Research Projects

MEDMI has used a series of research projects to demonstrate the power of linking environment and health data. These studies focus on identifying the impacts of several environmental changes on health and wellbeing, and each explore different approaches to combining datasets. They have tested MEDMI’s approach and fed into its development as a proof of concept for using Big Data in environment and health research.

MEDMI has developed several applications to link and analyse environment and health databases. These tools are free to use.

View the tools
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Analysing linked data through a web-based browser

This project has developed robust proof of concept for using a web-based browser to access and interrogate linked data.

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Climate, weather and infectious diseases

This study has created an application which allows users to examine relationships between seasonal environmental changes and infectious diseases.

Climate, coastal and ocean dynamics, and HABS

This project uniquely leverages new Met Office capabilities to link coastal and oceanographic processes with remote sensing data to explore the possible link between harmful algal blooms (HABs) and climate change.

Meniere's Disease and Atmospheric Pressure

Ménière’s disease is an inner ear disorder. A mobile phone app has been developed which allows participants to monitor symptoms. Key findings within this data set confirmed the inverse association between atmospheric pressure and the symptoms of Ménière’s.

Osteoporosis and Solar Irradiance

Osteoporosis and UV Osteoporosis is a progressive bone disease that is characterised by a decrease in bone mass and density, which can lead to an increased risk of fracture. Osteoporosis is common in the UK with three million sufferers and 300,000 people receiving treatment from the NHS each year for fragility fractures i.e. those occurring from…

Linkage Tools for Pre-Processing Pollen Data

This Pilot Project offers a first step towards statistical estimates of atmospheric pollen concentration by linking pollen and meteorological datasets and using a Gaussian plume model to link with a high resolution land cover map.

Statistical Downscaling of Gridded Air-Quality Data

Based on complex Bayesian statistical models (Mukhopadhyay and Sahu, 2016) this project develops methods to integrate sparse AURN air quality monitoring data and a high spatial resolution output of the AQUM, run in hindcast mode, to produce estimates of air quality at any given spatial location.