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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 I Kennedy, Katherine Phillips, Megha Singh, Mohamed Mhereeg, Neil Cockburn, Peter Brocklehurst, Rachel Plachcinski, Richard Riley, Shakila Thangaratinam, Sinead Brophy, Sudasing Pathirannehelage Buddhika Hemali Sudasinghe, Utkarsh Agrawal, Zoe Vowles, Kathryn M Abel, Krishnarajah Nirantharakumar, Mairead Black, Kelly-Ann Eastwood the MuM-PreDiCT Group
Introduction One in five pregnant women have multiple long-term conditions in the United Kingdom (UK). Studies have shown that maternal multiple long-term conditions are associated with adverse outcomes. This observational study aims to compare maternal and children’s outcome for pregnant women with multiple long-term to those without multiple long-term conditions.
Methods and analysis Pregnant women aged 15 to 49 years old with a conception date between 2000 and 2019 in the UK will be included. The data source will be routine health records from all four UK nations (Clinical Practice Research Datalink [CPRD, England], Secure Anonymised Information Linkage [SAIL, Wales], Scotland routine health records and Northern Ireland Maternity System [NIMATS]), and the Born in Bradford prospective 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 (i) antenatal, (ii) peripartum, (iii) postnatal and long-term, and (iv) 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 logistic 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 meta-analysis.
Ethics and dissemination Approval has been obtained from the respective data sources in each UK nation: CPRD: Independent Scientific Advisory Committee (reference: 20_181R); SAIL: Information Governance Review Panel; Scotland: National Health Service Scotland Public Benefit and Privacy Panel for Health and Social Care (HSC-PBPP), The University Teaching and Research Ethics Committee (UTREC) from the University of St Andrews; NIMATS: Honest Broker Service Governance Board; Born in Bradford: Bradford National Health Service Research Ethics Committee (ref 07/H1302/112).
Study findings will be submitted for publications in peer reviewed journals and presented at key conferences for health and social care professionals involved in the care of pregnant women with multiple long-term conditions and their children.
Strengths and limitations of this study
The study will utilise rich data sources from routine health records from all four UK nations and a birth cohort.
Beyond examining maternal outcomes, linked mother baby data and the birth cohort data will allow for the exploration of children’s outcomes.
Key limitations include missing data, misclassification bias due to inaccurate clinical coding and residual confounding.