# A spatio-temporal process-convolution model for quantifying health inequalities in respiratory prescription rates in Scotland

## TL;DR

This paper introduces a novel spatio-temporal process-convolution model to analyze health inequalities in respiratory prescription rates across Scotland, accounting for complex data types and identifying key environmental and socioeconomic drivers.

## Contribution

A new adaptive process-convolution model with tapering for analyzing complex spatio-temporal health data at the GP surgery level.

## Key findings

- Particulate air pollution significantly impacts prescription rates.
- Poverty and ethnicity are key drivers of health inequalities.
- Regional disparities persist even after adjusting for covariates.

## Abstract

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.