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Exposure measurement error in air pollution studies: the impact of shared, multiplicative measurement error on epidemiological health risk estimates

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Abstract

Spatiotemporal air pollution models are increasingly being used to estimate health effects in epidemiological studies. Although such exposure prediction models typically result in improved spatial and temporal resolution of air pollution predictions, they remain subject to shared measurement error, a type of measurement error common in spatiotemporal exposure models which occurs when measurement error is not independent of exposures. A fundamental challenge of exposure measurement error in air pollution assessment is the strong correlation and sometimes identical (shared) error of exposure estimates across geographic space and time. When exposure estimates with shared measurement error are used to estimate health risk in epidemiological analyses, complex errors are potentially introduced, resulting in biased epidemiological conclusions. We demonstrate the influence of using a three-stage spatiotemporal exposure prediction model and introduce formal methods of shared, multiplicative measurement error (SMME) correction of epidemiological health risk estimates. Using our three-stage ensemble learning-based nitrogen oxides (NOx) exposure prediction model, we quantified SMME. We conducted an epidemiological analysis of wheeze risk in relation to NOx exposure among school-aged children. To demonstrate the incremental influence of exposure modeling stage, we iteratively estimated the health risk using assigned exposure predictions from each stage of the NOx model. We then determined the impact of SMME on the variance of health risk estimates under various scenarios. Depending on the stage of the spatiotemporal exposure model used, we found that wheeze odds ratio ranged from 1.16 to 1.28 for an interquartile range increase in NOx. With each additional stage of exposure modeling, the health effect estimate moved further away from the null (OR = 1). When corrected for observed SMME, the health effects confidence intervals slightly lengthened, but our epidemiological conclusions were not altered. When the variance estimate was corrected for the potential “worst case scenario” of SMME, the standard error further increased, having a meaningful influence on epidemiological conclusions. Our framework can be expanded and used to understand the implications of using exposure predictions subject to shared measurement error in future health investigations.

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Funding

This work was supported by the National Institute of Health (NIH) grant 4UH3OD023287-03 and MADRES Center for Environmental Health Disparities (5P50ES026086-05). Additional support was provided by the National Institute of Biomedical Imaging and Bioengineering (NIBIB) grant U54EB022002.

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Authors and Affiliations

  1. Division of Environmental Health, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90089, USA

    Mariam S. Girguis, Lianfa Li, Carrie Breton, Frank Gilliland & Rima Habre

  2. Sonoma Technology, Inc, Petaluma, CA, USA

    Fred Lurmann

  3. Program in Public Health, Susan and Henry Samueli College of Health Sciences, University of California, Irvine, CA, USA

    Jun Wu

  4. Division of Biostatistics, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA

    Daniel Stram

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  1. Mariam S. Girguis
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Correspondence to Mariam S. Girguis.

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Girguis, M.S., Li, L., Lurmann, F. et al. Exposure measurement error in air pollution studies: the impact of shared, multiplicative measurement error on epidemiological health risk estimates. Air Qual Atmos Health 13, 631–643 (2020). https://doi.org/10.1007/s11869-020-00826-6

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