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Distributed Lag Interaction Models with Two Pollutants.

Yin-Hsiu Chen1, Bhramar Mukherjee1, Veronica J Berrocal1

  • 1Department of Biostatistics, University of Michigan.

Journal of the Royal Statistical Society. Series C, Applied Statistics
|January 15, 2019
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Summary
This summary is machine-generated.

This study introduces a new distributed lag interaction model (DLIM) to analyze the combined effects of two air pollutants on health. The proposed shrinkage methods improve accuracy in estimating these complex environmental health relationships.

Keywords:
ShrinkageTime seriesTukey’s single df test for non-additivityTwo-dimensional distributed lag interaction models

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Area of Science:

  • Environmental Epidemiology
  • Biostatistics
  • Statistical Modeling

Background:

  • Distributed lag models (DLMs) are standard for assessing single air pollutant impacts on health.
  • Existing DLMs typically analyze one pollutant at a time, limiting understanding of combined effects.

Purpose of the Study:

  • To introduce the distributed lag interaction model (DLIM) for characterizing joint lagged effects of two air pollutants.
  • To extend Tukey's interaction structure and develop shrinkage versions for improved bias-variance tradeoff.

Main Methods:

  • Proposed a DLIM using tensor product basis functions for the interaction surface.
  • Extended Tukey's one-degree-of-freedom interaction structure to a two-dimensional DLM context.
  • Developed shrinkage versions of the DLIM to allow flexibility and optimize performance.

Main Results:

  • Derived marginal lag effects for one pollutant at fixed quantiles of another.
  • Simulation studies demonstrated superior performance of shrinkage methods (lower MSE).
  • Applied DLIM to NMMAPS data, analyzing joint effects of PM10 and O3 on mortality in Chicago.

Conclusions:

  • The DLIM effectively models the joint lagged effects of multiple air pollutants on health outcomes.
  • Shrinkage methods offer improved accuracy and flexibility in environmental epidemiology.
  • The approach provides valuable insights into complex air pollution-health interactions.