Multimorbid Pregnancy: Determinants, Clusters, Consequences and Trajectories (MuM-PreDiCCT)

Lead Research Organisation: University of Birmingham
Department Name: Institute of Applied Health Research


The Problem
Multimorbidity is when people suffer from more than one long-term illness. It can be difficult for people with several long-term illnesses to manage their conditions and sometimes they don't receive the best quality care. Patients with multimorbidity may have to coordinate appointments with different specialists and their medications need to be managed carefully. During pregnancy, these challenges may increase for women with multimorbidity.
We know that multimorbidity in pregnancy is becoming more common, but we don't understand why this is and what the consequences are for mothers and babies. Without this deeper understanding of the problem, women with several long-term illnesses won't have the best experience of care before, during and after pregnancy because services are not tailored to their specific needs.

Our aims and approach
Our collaboration will bring together experts in data analysis, diseases and public health from 7 academic institutions in the UK. We will work in close partnership with women with experience of multimorbidity in pregnancy.
Firstly, our data specialists will look at electronic health records to find out how many women have multimorbidity in pregnancy and what illnesses they have. We will try and identify if factors such as age, weight, cultural or social background, level of education and number of previous pregnancies influences whether a woman has multimorbidity in pregnancy.
We will also find out which illnesses group together(cluster) during pregnancy, which clusters are most common and whether some clusters affect some women more than others.
In the second part of the study, we will compare what happens to mothers with and without multimorbidity during pregnancy. We will find out whether women with multimorbidity are more likely to develop illnesses during the pregnancy (e.g. gestational diabetes), after the pregnancy (e.g postnatal depression) and also in the longer-term (e.g. heart-disease). We will also look at the health and wellbeing of children of women with multimorbidity in pregnancy.
The third part of our research will focus on medications in pregnancy. We will find out what medicines women with multimorbidity take during pregnancy and how the medications affect the health of the mother and the baby during pregnancy. This knowledge will help doctors prescribe safely during pregnancy.
We know that complications in pregnancy are a warning sign of future illnesses in women. As part of this project, we will be able to find out more about how different pregnancy complications affect the longer-term health of women. We can use this knowledge to put preventative measures in place where possible.
Finally, we will meet with women and healthcare professionals to discuss the services available for women with multimorbidity in pregnancy. We will find out how appropriate and accessible these services are and how services can be improved. Going forward, this will help us to jointly design health services with women and their partners.

Involving the public
We will work in partnership with women with experience of multimorbidity in pregnancy. Their insights will help ensure that our project is grounded in the experiences of women and that all stages of the project drive towards improving care for women.

Sharing our findings
Our team has large networks and we will share our findings with healthcare professionals, through professional organisations (e.g. Royal Colleges of Obstetricians and Gynaecologists); through NHS networks (e.g. Local Maternity Services) and through charities and women's networks (e.g. Maternity Voice Partnerships).

Our Impact
This study will give healthcare professionals and women a much better understanding of multimorbidity in pregnancy. Through this enhanced understanding, we will be able to plan and design services that meet the needs of women and their families before, during and after the birth of their babies.

Technical Summary

Our vision is to characterise multimorbidity (MM) in pregnant women and then investigate the consequences of MM. To do so we will use electronic medical records, birth cohort and clinical trials data from across the four nations in the UK. We will work to develop harmonious definitions of variables and linkage algorithms to ensure comparable methods across the datasets. We will describe the proportion of women affected by MM and prevalence of each combinations of morbidities. We will apply clustering and trajectory methods to identify clusters and sequence dependent clusters. Then we will investigate if MM status and clusters differ by age, deprivation, ethnicity, parity, body mass index and lifestyle factors. By identifying the characteristics of patients within these clusters, we will be able to develop preventive interventions.

We will then study how MM pregnancy impacts on the health of mothers and offspring. Initially we will use established consensus methods to develop a core outcome set for MM pregnancy research. Then we will determine the rates of these outcomes in both mothers and offspring based on their MM status and then based on prescription combinations they have received. Findings will help to monitor and prevent adverse outcomes. Findings will also help improve safe prescribing during pregnancy.

Our previous work has demonstrated that pregnancy related complications are associated with future morbidities. Using this information and other covariates we will use bio-statistical and machine learning methods to develop and validate prediction models for long-term outcomes. This is expected to help with prevention and early identification of future morbidities.

Finally to improve maternity services, a mixed methods study will be conducted with patients and health professionals to understand current service provision and their limitations. This will then lead to co-designing of pathways for pregnant women with MM.


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