Psychosis Prevalence in London Neighbourhoods; a Case Study in Spatial Confounding

Abstract

Abstract A considerable body of research concerns spatial variations in psychosis and impacts of neighbourhood risk factors. Such research frequently adopts a disease mapping approach, with unknown spatially clustered neighbourhood influences summarised by random effects. However, added spatial random effects may show confounding with observed area predictors, especially when observed area predictors have a clear spatial pattern. In a case study application, the standard disease mapping model is compared to methods which account and adjust for spatial confounding in an analysis of psychosis prevalence in London neighbourhoods. Established area risk factors such as area deprivation, non-white ethnicity, greenspace access and social fragmentation are considered as influences on psychosis levels. The results show evidence of spatial confounding in the standard disease mapping model. Impacts expected on substantive grounds and available evidence are either nullified or reversed in direction. Inferences about excess relative psychosis risk in different small neighbourhoods are affected. It is argued that the potential for spatial confounding to affect inferences about geographic disease patterns and risk factors should be routinely considered in ecological studies of health based on disease mapping.