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Current Environmental Health Reports

, Volume 4, Issue 4, pp 472–480 | Cite as

Incorporating Measurement Error from Modeled Air Pollution Exposures into Epidemiological Analyses

Air Pollution and Health (S Adar and B Hoffmann, Section Editors)
Part of the following topical collections:
  1. Topical Collection on Air Pollution and Health

Abstract

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.

Recent findings

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.

Summary

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.

Keywords

Air pollution Bootstrap Health Measurement error Regression calibration SIMEX 

Notes

Acknowledgements

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.

References

Papers of particular interest, published recently, have been highlighted as: • Of importance

  1. 1.
    Zeger SL, Thomas D, Dominici F, Samet JM, Schwartz J, Dockery D, et al. Exposure measurement error in time-series studies of air pollution: concepts and consequences. Environ Health Perspect. 2000;108:419–26.CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Brauer M, Brumm J, Vedal S, Petkau AJ. Exposure misclassification and threshold concentrations in time series analyses of air pollution health effects. Risk Anal. 2002;22:1183–93.CrossRefPubMedGoogle Scholar
  3. 3.
    Gryparis A, Paciorek CJ, Zeka A, Schwartz J, Coull BA. Measurement error caused by spatial misalignment in environmental epidemiology. Biostatistics. 2009;10:258–74.CrossRefPubMedGoogle Scholar
  4. 4.
    Szpiro AA, Sheppard L, Lumley T. Efficient measurement error correction with spatially misaligned data. Biostatistics. 2011;12:610–23.CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Sheppard L, Burnett RT, Szpiro AA, Kim S-Y, Jerrett M, Pope CA 3rd, et al. Confounding and exposure measurement error in air pollution epidemiology. Air Qual Atmos Health. 2012;5:203–16.CrossRefPubMedGoogle Scholar
  6. 6.
    Hoek G: Methods for assessing long-yerm exposures to outdoor air pollutants. Curr Environ Health Rep. 2017. in press.Google Scholar
  7. 7.
    Eeftens M, Beelen R, de Hoogh K, Bellander T, Cesaroni G, Cirach M, et al. Development of land use regression models for PM(2.5), PM(2.5) absorbance, PM(10) and PM(coarse) in 20 European study areas; results of the ESCAPE project. Environ Sci Technol. 2012;46:11195–205.CrossRefPubMedGoogle Scholar
  8. 8.
    Sampson PD, Richards M, Szpiro AA, Bergen S, Sheppard L, Larson TV, et al. A regionalized national universal kriging model using partial least squares regression for estimating annual PM2.5 concentrations in epidemiology. Atmos Environ. 2013;75:383–92.CrossRefGoogle Scholar
  9. 9.
    Keller JP, Olives C, Kim SY, Sheppard L, Sampson PD, Szpiro AA, et al. A unified spatiotemporal modeling approach for predicting concentrations of multiple air pollutants in the multi-ethnic study of atherosclerosis and air pollution. Environ Health Perspect. 2015;123:301–9.PubMedGoogle Scholar
  10. 10.
    Kloog I, Nordio F, Coull BA, Schwartz J. Incorporating local land use regression and satellite aerosol optical depth in a hybrid model of spatiotemporal PM2.5 exposures in the Mid-Atlantic states. Environ Sci Technol. 2012;46:11913–21.CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Akita Y, et al. Large scale air pollution estimation method combining LUR and chemical transport modeling. Environ Sci Technol. 2014;48:4452.CrossRefPubMedGoogle Scholar
  12. 12.
    de Hoogh K, Gulliver J, Donkelaar AV, Martin RV, Marshall JD, Bechle MJ, et al. Development of West-European PM2.5 and NO2 land use regression models incorporating satellite-derived and chemical transport modelling data. Environ Res. 2016;151:1–10.CrossRefPubMedGoogle Scholar
  13. 13.
    Reid CE, Jerrett M, Tager IB, Petersen ML, Mann JK, Balmes JR. Differential respiratory health effects from the 2008 northern California wildfires: a spatiotemporal approach. Environ Res. 2016;150:227–35.CrossRefPubMedGoogle Scholar
  14. 14.
    Di Q, Kloog I, Koutrakis P, Lyapustin A, Wang Y, Schwartz J. Assessing PM2.5 exposures with high spatiotemporal resolution across the continental United States. Environ Sci Technol. 2016;50:4712–21.CrossRefPubMedGoogle Scholar
  15. 15.
    Kim S-Y, Sheppard L, Kim H. Health effects of long-term air pollution: influence of exposure prediction methods. Epidemiology. 2009;20:442–50.CrossRefPubMedGoogle Scholar
