A Scalar-on-Quantile-Function Approach for Estimating Short-term Health Effects of Environmental Exposures

TL;DR

This paper introduces a novel scalar-on-quantile-function modeling approach that captures within-unit exposure heterogeneity to better estimate short-term health effects of environmental pollutants, demonstrated through air pollution and ED visits data.

Contribution

It develops a flexible functional covariate model using exposure quantile functions to improve health effect estimation at different exposure levels.

Findings

Lower quantiles of carbon monoxide exposure have stronger associations with ED visits.

The approach captures heterogeneity in exposure that aggregate measures miss.

Software implementation is available in R package nbRegQF.

Abstract

Environmental epidemiologic studies routinely utilize aggregate health outcomes to estimate effects of short-term (e.g., daily) exposures that are available at increasingly fine spatial resolutions. However, areal averages are typically used to derive population-level exposure, which cannot capture the spatial variation and individual heterogeneity in exposures that may occur within the spatial and temporal unit of interest (e.g., within day or ZIP code). We propose a general modeling approach to incorporate within-unit exposure heterogeneity in health analyses via exposure quantile functions. Furthermore, by viewing the exposure quantile function as a functional covariate, our approach provides additional flexibility in characterizing associations at different quantile levels. We apply the proposed approach to an analysis of air pollution and emergency department (ED) visits in Atlanta over four years. The analysis utilizes daily ZIP code-level distributions of personal exposures to four traffic-related ambient air pollutants simulated from the Stochastic Human Exposure and Dose Simulator. Our analyses find that effects of carbon monoxide on respiratory and cardiovascular disease ED visits are more pronounced with changes in lower quantiles of the population-level exposure. Software for implement is provided in the R package nbRegQF.