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Error in air pollution exposure model determinants and bias in health estimates



Land use regression (LUR) models are commonly used in environmental epidemiology to assign spatially resolved estimates of air pollution to study participants. In this setting, estimated LUR model parameters are assumed to be transportable to a main study (the ‘‘transportability assumption’’). We provide an empirical illustration of how violation of this assumption can affect exposure predictions and bias health-effect estimates.


We based our simulation on two existing LUR models, one for nitrogen dioxide, the other for particulate matter with aerodynamic diameter <2.5 μm. We assessed the impact of error in exposure determinants used in the LUR models on resultant air pollution predictions and on bias in an exposure-health-effect estimate assessed in a hypothetical cohort. We assigned error to predictors at monitoring sites (sites used to develop the LUR model) and at prediction sites (sites for which exposure predictions were needed), allowing for different error levels between site types.


Realistic error in the exposure determinants of the selected LUR models did not induce large additional error in exposure predictions and resulted in only minor (<1%) bias in health-effect estimates. Bias in the health-effect estimates strongly increased (up to 13.6%) when exposure determinant errors were different for monitoring sites than for prediction sites.


These results suggest that only modest reductions in bias in estimated exposure health-effects are to be expected from reducing error in exposure determinants. It is important to avoid heterogeneous errors in exposure determinants between monitoring sites and prediction sites to satisfy the transportability assumption and avoid bias in estimated exposure health-effects.

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  1. Hoek G, et al. A review of land-use regression models to assess spatial variation of outdoor air pollution. Atmos Environ. 2008;42:7561–78.

    Article  CAS  Google Scholar 

  2. Eeftens 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.

    Article  CAS  Google Scholar 

  3. Beelen R, et al. Development of NO2 and NOx land use regression models for estimating air pollution exposure in 36 study areas in Europe—The ESCAPE project. Atmos Environ. 2013;72:10–23.

    Article  CAS  Google Scholar 

  4. Beelen R, et al. Effects of long-term exposure to air pollution on natural-cause mortality: an analysis of 22 European cohorts within the multicentre ESCAPE project. Lancet. 2014;383:785–95.

    Article  CAS  Google Scholar 

  5. Fuertes E, et al. Associations between particulate matter elements and early-life pneumonia in seven birth cohorts: Results from the ESCAPE and TRANSPHORM projects. Int J Hyg Environ Health. 2014;217:819–29.

    Article  CAS  Google Scholar 

  6. Stafoggia M, et al. Long-term exposure to ambient air pollution and incidence of cerebrovascular events: results from 11 European cohorts within the ESCAPE project. Environ Health Perspect. 2014;122:919–25.

    Article  Google Scholar 

  7. Fuks KB, et al. Arterial blood pressure and long-term exposure to traffic-related air pollution: an analysis in the European Study of Cohorts for Air Pollution Effects (ESCAPE). Environ Health Perspect. 2014;122:896–905.

    Article  Google Scholar 

  8. Cai Y, et al. Cross-sectional associations between air pollution and chronic bronchitis: an ESCAPE meta-analysis across five cohorts. Thorax. 2014;69:1005–14.

    Article  Google Scholar 

  9. Adam M, et al. Adult lung function and long-term air pollution exposure. ESCAPE: a multicentre cohort study and meta-analysis. Eur Respir J. 2014;45:38–50.

    Article  Google Scholar 

  10. Cesaroni G, et al. Long term exposure to ambient air pollution and incidence of acute coronary events: prospective cohort study and meta-analysis in 11 European cohorts from the ESCAPE Project. BMJ. 2014;348:f7412.

    Article  Google Scholar 

  11. Pedersen M, et al. Ambient air pollution and low birthweight: A European cohort study (ESCAPE). Lancet Respir Med. 2013;1:695–704.

    Article  CAS  Google Scholar 

  12. Gryparis A, Paciorek CJ, Zeka A, Schwartz J, Coull BA. Measurement error caused by spatial misalignment in environmental epidemiology. Biostatistics. 2009;10:258–74.

    Article  Google Scholar 

  13. Spiegelman D. Approaches to uncertainty in exposure assessment in environmental epidemiology. Annu Rev Public Health. 2010;31:149–63.

    Article  Google Scholar 

  14. Carroll, RJ, Ruppert, D, Stefanski, LA, Crainiceanu, CM. Measurement error in nonlinear models: A modern perspective, 2nd ed. 2006. Boca Raton, FL: Chapman & Hall/CRC Press.

