Maternal and child outcomes for pregnant women with pre-existing multiple long-term conditions: protocol for an observational study in the UK

Siang ing Lee, Holly Hope, Dermot O’reilly, Lisa Kent, Gillian Santorelli, Anuradhaa Subramanian, Ngawai Moss, Amaya Azcoaga-Lorenzo, Adeniyi francis Fagbamigbe, Catherine Nelson-Piercy, Christopher Yau, Colin Mccowan, Jonathan ian Kennedy, Katherine Phillips, Megha Singh, Mohamed Mhereeg, Neil Cockburn, Peter Brocklehurst, Rachel Plachcinski, Richard d RileyShakila Thangaratinam, Sinead Brophy, Sudasing pathirannehelage buddhika Hemali sudasinghe, Utkarsh Agrawal, Zoe Vowles, Kathryn Mary Abel, Krishnarajah Nirantharakumar, Mairead Black, Kelly-Ann Eastwood

Research output: Contribution to journalArticlepeer-review


Introduction One in five pregnant women has multiple pre-existing long-term conditions in the UK. Studies have shown that maternal multiple long-term conditions are associated with adverse outcomes. This observational study aims to compare maternal and child outcomes for pregnant women with multiple long-term conditions to those without multiple long-term conditions (0 or 1 long-term conditions).

Methods and analysis Pregnant women aged 15–49 years old with a conception date between 2000 and 2019 in the UK will be included with follow-up till 2019. The data source will be routine health records from all four UK nations (Clinical Practice Research Datalink (England), Secure Anonymised Information Linkage (Wales), Scotland routine health records and Northern Ireland Maternity System) and the Born in Bradford birth cohort. The exposure of two or more pre-existing, long-term physical or mental health conditions will be defined from a list of health conditions predetermined by women and clinicians. The association of maternal multiple long-term conditions with (a) antenatal, (b) peripartum, (c) postnatal and long-term and (d) mental health outcomes, for both women and their children will be examined. Outcomes of interest will be guided by a core outcome set. Comparisons will be made between pregnant women with and without multiple long-term conditions using modified Poisson and Cox regression. Generalised estimating equation will account for the clustering effect of women who had more than one pregnancy episode. Where appropriate, multiple imputation with chained equation will be used for missing data. Federated analysis will be conducted for each dataset and results will be pooled using random-effects meta-analyses.

Ethics and dissemination Approval has been obtained from the respective data sources in each UK nation. Study findings will be submitted for publications in peer-reviewed journals and presented at key conferences.
Original languageEnglish
Article numbere068718
JournalBMJ Open
Issue number2
Early online date24 Feb 2023
Publication statusPublished - 24 Feb 2023


  • Maternal medicine
  • Humans
  • Middle Aged
  • Wales
  • England
  • Scotland
  • Pregnancy
  • Young Adult
  • Mental Disorders
  • Adolescent
  • Pregnant Women
  • Observational Studies as Topic
  • Female
  • Adult
  • Child


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