# Impacts of Social Environments on Neighborhood Depression Incidence: Fully Accounting for Spatial Effects

## TL;DR

This paper shows how neighborhood environments like crime and cohesion affect depression rates, and suggests that public health policies should consider local factors and collaboration.

## Contribution

The study introduces a new approach to modeling depression incidence by allowing non-stationary environmental impacts and spillover effects.

## Key findings

- Neighborhood cohesion and crime significantly influence depression incidence.
- Allowing non-stationary effects and spillovers improves model performance.
- Local environmental impacts and inter-agency collaboration are crucial for effective public health policies.

## Abstract

Neighborhood variations in depression, an important aspect of the overall mental health burden, have been linked both to environmental context (e.g., area crime, neighborhood cohesion), and to area socio-demographic composition. Previous models seeking to explain such spatial variations in mental health, such as those based on Bayesian disease mapping, follow a standard approach defined by: spatially stationary effects of area predictors; predictor effects neglecting potential spatial spillover; and a spatially structured residual to account for unmodelled spatial dependencies. In a study of depression incidence in England neighborhoods, we consider the gains from an alternative strategy, allowing nonstationary environmental impacts; spillover effects of environmental factors, and a non-stationary spatial intensity. We focus particularly on impacts of socio-behavioral environments, namely neighborhood cohesion and crime. We find these to be major influences on neighborhood depression incidence, and also find major gains in model performance by explicitly considering non-stationarity and spillovers. Allowing context heterogeneity, varying spatial intensity and spillover are shown to enhance the impacts of socio-behavioral environments on depression incidence, and such findings have broader relevance to disease mapping regression. Public health policy framing may therefore need to be tailored to locally specific environmental impacts, and to inter-agency collaboration across arbitrary boundaries.