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Identifying targets to reduce health inequality; does area or individual-level data on social determinants of health perform better at predicting health outcomes?

   School of Medicine

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  Dr Emma Parry  No more applications being accepted  Funded PhD Project (Students Worldwide)

About the Project

We are interested in social determinants of health (SDH) and how these impact on people’s health outcomes. At present this is difficult to assess. We can look at area factors such as someone’s postcode or we can look at individual factors (e.g. income adequacy, food poverty), both of which shape health outcomes. Determining whether area factors or individual factors better predict health outcomes is still contested. Ecological fallacy can arise if using area level data to predict health outcomes. e.g. if a particular group of people living in an area of a city are highlighted as having poorer health outcomes, it is an error to conclude that a randomly selected individual will have poorer health outcomes than that for the overall population. 

 This exciting project focusses on identify the types of indicators, area-based and those individual-based (and capturing the different social determinants of health), which most accurately help to predict health outcomes.

The proposed research will be of interest and potential benefit to a range of national organisations and stakeholders. By understanding whether area or individual-level factors on SDH perform better at predicting health outcomes will inform which data to utilise for understanding individual and population level health inequalities, and guide service development and policy. Gathering better information on health inequalities will equip front line clinicians to improve personalised care and provide better information on populations to aid public health decisions. The findings will also inform, guide and direct further research on health inequalities.

 The proposed PhD project is timely. Health is a driver for quality of life and productivity. Health inequalities are worsening in the UK1 and the Covid-19 pandemic has further exposed disparities in health and health outcomes2. Key to reducing inequalities is understanding the social and behavioural factors that underpin them. Social determinants of health - the conditions in which people are born, grow, work, live, and age, and the wider set of forces and systems shaping the conditions of daily life3 – critically influence population and individual health outcomes. Despite this, information on individual-level social risks (such as income adequacy, food poverty and fuel poverty) are not collected routinely or in a standardised format in primary care or for public health purposes, making it difficult to assess their impact on health outcomes. The Indices of Multiple Deprivation are the routinely used measure in England for deprivation based on geographical area­5 but does not give accurate information at an individual-level. Yet there is some evidence that having better information on individual-level social risks can improve health outcomes through adaptations of care plans, influencing clinical decision making6, awareness of financial barriers to accessing medication, addressing behavioural health issues, helping with transportation7 and increased referral to community services such as social prescribers8-9.

The overall aim of this project is to get a better understanding of the drivers of inequalities. We aim to explore how well data collected from individuals on their social determinants of health (e.g. income adequacy, food poverty, fuel poverty) compares to area level data (e.g. deprivation levels determined by postcode) to predict someone’s risk of certain health outcomes. The project will also explore the scope for joining up an existing local authority database that contains information on SDH and link this to information held in the electronic primary care health record to highlight opportunities and challenges of utilising existing datasets to tackle inequalities. The studentship will be primarily based at Keele University which is a world leading, supportive and popular academic environment.

The successful candidate will work with closely with a diverse multi-disciplinary team and will collaborate with the Local Authority of a large, deprived multi-ethnic city. The highly desirable skills that will be developed through this PhD will include advanced quantitative methods such multi-level mixed modelling using a large population level musculoskeletal dataset. This studentship will focus on understanding the best measure of SDH which is of key importance in understanding inequalities, how we can address them and giving us better intelligence on our populations. 

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Funding Notes

Funding is available to cover 100% UK/EU student tuition fees and consumables for 3 years.
Stipend fee available.
Non-UK/EU applicants will be considered if they are able to self-fund any additional tuition fees and costs that may exist and obtain a visa to study in the UK.

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