A spatio-temporal process-convolution model for quantifying health inequalities in respiratory prescription rates in Scotland
- Date: Friday 5 May 2017, 14:00 – 15:00
- Location: Mathematics Level 8, MALL 1, School of Mathematics
- Type: Seminars, Statistics
- Cost: Free
Duncan Lee, University of Glasgow. Part of the statistics seminars series.
The rates of respiratory prescriptions vary by GP surgery across Scotland, suggesting there are sizeable health inequalities in respiratory ill health across the country. The aim of this paper is to estimate the magnitude, spatial pattern and drivers of this spatial variation. Monthly data on respiratory prescriptions are available at the GP surgery level, which creates an interesting methodological challenge as these data are not the classical geostatistical, areal unit or point process data types. A novel process-convolution model is proposed, which extends existing methods by being an adaptive smoother via a random weighting scheme and using a tapering function to reduce the computational burden. The results show that particulate air pollution, poverty and ethnicity all drive the health inequalities, while there are additional regional inequalities in rates after covariate adjustment.
Duncan Lee, University of Glasgow