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

Abstract

Fine particulate matter (PM_2.5) 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 PM_2.5 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 PM_2.5 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 PM_2.5 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.

Keywords: Environmental modeling; Multiresolution analysis; Spatiotemporal modeling; Wavelets.

Fig 1

Illustration of average wavelet decomposition of the satellite-derived PM_2.5 estimates, averaged over all days in 2007. The left panel shows the low frequency component from a wavelet decomposition, the middle panel is the high frequency component from a wavelet decomposition, and the right panel is the total surface.

Fig 2

Parameter estimates and corresponding 95% confidence intervals from PM_2.5 models for each time period. The square points represent the point estimates for β₁ from model 5.1 and the remaining shapes represent those for ${\beta }_1^{\ast }$, ${\beta }_2^{\ast }$, ${\beta }_3^{\ast }$ from model 5.2

Fig 3

Parameter estimates and corresponding 95 % confidence intervals from model 5.3 when we remove high frequency spatial levels for each trimester. Within each panel from left to right we successively remove more and more of the higher frequency levels