Lead Institute / Faculty: MRC Lifecourse Epidemiology Unit
Main Supervisor: Sarah Crozier
Other members of the supervisory team: Christina Vogel and Hazel Inskip
Duration of the award: 3 years, full time
Project description: An opportunity exists for a fully-funded PhD student to work within the Medical Research Council Lifecourse Epidemiology Unit at the University of Southampton. The aim of the PhD is to apply statistical methods for longitudinal data to dietary information collected from preconception to childhood in the Southampton Women’s Survey.
Obesity is a growing public health problem and future preventative strategies will depend upon an understanding of the influence of dietary behaviours. Knowledge of the role of diet in influencing health and disease is usually based on assessment of diet at one time point and subsequent associations with health outcomes. However, dietary choices change over time, and lifecourse experience of diet could plausibly be more influential than diet measured at only one time point.
The Southampton Women’s Survey has collected data on young non-pregnant women and followed-up those who became pregnant and then studied their children, therefore providing a unique dataset with information including dietary behaviours collected prospectively from before the women became pregnant until the children reached 8-9 years of age. This project aims to combine methodological advancement in the study of nutritional epidemiology with application to outcomes of importance across the lifecourse. Statistical techniques that allow the modelling of trajectories of longitudinal data, such as residual growth models, multilevel linear spline models, growth mixture models and structural equation modelling, will be applied in the context of dietary behaviours.
The specific objectives of the project are:
1. To develop statistical methods to describe longitudinal patterns of dietary behaviour, focusing on dietary patterns describing a healthy diet.
2. To compare advantages and disadvantages of statistical methods to describe longitudinal patterns of dietary behaviour amongst both children and women.
3. To examine sociodemographic measures as predictors of longitudinal patterns of dietary behaviour.
4. To examine longitudinal patterns of dietary behaviour as predictors of pregnancy and childhood body composition and other outcomes, with a view to identifying when and in whom to intervene.
The Medical Research Council Lifecourse Epidemiology Unit aims to promote human health using lifecourse epidemiological methods. The Unit has an international reputation as a centre for the study of the Developmental Origins of Health and Disease; evidence from Lifecourse Epidemiology Unit and elsewhere has demonstrated that the risk of non-communicable diseases such as osteoporosis, osteoarthritis, sarcopenia, diabetes and cardiovascular disease is accrued throughout the entire lifecourse, even from life in the womb. The MRC Lifecourse Epidemiology Unit studies the risk factors and determinants of these diseases across life from conception to old age, investigates potential underlying mechanisms, and aims to translate these findings into novel strategies to improve human health.
The Unit houses a large team of statisticians, providing a supportive environment to early career researchers. Extensive training is available both informally and formally within the Faculty of Medicine. A variety of schemes and activities to support and develop researchers include a Transferable Skills Programme, a mentoring scheme, career development advice, a vibrant seminar programme and facilitating intra- and inter-faculty networking.
Please contact: Sarah Crozier ([email protected]
Person Specification: See below https://jobs.soton.ac.uk/Upload/vacancies/files/19293/Updated%20by%20HF%20Doctoral%20Researcher%20Person%20Specification_UoS_FoM_PhD_SRC.DOCX
We are looking for highly motivated applicants with a 2(i) degree or equivalent, in a relevant subject; or relevant master’s qualification would be desirable, and an interest in longitudinal analysis. Experience with Stata or R would be desirable but not essential.
Administrative contact and how to apply:
Please complete the University’s online application form, which you can find at https://studentrecords.soton.ac.uk/BNNRPROD/bzsksrch.P_Login?pos=7209&majr=7209&term=201819
You should enter Sarah Crozier as your proposed supervisor. To support your application provide an academic CV (including contact details of two referees), official academic transcripts and a personal statement (outlining your suitability for the studentship, what you hope to achieve from the PhD and your research experience to date).
Closing date: 7th January 2019