Active Pregnancy, Children & Inherited Conditions Mental Health

Multimorbidity and Pregnancy: Determinants, Clusters, Consequences and Trajectories (MuM-PreDiCT)

In plain English

AI plain-English summary

One in five pregnant women in the UK now enters pregnancy with two or more long-term health conditions—such as diabetes, high blood pressure, depression, or anxiety—yet healthcare services are not designed to manage this complexity. This matters because the rise in older mothers, obesity, and mental health conditions means multimorbidity in pregnancy is becoming more common, but researchers do not fully understand how multiple conditions and their medications interact to affect mothers and babies. Without that knowledge, women may receive fragmented or unsafe care, and services miss opportunities to prevent complications. If the research succeeds, it will produce practical tools for clinicians and patients: accessible risk information about pregnancy, medication combinations, and future health; a prediction model to flag women likely to develop long-term conditions after pregnancy; and recommendations for redesigning maternity services around the real needs of women with multiple conditions. This could reduce avoidable pregnancy complications, lower long-term health burdens for women and families, and cut healthcare costs by targeting interventions at the right time points. The work is applied and patient-facing, with direct implications for how the NHS delivers maternity care.

View original technical description
What is the problem? One in five pregnant women have two or more active long-term health conditions. These can be both physical conditions (like diabetes or raised blood pressure), and mental health conditions (such as depression or anxiety). Often women also have to take several medications to manage their different health needs. Having two or more health conditions is also becoming increasingly common in pregnant women as women are increasingly older when they start having a family and as obesity and mental health conditions are on the rise in general. We don't really understand what the consequences are of multiple health conditions or medications for mothers and babies. This can make pregnancy, healthcare and managing medications more complicated. Without deeper understanding of the problem, women with several long-term health conditions may not have the best and safest experience of care before, during and after pregnancy because services have not been designed with their health needs in mind. What will we do? Our research is divided into five work packages. The first work package will examine how health conditions accumulate over time and identify what makes a woman more at risk of developing two or more long-term health conditions before pregnancy. The second work package will explore women's experiences of care during pregnancy, birth and after birth. We will work together with families and health professionals to establish how care could be improved. The third work package will look further at how having two or more long-term health conditions may affect pregnant women and their children. We will do this in three ways: we will identify outcomes that women, health professionals and researchers feel should be reported in research; we will examine how often women experience pregnancy complications; and we will explore how frequently women and their children develop additional long-term ill health. In the fourth work package we will describe how medications are prescribed. We will investigate how taking combinations of medication may affect pregnant women and their babies. In our fifth work package, we will build a prediction model to help identify how likely a previously healthy pregnant woman will develop multiple long-term conditions after pregnancy. We can do this by using health information collected during or just after pregnancy. This is because we know that some complications in pregnancy may be a warning sign of future illnesses. What will our research achieve? We will help women and their healthcare professionals make informed decisions about their care and medication use by providing accessible information on risk. For example, the risks associated with pregnancy; the risks associated with combinations of medications during pregnancy; and the future risk of developing long-term health conditions after a pregnancy complication. Our work will also identify important time points to intervene and ways to prevent pregnancy complications or developing future long-term health conditions. This will reduce the health burden for women, partners, carers and reduce avoidable healthcare and economic cost in the long run for society. Working together with women and healthcare professionals, we will produce recommendations on how to plan and design services that meet the needs of women and their families before, during and after pregnancy.

View the original record at the funder ↗

Researchers

Amaya Azcoaga Lorenzo (Co-Investigator)Catherine Nelson-Piercy (Co-Investigator)Christopher Yau (Co-Investigator)Colin McCowan (Co-Investigator)Dermot O'Reilly (Co-Investigator)Gillian Santorelli (Co-Investigator)Helen Dolk (Co-Investigator)Holly F Hope (Co-Investigator)Kathryn Abel (Co-Investigator)Kelly-Ann Eastwood (Co-Investigator)Krishnarajah Nirantharakumar (Principal Investigator)Louise Locock (Co-Investigator)Mairead Black (Co-Investigator)Maria Loane (Co-Investigator)Ngawai Moss (Co-Investigator)Peter Brocklehurst (Co-Investigator)Rachel Plachcinski (Co-Investigator)Rebecca (Beck) Taylor (Co-Investigator)Richard Riley (Co-Investigator)Shakila Thangaratinam (Co-Investigator)Sinead Brophy (Co-Investigator)Zoe Vowles (Co-Investigator)

Related Research

Grants with similar aims, by meaning.

Multimorbid Pregnancy: Determinants, Clusters, Consequences and Trajectories (MuM-PreDiCCT)
Multimorbidity in Pregnancy: Determinant, Consequences, Clusters and Trajectories (MuM-PreDiCT)
MILLENIA: MultIpLe Long-tErm conditions in pregnancy aNd experiences of mIdwifery cAre – a mixed methods study
Investigating the health and care needs of women with multimorbidity
Molecular drivers & predictors of pregnancy complications & future health

Original classification

Research Grant

Plain English summaries and category classifications on this site are generated by AI and may not perfectly reflect the original research.