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
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.
This project aimed to systematically document the seasonality of pathogen-related infectious diseases reported in England and Wales to explore the possible influence of climate.
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 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…
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.
This pilot project investigated the opportunity to combine the strengths of MEDMI and SAIL (The Secure Anonymised information Linkage Dataset at Swansea University) to link environmental, health and socio economic 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.
