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Maternal depression and anxiety disorders and child mental health outcomes

In this project, we aim to increase what is known about the negative effects of maternal depression and anxiety disorders (MDAD) on the mental health outcomes of children. Mental health is a topical area of research which is receiving increasing attention in the media and is one of five ESRC strategic priorities for investment. This project will help develop an understanding of how mental depression and anxiety disorders are transmitted from one generation to the next and ultimately help to design interventions better able to reduce the consequences of maternal mental health for children.

There is a large amount of empirical evidence showing a strong association between mother and child mental health, but it has so far been difficult to separate the causal mechanisms explaining this association because shared family background plays a key role. We hope to overcome this issue by performing a sibling study, which will enable us to compare similar children who experienced problems at different stages of their lives.

We have two main objectives. We hope to:

  1. Estimate the indirect effects of MDAD that occur through changes to the child's home environment, assessing whether there are sensitive periods during childhood when such effects are larger and therefore more harmful for children and investigate the heterogeneity across gender, health endowment at birth, and area characteristics.
  2. Evaluate whether school and health policies may have a role in addressing the negative effects of maternal depression by assessing:
    a) whether starting school earlier damages or benefits children exposed to maternal depression,
    b) whether the timing of the diagnosis for post-partum depression caused by differences in health practices across areas affects the negative effect of maternal depression.

We will be using innovative statistical methods and a new dataset created by linking data from primary and secondary health care (QResearch and Hospital Episode Statistics, which has not been used in this setting before.

Project lead:

Collaborators:

Professor Julia Hippisley-Cox, University of Oxford

Professor Cheti Nicoletti, University of York

Dates:

October 2020 – August 2022

Funder:

Economic and Social Research Council

Our team