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MuMPreDiCT (Pregnancy and postpartum outcomes of mothers and their offspring)

Population Size

Not reported

Years

1997

Associated BioSamples

None/not available

Geographic coverage

United Kingdom

England

Lead time

Not applicable

Summary

Mum Predict is a research collaboration that aims to characterise and understand the determinants and consequences of pre-existing multi-morbidity in pregnant women, and to predict and prevent MM and its adverse consequences in women and their offspring.

Documentation

MuM-PreDiCT is a research collaboration across the UK that will conduct data-driven research to characterise and understand the determinants and consequences of pre-existing multimorbidity (MM) in pregnant women, and to predict and prevent MM and its adverse consequences in women and their offspring. The multidisciplinary approach undertaken, using existing quantitative data and new stakeholder data, aims to detail the burden of pre-existing MM in pregnant women, understand how morbidities accumulate and cluster from the pre-pregnancy stage through the maternity journey to their long-term healthcare, and then investigate what determinants should be targeted to influence MM through early interventions; explore women's experiences, and current health service provision to inform recommendations for practice; investigate the impact of pre-existing MM and multiple prescriptions on pregnancy, postpartum and long-term outcomes for mothers and their offspring; and investigate the extent to which pregnancy complications predict future MM in risk prediction models.

A significant outcome of this collaboration will be the creation of a comprehensive dataset on pregnancy and postpartum outcomes for mothers and their children, directly contributing to the core vision and objectives of the MIREDA Partnership. Specifically, the database will include pregnancy and birth records of English mothers aged 15-50 and their offspring, derived from electronic health records that link primary and secondary care data from the Clinical Practice Research Datalink (CPRD, GOLD, and Aurum) and linked to Hospital Episode Statistics (HES). This will be achieved through a federated analysis model in collaboration with the Centre for Health Data Science at the Institute of Applied Health Research, University of Birmingham.

Dataset type
Health and disease
Dataset sub-type
Not applicable
Associated media

Keywords

MuMPreDiCT, Pregnancy, Maternal health, Child Health, Childbirth, Multimorbidity, Social inequalities, Maternity care pathways, Postpartum, Offspring

Observations

Observed Node
Disambiguating Description
Measured Value
Measured Property
Observation Date

Events

Approximately 7 million mothers and 11.5 million pregnancies in England

11500000

Count

30 Dec 2023

Provenance

Purpose of dataset collection
Care
Source of data extraction
EPR
Collection source setting
Primary care - Clinic, Secondary care - In-patients
Patient pathway description
Pregnancies, mothers and their children are identified using the CPRD Pregnancy Register and Mother-Baby Link, and the HES Maternity tail. Pregnancy, birth and postpartum exposures and outcomes will be derived using the electronic health records from primary and secondary care.
Image contrast
Not stated
Biological sample availability
None/not available

Structural Metadata

Details

Publishing frequency
Annual
Version
1.0.0
Modified

08/10/2024

Citation Requirements
University of Birmingham

Coverage

Start date

01/01/1997

Time lag
2-6 months
Geographic coverage
United Kingdom, England
Minimum age range
15
Maximum age range
50
Follow-up

10 Years

Accessibility

Language
en
Controlled vocabulary
READ, SNOMED CT, ICD10, DM+D
Format
text/csv

Data Access Request

Dataset pipeline status
Not available
Time to dataset access
Not applicable
Jurisdiction
GB-ENG
Data use limitation
Research use only,No linkage
Data use requirements
Collaboration required,Ethics approval required,Project-specific restrictions,Publication moratorium
Data Controller
University of Birmingham

Dataset Types: Health and disease


Collection Sources: Primary care - Clinic, Secondary care - In-patients