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Background

Assisted Reproductive Treatment, such as IVF, is often a stressful process, with impacts on the relationships and lives of the couples involved. The methods usually involve hormonal manipulation of the menstrual cycle, and stimulation of the ovaries to produce multiple eggs at one time. There are a few well-recognised risks to the woman at the time of treatment, e.g. ovarian hyperstimulation syndrome. However, when treatment is successful the longer-term implications for the mother are often overlooked.

 Difficult conceptions, perceived high-risk pregnancies and anxiety about birth can all have a lasting impact. Postnatal depression (PND) affects 8-10% of all women, but there is evidence that it is higher after ART. Sufferers of PND are more vulnerable to later depressive episodes, and there are negative effects on the maternal child interaction which has longer-term implications. Thus with increasing numbers of women being treated the long-term impacts of ART on maternal mental health, are of increasing concern.

Research Experience, Research Methods and Training

The studentship will involve generic research skills such as reviewing the literature, developing hypotheses, analyzing data and writing up results.  The literature review could be either a structured review to inform the thesis, or a systematic review, if this is a feasible and appropriate component of the thesis. 

A large component of the studentship will be data analysis.  The student will use routinely collected primary care data from the Clinical Practice Research Datalink, linked to fertility treatment records. They will construct and describe fertility trajectories for mothers, using reported consultations and referrals, and augmented with HFEA records of fertility treatment. The student will need to identify and categorise episodes of depression, anxiety or mental health problems throughout each mother’s registration period, and explore the association between fertility history and mother’s mental health outcomes.  The dataset allows potential confounding factors to be taken into account – maternal age, IMD (indicator of SES), number of fertility cycles, primary or secondary infertility, multiple births, evidence of pregnancy problems, evidence of difficult delivery, co-morbidity.

It is anticipated that this will be completed with the use of standard statistical techniques such as logistic regression, although there may be scope to conduct more advanced statistical methods. The time-changing nature of the exposure, outcome and co-variates will be explored.

Prospective Candidate

Previous training or experience in epidemiology or a health-related degree, strong statistical skills, and an interest in maternal mental health.

Supervisors

Projects

Related research themes