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

SUBMITTER: Bonander C 

PROVIDER: S-EPMC12643553 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature