Improving Disease Risk Estimation in Small Areas by Accounting for Spatio-temporal Local Discontinuities

This work proposes a two-step method to enhance disease risk estimation in small areas by integrating spatiotemporal cluster detection within a Bayesian hierarchical spatiotemporal model. First, we introduce an efficient scan-statistic-based clustering algorithm that employs a greedy search within the scan window, enabling flexible cluster detection across large spatial domains. We then integrate these detected clusters into a Bayesian spatiotemporal model to estimate relative risks, explicitly accounting for identified risk discontinuities. We apply this methodology to large-scale cancer mortality data at the municipality level across continental Spain. Our results show our method offers superior cluster detection accuracy compared to SaTScan. Furthermore, integrating cluster information into a Bayesian spatiotemporal model significantly improves model fit and risk estimate performance, as evidenced by better DIC, WAIC, and logarithmic scores than SaTScan-based or standard BYM2 models. This methodology provides a powerful tool for epidemiological analysis, offering a more precise identification of high- and low-risk areas and enhancing the accuracy of risk estimation models.