You are here

Investigating the consequences of Measurement Error of gradually more sophisticated long-term personal exposure models in assessing health effects: the LOndon Study (MELONS)

Principal Investigator: 

King's College London, United Kingdom

This study will investigate the consequences of measurement error on estimates of health effects of long-term exposure to outdoor air pollution in London by developing increasingly sophisticated exposure models.The investigators plan to compare exposure models that account for mobility, are based on exposure estimates at the residential address, and are based on concentrations measured at the nearest air pollution monitor.

Funded under

Investigating the Consequences of Measurement Error of Gradually More Sophisticated Long-Term Personal Exposure Models in Assessing Health Effects: The LONdon Study (MELONS)

Klea Katsouyanni1,4, Benjamin Barratt1, Dimitris Evangelopoulos1, Sean Beevers1, Heather Walton1, Hanbin Zhang1, Barbara Butland2, Audrey deNazelle3, Evangelos Evangelou3, Evangelia Samoli4, Joel Schwartz5

1King's College London, UK; 2St. George's University of London, UK; 3Imperial College London, UK; 4National and Kapodistrian University of Athens, Greece; 5Harvard T.H. Chan School of Public Health, Boston, MA, USA

Background: The importance of within-city between-person variability in exposure and the measurement error (ME) associated with the use of few monitors to estimate individual exposure has been recognized. The large number of participants in cohorts does not allow measuring personal exposure of all participants longitudinally. Thus, models have been increasingly used to estimate individualized long-term exposures. The overall aim of MELONS is to evaluate whether increasingly detailed estimates of long-term individual exposure to pollutants for large scale studies are useful and effective in yielding better estimates of the health effects of exposure to outdoor air pollution. The study will be implemented in London, a city with relatively low air pollution. Specific objectives are: Develop long-term estimates of personal exposures to outdoor air pollution based on highly detailed exposure measurement datasets already available. Use existing models for estimating concentrations, including combinations of dispersion and models based on land use variables and satellite data with the use of machine learning techniques, and models based on combining ambient and micro-environmental modelling with time activity patterns to estimate long-term exposure of individuals to particulate matter, black carbon, nitrogen dioxide and ozone. Assess the impact of ME of each method on effect estimates using simulated datasets. Apply the different exposure estimation methods in a London cohort, compare their performance and correct for ME.

Methods: We will use personal monitoring with separate estimates for exposure to pollutants from indoor and outdoor sources, conducted within the following projects, concerning groups of subjects with different characteristics: COPE; PASTA (Physical Activity Through Sustainable Transport Approaches) London branch project; DEMist; Breathe London: Wearables. All 18,000 person-days of measurements also have temperature and relative humidity measurements and GPS information. For the subjects above we will estimate their exposure to pollutants using various models (such as dispersion, Land Use Regression, combinations with satellite data, models incorporating time-activity patterns by age group in the same population). Additionally, a simulation study to assess the consequences of ME on the effect estimates will be applied. We will use the UK Biobank cohort to estimate exposures of participants using the various methods, compare health effects and correct for ME.

Results: Previous studies which investigated the consequences of ME showed that they lead to attenuation of effect estimates in the large majority of circumstances, but the data which formed the basis of these were not so detailed and extensive. The results of MELONS will improve insight into how persons are exposed and how the different exposure methods provide advantages for valid and accurate effect estimates.

Conclusions: We will aim to provide answers to questions like: do we need to conduct large expensive personal exposure measurement campaigns? Is the standard use of nearest monitor or of using outdoor modelled concentrations at residential address leading to more bias compared to more personalized/ sophisticated methods?