Unveiling Population Heterogeneity in Health Risks Posed by Environmental Hazards Using Regression-Guided Neural Network

TL;DR

This paper introduces ReGNN, a hybrid neural network approach that uncovers hidden population heterogeneity in health risks from environmental hazards, outperforming traditional methods like MMR in identifying vulnerable groups.

Contribution

ReGNN combines neural networks with regression to non-linearly model interactions, revealing hidden vulnerabilities in health risk assessments from environmental exposures.

Findings

ReGNN uncovers heterogeneity missed by traditional MMR.

ReGNN effectively models complex, non-linear interactions.

Application to air pollution shows improved identification of vulnerable groups.

Abstract

Environmental hazards place certain individuals at disproportionately higher risks. As these hazards increasingly endanger human health, precise identification of the most vulnerable population subgroups is critical for public health. Moderated multiple regression (MMR) offers a straightforward method for investigating this by adding interaction terms between the exposure to a hazard and other population characteristics to a linear regression model. However, when the vulnerabilities are hidden within a cross-section of many characteristics, MMR is often limited in its capabilities to find any meaningful discoveries. Here, we introduce a hybrid method, named regression-guided neural networks (ReGNN), which utilizes artificial neural networks (ANNs) to non-linearly combine predictors, generating a latent representation that interacts with a focal predictor (i.e. variable measuring exposure to an environmental hazard). We showcase the use of ReGNN for investigating the population heterogeneity in the health effects of exposure to air pollution (PM2.5) on cognitive functioning scores. We demonstrate that population heterogeneity that would otherwise be hidden using traditional MMR can be found using ReGNN by comparing its results to the fit results of the traditional MMR models. In essence, ReGNN is a novel tool that enhances traditional regression models by effectively summarizing and quantifying an individual's susceptibility to health risks.