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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
Status: 
Ongoing
Abstract

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

Klea Katsouyanni1,3, Dimitris Evangelopoulos1, Benjamin Barratt1, Hanbin Zhang1, Barbara K. Butland2, Evangelia Samoli3, Audrey deNazelle1, Sean Beevers1, Heather Walton1, Evangelos Evangelou1, Joel Schwartz4 

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

Background. The importance of within-city between-person variability in exposure to air pollution and the measurement error (ME) associated with the use of surrogate measures to estimate individual exposure has been recognized, while in large cohorts, personal measurements are not feasible. The overall aim of MELONS is to evaluate whether increasingly detailed exposure estimates for large scale studies are useful and effective in yielding better health effect estimates of outdoor air pollution. Specific objectives are: (i) Develop long-term estimates of personal exposures to PM2.5, BC, NO2, and ozone from outdoor sources based on highly detailed exposure measurement datasets already available in London, UK. (ii) Use existing ambient models for estimating concentrations and long-term exposures. (iii) Assess the impact of ME of each method on effect estimates using simulated datasets. (iv) Apply the different exposure estimation methods in a London cohort, compare their performance and correct for ME.

Methods. Personal monitoring from studies on subjects with different demographic characteristics – school children, healthy adults, professional drivers and chronic obstructive pulmonary disease (COPD) patients – are utilized to derive exposure to pollutants from indoor and outdoor sources separately and then extrapolated to annual exposure estimates. In addition to the personal measurements and assessment of exposure from indoor/outdoor origin, we will estimate the subjects’ exposures using various models (such as a hybrid model including information from dispersion and land use regression models and satellite data, and models incorporating age-specific time-activity patterns 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. We integrated a 18,000 person-days pollutant database with linked GPS measurements, performed location tagging on one-minute data and calculated monthly home infiltration efficiency. A methodology has been developed to separate subject exposure to pollutants from outdoor and indoor sources. For the COPD patients, daily mean (±SD) personal exposure to PM2.5 from indoor and outdoor sources was 4.9 ± 3.7 µg/m3 and 8.1 ± 6.1 µg/m3 respectively.

Conclusions. The results of MELONS improve insight into how persons of diverse demographics are exposed to outdoor sources of pollution. Despite COPD patients spending high proportions of time indoors, their exposure to PM2.5 from outdoor sources is still greater than that from indoor sources. Results will also provide information on the degree to which different exposure assessment methods provide advantages for valid and accurate effect estimates. We will aim to answer questions like: are large personal exposure measurement campaigns necessary for epidemiological studies?