  16. 16.
    Szpiro AA, Paciorek CJ, Sheppard L. Does more accurate exposure prediction necessarily improve health effect estimates? Epidemiology. 2011;22:680–5.CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Basagaña X, Aguilera I, Rivera M, Agis D, Foraster M, Marrugat J, et al. Measurement error in epidemiologic studies of air pollution based on land-use regression models. Am J Epidemiol. 2013;178:1342–6.CrossRefPubMedGoogle Scholar
  18. 18.
    Butland BK, Armstrong B, Atkinson RW, Wilkinson P, Heal MR, Doherty RM, et al. Measurement error in time-series analysis: a simulation study comparing modelled and monitored data. BMC Med Res Methodol. 2013;13:136.CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Dionisio KL, Chang HH, Baxter LK. A simulation study to quantify the impacts of exposure measurement error on air pollution health risk estimates in copollutant time-series models. Environ Health. 2016;15:114.CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Dionisio KL, Baxter LK, Chang HH. An empirical assessment of exposure measurement error and effect attenuation in bipollutant epidemiologic models. Environ Health Perspect. 2014;122:1216–24.PubMedPubMedCentralGoogle Scholar
  21. 21.
    Sellier Y, Galineau J, Hulin A, Caini F, Marquis N, Navel V, et al. EDEN mother–child cohort study group. Health effects of ambient air pollution: do different methods for estimating exposure lead to different results? Environ Int. 2014;66:165–73.CrossRefPubMedGoogle Scholar
  22. 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. 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
  24. 24.
    • Alexeeff SE, Carroll RJ, Coull B. Spatial measurement error and correction by spatial SIMEX in linear regression models when using predicted air pollution exposures. Biostatistics. 2016;17:377–89. This study includes an application of spatial SIMEX. CrossRefPubMedGoogle Scholar
  25. 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
  26. 26.
    • Szpiro AA, Paciorek CJ. Measurement error in two-stage analyses, with application to air pollution epidemiology. Environmetrics. 2013;24:501–17. This study includes an application of the non-parametric bootstrap. CrossRefPubMedGoogle Scholar
  27. 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
  28. 28.
    • Keller JP, Chang HH, Strickland MJ, Szpiro AA. Measurement error correction for predicted spatiotemporal air pollution exposures. Epidemiology. 2017;28:338–45. This study includes an application of both the non-parametric bootstrap and the parameter bootstrap. CrossRefPubMedGoogle Scholar
  29. 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
  30. 30.
    Spiegelman D, McDermott A, Rosner B. Regression calibration method for correcting measurement-error bias in nutritional epidemiology. Am J Clin Nutr. 1997;65:1179S–86S.PubMedGoogle Scholar
  31. 31.
    Bateson TF, Wright JM. Regression calibration for classical exposure measurement error in environmental epidemiology studies using multiple local surrogate exposures. Am J Epidemiol. 2010;172:344–52.CrossRefPubMedGoogle Scholar
  32. 32.
    Liao X, Zucker DM, Li Y, Spiegelman D. Survival analysis with error-prone time-varying covariates: a risk set calibration approach. Biometrics. 2011;67:50–8.CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Stefanski LA, Cook J. Simulation extrapolation: the measurement error jackknife. J Am Stat Assoc. 1995;90:1247–56.CrossRefGoogle Scholar
  34. 34.
    Kosmidis I. Bias in parametric estimation: reduction and useful side-effects. WIREs Comput Stat. 2014;6:185–96.CrossRefGoogle Scholar
  35. 35.
    Fung KY, Krewski D. On measurement error adjustment methods in Poisson regression. Environmetrics. 1999;10:213–24.CrossRefGoogle Scholar
  36. 36.
    Dominici F, Peng RD, Barr CD, Bell ML. Protecting human health from air pollution: shifting from a single-pollutant to a multi-pollutant approach. Epidemiology. 2010;21:187–94.CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Hygiene, Epidemiology and Medical Statistics, Medical SchoolNational and Kapodistrian University of AthensAthensGreece
  2. 2.Population Health Research Institute and MRC-PHE Centre for Environment and HealthSt George’s, University of LondonLondonUK

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