Spatial multiresolution analysis of the effect of PM2.5 on birth weights

Joseph Antonelli, Joel Schwartz, Itai Kloog, Brent A. Coull

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

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 toward zero, with the effect estimate growing in magnitude once this source of variability is removed.

Original languageEnglish
Pages (from-to)792-807
Number of pages16
JournalAnnals of Applied Statistics
Volume11
Issue number2
DOIs
StatePublished - 1 Jun 2017

Keywords

  • Environmental modeling
  • Multiresolution analysis
  • Spatiotemporal modeling
  • Wavelets

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