Spatial Multiresolution Analysis of the Effect of PM2.5 on Birth Weights

TL;DR

This paper introduces a novel wavelet-based method to analyze spatial scales of PM2.5 pollution and their specific effects on birth weights, effectively removing temporal confounding to improve accuracy.

Contribution

The study develops a two-dimensional wavelet decomposition technique that isolates spatial effects of PM2.5 on health outcomes, enhancing analysis precision over standard methods.

Findings

Both local and urban PM2.5 sources negatively impact birth weight.

Removing temporal confounding increases the estimated effect size.

Spatial analysis reveals pollution effects at multiple scales.

Abstract

Fine particulate matter (PM2.5) measured at a given location is a mix of pollution generated locally and pollution traveling long distances in the atmosphere. Therefore, the identification of spatial scales associated with health effects can inform on pollution sources responsible for these effects, resulting in more targeted regulatory policy. Recently, prediction methods that yield high-resolution spatial estimates of PM2.5 exposures allow one to evaluate such scale-specific associations. We propose a two-dimensional wavelet decomposition that alleviates restrictive assumptions required for standard wavelet decompositions. Using this method we decompose daily surfaces of PM2.5 to identify which scales of pollution are most associated with adverse health outcomes. A key feature of the approach is that it can remove the purely temporal component of variability in PM2.5 levels and calculate effect estimates derived solely from spatial contrasts. This eliminates the potential for unmeasured confounding of the exposure - outcome associations by temporal factors, such as season. We apply our method to a study of birth weights in Massachusetts, U.S.A from 2003-2008 and find that both local and urban sources of pollution are strongly negatively associated with birth weight. Results also suggest that failure to eliminate temporal confounding in previous analyses attenuated the overall effect estimate towards zero, with the effect estimate growing in magnitude once this source of variability is removed.