillbirth (2.12 vs. 3.9 per 1,000 births) rates close to national estimates but a notable discrepancy for miscarriage (8.00 vs. 15.3 per 100 pregnancies) and termination of pregnancy (5.36 vs. 25.1 per 1,000 women). Demographic alignment was strong for maternal age (31 vs. 29.8 years) and deprivation (IMD10 median: 3 vs. 2.6), though ethnic minority representation was expectedly higher in the GMCR (37.0% vs. 28.7%).
ConclusionsThis pregnancy register enriches the GMCR for maternal health research, enabling more comprehensive and regionally tailored studies to inform healthcare delivery and policy. This work represents an important first step in developing a scalable, open-source algorithmic tool to support pregnancy research across diverse populations. By making the code openly available, it can be adapted for use in federated data environments, enhancing reproducibility and collaboration.
Related
- Investigation into biomarkers to predict preterm birth (INSIGHT): a longitudinal UK pregnancy cohort for spontaneous preterm birth research
- A modified Delphi study to determine Australian healthcare consumer priorities and consensus for the use of genomics and precision medicine approaches in pregnancy healthcare and research
- Trends, collaboration, and impact of scientific research on the Mediterranean diet during pregnancy.
- Performance of GPT-5.5 thinking for Manchester Triage System-based emergency department triage: agreement with an expert reference standard and prediction of short-term outcomes.
- A 3D-printed phantom to validate subject orientation in 3D imaging and recordings
BibTeX
@misc{cornett2026generate,
title = {{Can we Generate and Validate a Pregnancy Register for Research in the UK Greater Manchester Care Record Database Using Routine Primary Care Records}},
author = {C. Cornett and G. Tilston and G. Martin and V. Palin},
howpublished = {medRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.30.26364393},
url = {https://www.medrxiv.org/content/10.64898/2026.09.30.26364393v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Can we Generate and Validate a Pregnancy Register for Research in the UK Greater Manchester Care Record Database Using Routine Primary Care Records
AU - C. Cornett
AU - G. Tilston
AU - G. Martin
AU - V. Palin
PY - 2026
DA - 2026/09/30
PB - medRxiv
DO - 10.64898/2026.09.30.26364393
UR - https://www.medrxiv.org/content/10.64898/2026.09.30.26364393v1
AB - BackgroundThe Greater Manchester Care Record (GMCR) integrates health and social care data for 2.8 million individuals. While it contains maternity information, identifying individual pregnancies, including timing and outcomes, is complex due to variations in healthcare interactions and recording practices. This study develops open-source code to create a pregnancy register within the GMCR using diagnosis- and procedure-based algorithm established in other databases. Methods1,448 maternity SNOMED clinical codes identified pregnancy-related primary care records among women aged 14-49 years between March 2012 and August 2023. Codes were grouped by pregnancy outcome, with a hierarchical ruling approach where multiple outcomes appeared within one episode. Pregnancy start dates were derived from hierarchical coded events or imputed using biologically plausible duration based on outcome type. Internal validation compared GMCR register episodes against Hospital Episode Statistics (HES) hospital maternity admissions; external validation compared outcome rates against published UK estimates. ResultsThe algorithm was developed across 379,832 women, identifying 880,926 pregnancies. Internal validation showed nearly half of register deliveries had a corresponding HES record, with a positive predictive value (PPV) of 48.1%and a median date difference of 17 days (IQR 1-30). External comparison revealed live birth (927.8 vs. 995.6 per 1,000 deliveries) and stillbirth (2.12 vs. 3.9 per 1,000 births) rates close to national estimates but a notable discrepancy for miscarriage (8.00 vs. 15.3 per 100 pregnancies) and termination of pregnancy (5.36 vs. 25.1 per 1,000 women). Demographic alignment was strong for maternal age (31 vs. 29.8 years) and deprivation (IMD10 median: 3 vs. 2.6), though ethnic minority representation was expectedly higher in the GMCR (37.0% vs. 28.7%). ConclusionsThis pregnancy register enriches the GMCR for maternal health research, enabling more comprehensive and regionally tailored studies to inform healthcare delivery and policy. This work represents an important first step in developing a scalable, open-source algorithmic tool to support pregnancy research across diverse populations. By making the code openly available, it can be adapted for use in federated data environments, enhancing reproducibility and collaboration.
ER -