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networked city
Exploring the complex relationship between environmental factors and health outcomes using machine learning approaches
Health and Wellbeing
OX
Oxford
Project
Information

THE CHALLENGE

Disparities in the burden of chronic disease between and within populations are well-recognised. While many of the modifiable, individual-level disease risk factors have been identified, understanding the wider determinants of health, such as the physical (as well as socio-political) environment, is likely to play a key role in addressing iniquities in population health.

Intervening or modifying upstream environmental determinants of health could potentially lead to a greater impact in improving health for a greater number in the population. Although the impact of the environment on health is well-recognised, large-scale and detailed phenotypic environmental exposures linked at an individual level has been limited. The combined impact or the relative contributions of these different environmental factors on health or on geospatial clustering of risk factors and disease at high resolution remain understudied.

Research Approach

In this project, we hope to elucidate the importance of environmental factors in influencing health and disease and their geospatial distribution using a range of data science and machine learning approaches to extract, infer, and validate knowledge using a large, well-characterised UK cohort. Through this approach, we hope to be able to estimate risks for a range of health conditions based on environmental factors to create a risk map which can be useful for policy makers even in areas where local health data might be limited.

Our Aims

  • Explore the relation between environment and health using a wide range of objective measures representing different environmental domains and individual level health outcomes in population health.

  • Create a 'health risk map' to address the disease burden of a range of different health outcomes based on local environmental data

Research Questions

  1. Using individual-level health data and linked environmental data, are environmental factors predictive of a range of major causes morbidity and mortality?

  2. Are machine learning techniques, including deep learning models, predictive of health outcomes by taking account of environmental factors separately and simultaneously?

  3. Are remote sensing based measures of the environment predictive of chronic health outcomes?