Linking maternal mental health and health care utilisation to child educational outcomes: Challenges and opportunities of ECHILD

Main Article Content

Amanda Mason-Jones

Abstract

Objectives
This study aims to create a cohort that includes maternal mental health, emergency department utilisation during pregnancy, and child health and development outcomes. However, to do this, it will be necessary to assess the usability of the ECHILD linkage 'spines' for answering such research questions.


Methods
A cohort with one row per individual (mother and baby) for birth year 2012/3 will be created by linking health and educational records over a 12-year period using the ECHILD-MB and the Pupil Matching Reference. These will include the Minimum Mental Health Dataset (MMHDS), Hospital Episode Statistic for Admitted Patient Care (HES-APC), Emergency Department attendance and admission (HES-A&E), School Census, Early Years Census and Key Stage 1 (KS1). This will include social-demographic data, health care utilisation, diagnostic groups for any hospital attendance and admission, and outcomes and educational outcomes for the child from the National Pupil Database.


Results
The process of data linkage and challenges and opportunities that other researchers can learn from, will be presented.


Conclusion
Mothers’ mental health and utilisation of emergency services during pregnancy has been linked to their child’s health and increased utilisation of those services in the first year of life [1], ECHILD offers to extend this to the whole of England and to include the impact on the child’s educational outcomes.



  1. Mason-Jones AJ, Beltrán L, Keding A, Berry V, Blower SL, Whittaker K, Bywater T. Predictors of Mother and Infant Emergency Department Attendance and Admission: A Prospective Observational Study. Maternal and child health journal. 2023; 27:527-37.

Article Details

How to Cite
Mason-Jones, A. (2025) “Linking maternal mental health and health care utilisation to child educational outcomes: Challenges and opportunities of ECHILD”, International Journal of Population Data Science, 10(4). doi: 10.23889/ijpds.v10i3.3043.