  15. Cyrys J, et al. Variation of NO2 and NOx concentrations between and within 36 European study areas: Results from the ESCAPE study. Atmos Environ. 2012;62:374–90.

    Article  CAS  Google Scholar 

  16. Eeftens M, et al. Spatial variation of PM2.5, PM10, PM2.5 absorbance and PMcoarse concentrations between and within 20 European study areas and the relationship with NO2—Results of the ESCAPE project. Atmos Environ. 2012;62:303–17.

    Article  CAS  Google Scholar 

  17. Mendoza D, et al. Implications of uncertainty on regional CO2 mitigation policies for the U.S. onroad sector based on a high-resolution emissions estimate. Energy Policy. 2013;55:386–95.

    Article  Google Scholar 

  18. Kadaster. Address Coordinates Netherlands (ACN)—Quality survey 2000 [Adres Coordinaten Nederland (ACN)—Kwaliteitsonderzoek 2000]. 2001. Apeldoorn, The Netherlands: Kadaster.

  19. Beekhuizen J, et al. Impact of input data uncertainty on environmental exposure assessment models: A case study for electromagnetic field modelling from mobile phone base stations. Environ Res. 2014;135C:148–55.

    Article  Google Scholar 

  20. Jacquemin B, et al. Impact of geocoding methods on associations between long-term exposure to urban air pollution and lung function. Environ Health Perspect. 2013;121:1054–60.

    Article  Google Scholar 

  21. Brunekreef B, et al. The prevention and incidence of asthma and mite allergy (PIAMA) birth cohort study: Design and first results. Pediatr Allergy Immunol. 2002;13(Suppl 1):55–60.

    Article  Google Scholar 

  22. Gehring U, et al. Air pollution exposure and lung function in children: The ESCAPE project. Environ Health Perspect. 2013;121:1357–64.

    Article  Google Scholar 

  23. Hagemann R, et al. Spatial variability of particle number concentrations and NOx in the Karlsruhe (Germany) area obtained with the mobile laboratory ‘AERO-TRAM’. Atmos Environ. 2014;94:341–52.

    Article  CAS  Google Scholar 

  24. Mazumdar S, Rushton G, Smith BJ, Zimmerman DL, Donham KJ. Geocoding accuracy and the recovery of relationships between environmental exposures and health. Int J Health Geogr. 2008;7:13.

    Article  Google Scholar 

  25. Szpiro AA, Sheppard L, Lumley T. Efficient measurement error correction with spatially misaligned data. Biostatistics. 2011;12:610–23.

    Article  Google Scholar 

  26. Szpiro AA, Paciorek CJ. Measurement error in two-stage analyses, with application to air pollution epidemiology. Environmetrics. 2013;24:501–17.

    Article  Google Scholar 

  27. Lopiano KK, Young LJ, Gotway CA. A comparison of errors in variables methods for use in regression models with spatially misaligned data. Stat Methods Med Res. 2011;20:29–47.

    Article  Google Scholar 

  28. Lopiano KK, Young LJ, Gotway CA. Estimated generalized least squares in spatially misaligned regression models with Berkson error. Biostatistics. 2013;14:737–51.

    Article  Google Scholar 

  29. Chang HH, Peng RD, Dominici F. Estimating the acute health effects of coarse particulate matter accounting for exposure measurement error. Biostatistics. 2011;12:637–52.

    Article  Google Scholar 

  30. Basagaña X, et al. Measurement error in epidemiologic studies of air pollution based on land-use regression models. Am J Epidemiol. 2013;178:1342–6.

    Article  Google Scholar 

  31. Alexeeff SE, et al. Consequences of kriging and land use regression for PM2.5 predictions in epidemiologic analyses: Insights into spatial variability using high-resolution satellite data HHS Public Access. J Expo Sci Env Epidemiol. 2015;2540:138–44.

    Article  Google Scholar 

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This work was supported by grant 211250 (ESCAPE) and grant 308610 (EXPOSOMICS) from the European Community’s Seventh Framework Program (FP7/2007–2011).

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Correspondence to Jelle Vlaanderen.

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Vlaanderen, J., Portengen, L., Chadeau-Hyam, M. et al. Error in air pollution exposure model determinants and bias in health estimates. J Expo Sci Environ Epidemiol 29, 258–266 (2019).

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