A mixture of distributed lag non-linear models to account for spatially heterogeneous exposure-lag-response associations

TL;DR

This paper introduces DLNM-Clust, a Bayesian mixture model that captures spatial heterogeneity in exposure-lag-response relationships, improving risk estimation for environmental health impacts across different regions.

Contribution

The paper presents DLNM-Clust, a novel Bayesian mixture of distributed lag non-linear models that accounts for spatial heterogeneity in exposure-response associations.

Findings

Identified distinct spatial clusters with different exposure-lag-response patterns.

Demonstrated improved risk estimation accuracy in Belgian COVID-19 data.

Highlighted the importance of spatially tailored environmental health models.

Abstract

Environmental exposures, such as air pollution and extreme temperatures, have complex effects on human health. These effects are often characterized by non-linear exposure-lag-response relationships and delayed impacts over time. Accurately capturing these dynamics is crucial for informing public health interventions. The Distributed Lag Non-Linear Model (DLNM) is a flexible statistical framework for estimating such effects in epidemiological research. However, standard DLNM implementations typically assume a homogeneous exposure-lag-response association across the study region, overlooking potential spatial heterogeneity, which can lead to biased risk estimates. To address this limitation, we introduce DLNM-Clust: a novel mixture of DLNMs that extends the traditional DLNM. Within a Bayesian framework, DLNM-Clust probabilistically assigns each geographic unit to one of $C$ latent spatial clusters, each of which is defined by a distinct DLNM specification. This approach allows capturing both common patterns and singular deviations in the exposure-lag-response surface. We demonstrate the method using municipality-level time-series data on the relationship between air pollution and the incidence of COVID-19 in Belgium. Our results emphasize the importance of spatially aware modeling strategies in environmental epidemiology, facilitating region-specific risk assessment and supporting the development of targeted public health initiatives.