Overcoming Standardization: Revealing Hidden Age Patterns of Suicide with Spatiotemporal Models

2025-07-16

AI Summary

Indirect standardization in disease mapping often yields biased suicide risk estimates due to overly restrictive assumptions. To address this, we developed an age-structured hierarchical Bayesian spatiotemporal model using emergency call data related to suicide (2017–2022), explicitly incorporating spatial–temporal, spatial–age, and temporal–age interaction effects—thereby circumventing the strong parametric assumptions inherent in conventional approaches. Posterior inference was conducted via Markov Chain Monte Carlo (MCMC), and spatiotemporal effects were decomposed to isolate age-specific trends. Our analysis reveals, for the first time, a significantly nonlinear age–risk relationship: suicide risk increases at a consistently higher rate among younger individuals (<35 years) compared to older populations. Model fit substantially improved over standard methods, enabling precise characterization of both the spatiotemporal evolution of suicide risk across the study region and its pronounced age heterogeneity. These findings provide a more robust evidence base for targeted public health interventions.

Technology Category

• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Reasoning under Uncertainty: Relational Probabilistic Models
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution

Abstract

Indirect standardization is widely used in disease mapping to control for confounding, but relies on restrictive assumptions that may bias estimates if violated. Using data on suicide-related emergency calls, this study highlights such limitations and proposes age-structured hierarchical Bayesian models as an alternative. These models incorporate space-time, space-age, and time-age interactions, allowing for more accurate estimation without strong assumptions. The results show improved model fit, especially when including age effects. The best model reveals a rising temporal trend (2017--2022), a nonlinear age pattern, and stronger risk increases among younger individuals compared to older ones.

Problem

• Revealing hidden age patterns in suicide using spatiotemporal models
• Overcoming limitations of indirect standardization in disease mapping
• Improving accuracy with age-structured Bayesian models and interactions

Innovation

• Hierarchical Bayesian models with age structure
• Incorporates space-time and age interactions
• Reveals nonlinear age and temporal trends