Spatial Difference-in-Differences with Bayesian Disease Mapping Models

Abstract

Bayesian disease-mapping models are widely used in small-area epidemiology to account for spatial correlation and stabilize estimates through spatial smoothing. In contrast, difference-in-differences (DID) methods—commonly used to estimate treatment effects from observational panel data—typically ignore spatial dependence. This paper integrates disease-mapping models into an imputation-based DID framework to address spatially structured residual variation and improve precision in small-area evaluations. The approach builds on recent advances in causal panel data methods, including two-way Mundlak estimation, to enable causal identification equivalent to fixed effects DID while incorporating spatiotemporal random effects. We implement the method using Integrated Nested Laplace Approximation, which supports flexible spatial and temporal structures and efficient Bayesian computation. Simulations show that, when the spatiotemporal structure is correctly specified, the approach improves precision and interval coverage compared with standard DID methods. We illustrate the method by evaluating local ice cleat distribution programs in Swedish municipalities.