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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

Poster abstract for HEI Annual Conference 2022

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, Benjamin Barratt1, Dimitris Evangelopoulos1, Hanbin Zhang1, Barbara Butland2, Evangelia Samoli3, Dylan Wood1, Sean Beevers1, Heather Walton1, Audrey de Nazelle1, Evangelos Evangelou1, Joel Schwartz4

1Imperial College London, London, UK; 2St George's University of London, London, UK; 3National and Kapodistrian University of Athens; Athens, Greece; 4Harvard School of Public Health, Boston, Massachusetts, USA

Background: The overall aim of MELONS is to evaluate whether increasingly detailed estimates of long-term individual exposure to pollutants, such as PM2.5, NO2, O3, CO and black carbon, for large-scale studies are useful and effective in yielding better estimates of the health effects of exposure to outdoor air pollution.

Methods: We used personal monitoring from four previous exposure measurement campaigns in 427 participants (validation datasets), including COPD patients (COPE), professional drivers (DEMiSt), schoolchildren (BLW) and healthy adults (PASTA) conducted in London. We compiled over 200 million validated 1-minute mean measurements of gaseous and particulate pollutants, clustered GPS measurements, performed location tagging and calculated monthly home infiltration efficiency in order to separate participants’ personal exposure into indoor- and outdoor-generated pollution. We are also collecting information from “surrogate” exposure assessment methods, such as modelled residential outdoor concentrations, fixed site measurements from the nearest monitor and modelled concentrations incorporating typical time-activity patterns of the London population. By comparing these surrogate exposures with the “gold-standard” outdoor-generated personal exposure from the validation datasets, we will be able to quantify the exposure measurement error (ME) for each pollutant and understand its type (Classical or Berkson). These ME structures will be used as input in a simulation study through which we will assess the consequences of ME of each measurement/modelling method on the effect estimates for health outcomes. An application on the UK-Biobank cohort will be performed and the health effect estimates will be corrected for ME bias based on the outputs of the simulations.

Results of first 18 months work: COPD patients spent on average most of their time at home (93%) and only 6% outdoors, while DEMiSt, BLW and PASTA participants spent approximately 55% on average at home and 10% outdoors for schoolchildren and healthy adults, 29% outdoors for professional drivers. These figures have an impact in their exposures to pollution, with preliminary results suggesting that while total PM2.5 exposure for COPE and BLW is 12.1±7.2 μg/m3 and 9.9±10.1 μg/m3, indoor-generated personal exposure is 6.5±5.2 μg/m3 and 3.9±7.6 μg/m3, and outdoor-generated is 5.5±4.0 μg/m3 and 5.8±5.0 μg/m3 respectively. Our test simulation runs suggest that the epidemiological estimates can be highly biased towards the null (up to 60%) due to ME.

Conclusions: Our findings from the first 18 months show that separating indoor- and outdoor-generated pollution is important in using a reference method, but also for correcting the epidemiological estimates when surrogate exposures are used.

Future work: We will assess the impact of measurement error from different surrogate measures on the health effect estimates and apply correction methods using the UK-Biobank.