Incorporating Measurement Error from Modeled Air Pollution Exposures into Epidemiological Analyses
- 103 Downloads
Purpose of review
Outdoor air pollution exposures used in epidemiological studies are commonly predicted from spatiotemporal models incorporating limited measurements, temporal factors, geographic information system variables, and/or satellite data. Measurement error in these exposure estimates leads to imprecise estimation of health effects and their standard errors. We reviewed methods for measurement error correction that have been applied in epidemiological studies that use model-derived air pollution data.
We identified seven cohort studies and one panel study that have employed measurement error correction methods. These methods included regression calibration, risk set regression calibration, regression calibration with instrumental variables, the simulation extrapolation approach (SIMEX), and methods under the non-parametric or parameter bootstrap. Corrections resulted in small increases in the absolute magnitude of the health effect estimate and its standard error under most scenarios.
Limited application of measurement error correction methods in air pollution studies may be attributed to the absence of exposure validation data and the methodological complexity of the proposed methods. Future epidemiological studies should consider in their design phase the requirements for the measurement error correction method to be later applied, while methodological advances are needed under the multi-pollutants setting.
KeywordsAir pollution Bootstrap Health Measurement error Regression calibration SIMEX
The authors would like to thank Dr. Richard Atkinson for his valuable review and comments on this paper.
Compliance with Ethical Standards
Conflict of Interest
Evangelia Samoli and Barbara Butland declare that they have no conflict of interest.
Human and Animal Rights and Informed Consent
This article does not contain any studies with human or animal subjects performed by any of the authors.
Papers of particular interest, published recently, have been highlighted as: • Of importance
- 6.Hoek G: Methods for assessing long-yerm exposures to outdoor air pollutants. Curr Environ Health Rep. 2017. in press.Google Scholar
- 22.• Hart JE, Spiegelman D, Beelen R, Hoek G, Brunekreef B, Schouten LJ, et al. Long-term ambient residential traffic-related exposures and measurement error-adjusted risk of incident lung cancer in the Netherlands Cohort Study on Diet and Cancer. Environ Health Perspect. 2015;123:860–6. This study includes an application of regression calibration. PubMedPubMedCentralGoogle Scholar
- 23.• Hart JE, Liao X, Hong B, Puett RC, Yanosky JD, Suh H, et al. The association of long-term exposure to PM2.5 on all-cause mortality in the Nurses’ Health Study and the impact of measurement-error correction. Environ Health. 2015;14:38. This study includes an application of risk set regression calibration. CrossRefPubMedPubMedCentralGoogle Scholar
- 25.• Bergen S, Sheppard L, Sampson PD, Kim S-Y, Richards M, Vedal S, et al. A national prediction model for PM2.5 component exposures and measurement error-corrected health effect inference. Environ Health Perspect. 2013;121:1017–25. This study includes an application of both the parameter bootstrap and the partial parametric bootstrap. PubMedPubMedCentralGoogle Scholar
- 27.• Bergen S, Sheppard L, Kaufman JD, Szpiro AA. Multipollutant measurement error in air pollution epidemiology studies arising from predicting exposures with penalized regression splines. Appl Stat. 2016;65:731–53. This study includes an application of the non-parametric bootstrap in two-pollutant models. Google Scholar
- 29.• Strand M, Sillau S, Grunwald GK, Rabinovitch N. Regression calibration with instrumental variables for longitudinal models with interaction terms, and application to air pollution studies. Environmetrics. 2015;26:393–405. This study includes an application of regression calibration using instrumental variables. CrossRefPubMedPubMedCentralGoogle Scholar