There is considerable evidence of elevated psychosis rates in more urban settings. However, the urbanicity effect is confounded with other neighbourhood contextual effects, such as from deprivation and crime. To assess the nature of the underlying urbanicity effect, removing distorting effects of confounders, we consider a novel method to assessing causality in spatial applications: a propensity weight approach, with weights obtained by entropy optimization, and adjusting for the spatial overlap in the urbanicity effect via a bivariate exposure approach. The application is to the effect of urbanicity on psychosis prevalence in 6856 English neighbourhoods. We use a measure of urbanicity adapted to represent aspects of urban form, rather than simply population density or a binary indicator. The overlap effect in the psychosis outcome model is shown to outweigh the local effect, and we find a clear urbanicity gradient with a relative risk of 1.91 comparing the most and least urban areas, after adjustment for confounding through propensity weighting.

Urbanicity

Psychosis

Bivariate propensity

Dose-response

Confounders

Propensity weights

© 2025 The Author(s). Published by Elsevier Ltd.