Spatiotemporal Characterization of Overdose Mortality in Georgia, USA Using Spectral and Nonlinear Interaction Analysis, 2003-2021

TL;DR

This study introduces a nonlinear spectral-spatiotemporal framework to analyze overdose mortality in Georgia, revealing persistent long-term growth, spatially concentrated nonlinear interactions, and regime shifts post-2014.

Contribution

It develops a novel spectral and nonlinear interaction analysis method to characterize the dynamical structure of overdose trajectories at the county level.

Findings

Overdose dynamics are dominated by persistent low-frequency growth.

Nonlinear amplification is spatially concentrated and linked to long-term epidemic pressure.

Post-2014, counties with high low-frequency power show accelerated growth.

Abstract

Drug overdose mortality in the United States exhibits strong geographic heterogeneity and complex temporal evolution, yet most spatiotemporal studies focus on trends and risks without explicitly characterizing the underlying dynamical structure of overdose trajectories. We develop a nonlinear spectral-spatiotemporal framework to analyze county-level overdose mortality in the state of Georgia from 2003 to 2021. Annual mortality rates are decomposed into low- and high-frequency components to distinguish long-term epidemic pressure from short-term variability, and nonlinear cross-frequency interaction is quantified using bispectral intensity. Counties are grouped into spectral phenotypes using unsupervised clustering, and single-breakpoint change-point models are used to identify regime shifts and quantify post-break acceleration across phenotypes. We find that overdose dynamics across Georgia are dominated by persistent low-frequency growth with limited independent short-term volatility. Nonlinear amplification is spatially concentrated and co-occurs with strong long-term epidemic pressure. Despite synchronous statewide breakpoints around 2014, post-break growth accelerates most sharply in counties exhibiting high low-frequency power and elevated nonlinear interaction. Together, these results provide a mechanistically interpretable framework for identifying dynamical risk phenotypes and structural transitions in spatial overdose epidemics.