Duncan Lee (University of Glasgow) – A spatial random forest algorithm for population-level epidemiological risk assessment

A key objective in spatial epidemiology is to identify the drivers of elevated disease risks at a population level, using non-overlapping areal unit level data that comprise the total numbers of disease cases, exposures of interest and known confounders. The spatial pattern in disease risk is likely to be influenced by unmeasured confounders, whose omission induces spatial autocorrelation into the residuals from the chosen epidemiological model. A Poisson log-linear model fitted in a Bayesian paradigm is typically used for inference, which incorporates the known confounders with a linear or additive regression component and the unmeasured confounders via a set of spatially autocorrelated random effects. While such a model correctly allows for the inherent autocorrelation in the data, confounder interactions and the shapes of their functional relationships with disease risk have to be specified in advance rather than being directly learned from the data. Therefore this paper proposes the SPAR-Forest-ERF algorithm for population-level epidemiological risk assessment, which is the first fusion in this context of random forests for capturing non-linear and interacting confounder-response effects with Bayesian spatial autocorrelation models that can estimate interpretable exposure response functions (ERF) with full uncertainty quantification. Methodologically, we extend existing methods set in a prediction context by correctly propagating uncertainty between both the ML and statistical models, developing a new stopping criteria designed  to ensure the stability of  the primary inferential target, and incorporating a range of different ERFs for maximum model flexibility. This methodology is motivated by a new study of the impact of air pollution concentrations on self-rated health in Scotland, using data from the recently released 2022 national census.