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The impact of measurement error in modelled ambient particles exposures on health effect estimates in multi-level analysis: a simulation study.

Research output: Contribution to journalArticle

Klea Katsouyanni, Evangelia Samoli, Barbara Butland, Sophia Rodopoulou, Richard Atkinson, Benjamin Barratt, Sean Beevers, Andrew Beddows, Konstantina Dimakopoulou, Joel Schwartz, Mahdieh Danesh Yasdi

Original languageEnglish
Number of pages20
JournalEnvironmental Epidemiology
Accepted/In press26 Mar 2020

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Abstract

Background: Various spatio-temporal models have been proposed for predicting ambient particulate exposure for inclusion in epidemiological analyses. We investigated the effect of measurement error in the prediction of particulate matter with diameter <10 μm (PM10) and <2.5μm (PM2.5) concentrations on the estimation of health effects. Methods: We sampled 1,000 small administrative areas in London, U.K., and simulated the “true” underlying daily exposure surfaces for PM10 and PM2.5 for 2009-2013 incorporating temporal variation and spatial covariance informed by the extensive London monitoring network. We added measurement error assessed by comparing measurements at fixed sites and predictions from spatio-temporal land use regression (LUR) models; dispersion models; models using satellite data and applying machine learning algorithms; and combinations of these methods through generalized additive models. Two health outcomes were simulated to assess whether the bias varies with the effect size. We applied multi-level Poisson regression to simultaneously model the effect of long- and short-term pollutant exposure. For each scenario, we ran 1,000 simulations to assess measurement error impact on health effect estimation. Results: For long-term exposure to particles we observed bias towards the null, except for traffic PM2.5 for which only LUR underestimated the effect. For short-term exposure results were variable between exposure models and bias ranged from -11% (underestimate) to 20% (overestimate) for PM10 and of -20% to 17% for PM2.5. Integration of models performed best in almost all cases. Conclusions: No single exposure model performed optimally across scenarios. In most cases measurement error resulted in attenuation of the effect estimate.

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