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Monitoring vs. modeled exposure data in time-series studies of ambient air pollution and acute health outcomes

Abstract

Background

Population-based short-term air pollution health studies often have limited spatiotemporally representative exposure data, leading to concerns of exposure measurement error.

Objective

To compare the use of monitoring and modeled exposure metrics in time-series analyses of air pollution and cardiorespiratory emergency department (ED) visits.

Methods

We obtained daily counts of ED visits for Atlanta, GA during 2009–2013. We leveraged daily ZIP code level concentration estimates for eight pollutants from nine exposure metrics. Metrics included central monitor (CM), monitor-based (inverse distance weighting, kriging), model-based [community multiscale air quality (CMAQ), land use regression (LUR)], and satellite-based measures. We used Poisson models to estimate air pollution health associations using the different exposure metrics. The approach involved: (1) assessing CM-based associations, (2) determining if non-CM metrics can reproduce CM-based associations, and (3) identifying potential value added of incorporating full spatiotemporal information provided by non-CM metrics.

Results

Using CM exposures, we observed associations between cardiovascular ED visits and carbon monoxide, nitrogen dioxide, fine particulate matter, elemental and organic carbon, and between respiratory ED visits and ozone. Non-CM metrics were largely able to reproduce CM-based associations, although some unexpected results using CMAQ- and LUR-based metrics reduced confidence in these data for some spatiotemporally-variable pollutants. Associations with nitrogen dioxide and sulfur dioxide were only detected, or were stronger, when using metrics that incorporate all available monitoring data (i.e., inverse distance weighting and kriging).

Significance

The use of routinely-collected ambient monitoring data for exposure assignment in time-series studies of large metropolitan areas is a sound approach, particularly when data from multiple monitors are available. More sophisticated approaches derived from CMAQ, LUR, or satellites may add value when monitoring data are inadequate and if paired with thorough data characterization. These results are useful for interpretation of existing literature and for improving exposure assessment in future studies.

Impact statement

This study compared and interpreted the use of monitoring and modeled exposure metrics in a daily time-series analysis of air pollution and cardiorespiratory emergency department visits. The results suggest that the use of routinely-collected ambient monitoring data in population-based short-term air pollution and health studies is a sound approach for exposure assignment in large metropolitan regions. CMAQ-, LUR-, and satellite-based metrics may allow for health effects estimation when monitoring data are sparse, if paired with thorough data characterization. These results are useful for interpretation of existing health effects literature and for improving exposure assessment in future air pollution epidemiology studies.

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Fig. 1
Fig. 2: Associations of air pollution and ED visits for cardiovascular diseases, comparing among pollutants, exposure metrics, and study approaches.
Fig. 3: Associations of air pollution and ED visits for respiratory diseases, comparing among pollutants, exposure metrics, and study approaches.

Data availability

This study was based on air pollution data from a variety of sources and on hospital billing records. We will make the air pollution exposure metrics data applied in this study, and relevant documentation and metadata available without cost to researchers. Individuals and parties must agree to the conditions of use governing access to the public release data, including reporting responsibilities, restrictions on redistribution of the data to third parties, proper acknowledgement of the data resource, and restrictions on use for commercial purposes. We will not be able to make the hospital records data (neither patient-level nor aggregate forms) available to external investigators given restrictions in our data use agreements between Emory University and the Georgia Hospital Association. The exposure-response functions generated in this study are available in the Supplementary Material for incorporation into meta-analyses, health impact assessments, or other analyses by external investigators.

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Acknowledgements

This publication is based in part upon information obtained from the Georgia Hospital Association (GHA). We are grateful for the support of the GHA and their member hospitals. We would also like to acknowledge Drs. Annette Rohr and Chloe Kim of the Electric Power Research Institute for their helpful feedback.

Funding

This research was supported by funding from the Electric Power Research Institute (10009553). This publication was also made possible by grants to Emory University from the National Institute of Environmental Health Sciences of the National Institutes of Health under award numbers R01ES027892 and P30ES019776. The content is solely the responsibility of the authors and does not necessarily represent the official views of the Electric Power Research Institute or the National Institutes of Health.

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Contributions

STE was responsible for study design, results interpretation, and report writing. RRD was responsible for statistical analyses and results presentation. HY was responsible for development of exposure metrics and results interpretation. NS was responsible for results interpretation and report writing. SM was responsible for project management and mapping. HHC was responsible for study design, biostatistical support, results interpretation, and report writing.

Corresponding author

Correspondence to Stefanie T. Ebelt.

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The authors declare no competing interests.

Ethics approval and consent to participate

Use of the ED data was in accordance with agreements with the Georgia Hospital Association and this study was approved prior to its conduct by the Emory University Institutional Review Board.

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Ebelt, S.T., D’Souza, R.R., Yu, H. et al. Monitoring vs. modeled exposure data in time-series studies of ambient air pollution and acute health outcomes. J Expo Sci Environ Epidemiol (2022). https://doi.org/10.1038/s41370-022-00446-5

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  • DOI: https://doi.org/10.1038/s41370-022-00446-5

Keywords

  • Air pollution
  • Criteria pollutants
  • Epidemiology
  • Exposure modeling
  • Particulate matter
  • Population-based studies

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