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Maternal depression and anxiety disorders and child mental health outcomes

Status

Ongoing

Title

Maternal depression and anxiety disorders and child mental health outcomes

What is the aim of the study and why is it important?

Problems relating to Maternal Depression and Anxiety Disorders (MDAD) are common and are known to affect child health and development. In the UK, the cost of these problems has been estimated at £8.1 billion for each birth cohort of children, and 72 percent of this cost is related to the direct impact on the children.

The overarching aim of our proposed research is to provide robust empirical evidence that helps understand how depression and anxiety disorders are transmitted from one generation to the next, and to help design interventions aimed at reducing the negative consequences of poor maternal mental health for children.

To achieve this aim, we will address the following research questions:
1) Are the negative effects of MDAD on children exclusively explained by genetic transmission and family background characteristics? Or are these negative effects also explained by changes in the child’s home environment?
2) Do school policies and health practices have a role in mitigating and reducing the negative effect of maternal depression on children?

We will develop and use state-of-the-art estimation methods in combination with a novel administrative dataset covering general practices and hospitals to study the mental health of mothers and their children at different stages of the children’s lives up to adolescence.

How is the research being done?

The first hypothesis we will test is whether there is a significant effect of mother’s health for a large range of child health outcomes both in the short and long run. The study will follow on from the earlier work on maternal depression and child health of Baker et al (2017) who analysed the injury rate of children in England and maternal depression. We will use three different statistical approaches that will allow us to see how these effects change as children age.

We will also produce a different statistical model using our sample of siblings to control for the unobserved mother characteristics shared by the two siblings.

Our second strand of research will focus on the role of policies concerning education and health in shaping the link between MDAD and the mental health of children. By exploiting the fact that children in the UK start school at set intervals rather than set ages, we can compare the difference in health outcomes between later and early school starters for children exposed to MDAD and those not exposed to MDAD between the ages of four and five.

We will also perform statistical analysis to determine whether early (or late) diagnosis of postpartum depression, caused by differences in health practices across areas, has consequences for children.

Baker R, Kendrick D, Tata LJ, Orton E. Association between maternal depression and anxiety episodes and rates of childhood injuries: a cohort study from England. Inj Prev. 2017 Dec;23(6):396-402. doi: 10.1136/injuryprev-2016-042294. Epub 2017 Feb 23. PMID: 28232401.

Chief Investigator

Dr Catia Nicodemo

Lead Applicant Organisation Name

Sponsor

University of Oxford

Location of research

University of Oxford

Date on which research approved

30-Nov-2020

Project reference ID

OX29

Generic ethics approval reference

18/EM/0400

Are all data accessed are in anonymised form?

Yes

Brief summary of the dataset to be released (including any sensitive data)

We will focus on mothers who gave birth between the ages of 13 and 49 and on children born between 1st January 2000 and 31st July 2020. Child and maternal depression will be measured by identifying all relevant records for depression/anxiety recorded in GP and HES records, using BNF, Read and ICD-10 codes.

We follow the mother (when possible) from up to 2 years before the birth of the child to account for previous maternal mental health. We will follow children until the latest data available.

Furthermore, for the first time, we will link children with their siblings. Such sibling linkage has not yet been performed in the UK. It will allow us to use objective measures of health, as well as sibling difference methods to analyse the effect of mothers’ mental health on child mental health, while controlling for unobserved variables that are shared by siblings such as genetic endowment, maternal education, or maternal preferences.
We will follow mothers across the life stages of her child up to 2020 and identify contacts with GPs for depression and anxiety disorders (physician visits and filled prescriptions).

The children will be followed from when they were born until 2020 and we focus on their mental health, injuries (fractures, poisoning, i.e. external causes), vaccinations, drugs prescriptions, asthma, nurse visits, GPs visits, number of A&E visits, number of referrals and number of hospital admissions.

We will include several control variables in our estimation, namely birth delivery method, parity, gestation weeks, birth weight, sex of baby, ethnicity, quintile of Townsend Deprivation Index, and general practice code.

We are using data covering primary care from EMIS and data covering secondary care from HES.

What were the main findings?

Does deferring school entry for children born just before the enrollment cutoff date improve their mental well-being? We address this question using administrative data on prescriptions for attention deficit hyperactivity disorder (ADHD) in England. Higher ADHD rates among early school starters are often attributed to a peer-comparison bias caused by differences in relative age among classmates. However, previous studies do not consider other potential underlying mechanisms. By adopting a more comprehensive framework, we can confirm that relative age is the primary driver of the gap in ADHD rate in the long term. Furthermore, we find that such a long-term gap is driven by first-time prescriptions between ages 5 and 8, which is a critical period when the accuracy of ADHD diagnosis is most important. Based on these findings, our policy recommendations include sorting children by age and refining diagnostic decision-making in early primary school.

Funding Source

Economic and Social Research Council

Public Benefit Statement

Research Team

Dr Catia Nicodemo, University of Oxford

Dr Joaquim Vidiella-Martin, University of Oxford

Prof Julia Hippisley-Cox, University of Oxford

Prof Cheti Nicoletti, University of York

Access Type

Trusted Research Environment (TRE)

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