Search site

School of Geography

Analysis of spatial inequalities in public health prescription rates to predict antibiotic resistance risk

Supervisor: Prof Lex Comber

The government provides full details of all NHS prescription data. It aims to gain spatial intelligence about the spatial / temporal patterns and costs of prescriptions. The reasons for this are 1) economic, relating to the spiraling costs of prescriptions, and 2) the very real and increasing threat of antibiotic resistance amongst the population. However, pseudo-anonymised individual prescription data are also available and these can provide much deeper insight into the wider socio-economic of prescribing.

This research will use data  mining techniques to analyse high volumes of prescribing data (anonymised and pseudo-anonymised) within the context of geo-demographic information on population and social economic variables  in order to examine:

  • relationships between poor health and prescribing rates.
  • spatial inequalities in uptake in health provision, access to GPs and prescribing (which might then exaggerate the inequalities).
  • whether people in less deprived areas go to GPs sooner than those in more deprived areas. 
  • where GPs are prescribing more than expected.
  • where antibiotic resistance is likely to emerge. How / in what way / pattern will it spread?
  • what spatial and temporal trends are in prescriptions volume, type and cost. How does this project into the future spatially?

To discuss the proposed research, please contact a.comber(at)leeds.ac.uk