A unifying modelling approach for hierarchical distributed lag models

TL;DR

This paper introduces a flexible statistical framework for hierarchical distributed lag models using penalized GAMs, enabling interpretable, Bayesian, and hierarchical inference, demonstrated through epidemiological mortality data analysis.

Contribution

It unifies distributed lag models within penalized GAMs, allowing for hierarchical, Bayesian, and interpretable inference, with novel epidemiological applications.

Findings

Humidity increases mortality risk during cold conditions.

Joint effects of temperature and humidity on mortality are quantified.

Hierarchical pooling estimates district-specific risks.

Abstract

We present a statistical modelling framework for implementing Distributed Lag Models (DLMs), encompassing several extensions of the approach to capture the temporally distributed effect from covariates via regression. We place DLMs in the context of penalised Generalized Additive Models (GAMs) and illustrate that implementation via the R package \texttt{mgcv}, which allows for flexible and interpretable inference in addition to thorough model assessment. We show how the interpretation of penalised splines as random quantities enables approximate Bayesian inference and hierarchical structures in the same practical setting. We focus on epidemiological studies and demonstrate the approach with application to mortality data from Cyprus and Greece. For the Cyprus case study, we investigate for the first time, the joint lagged effects from both temperature and humidity on mortality risk with the unexpected result that humidity severely increases risk during cold rather than hot conditions. Another novel application is the use of the proposed framework for hierarchical pooling, to estimate district-specific covariate-lag risk on morality and the use of posterior simulation to compare risk across districts.