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OP XII – 1 Assessing the cumulative health effect following short term exposure to multiple pollutants: an evaluation of methodological approaches using simulations and real data
  1. Sophia Rodopoulou1,
  2. Klea Katsouyanni1,2,
  3. Pagona Lagiou1,3,
  4. Evangelia Samoli1
  1. 1National and Kapodistrian University of Athens, Department of Hygiene, Epidemiology and Medical Statistics, Faculty of Medicine, Athens, Greece
  2. 2King’s College, Department of Primary Care and Public Health Sciences and Environmental Research Group, London, UK
  3. 3Harvard T.H. Chan School of Public Health, Department of Epidemiology, Boston, MA, USA


Background/aim Assessment of the cumulative effect of correlated exposures is an open methodological issue in environmental epidemiology. Previous studies applied regression models with interaction terms or dimension reduction methods. The joint effect of pollutants has been also evaluated using weighted exposure scores with weights based on the strength of the specific pollutant-health outcomes associations.

Methods We compared three approaches addressing multipollutant exposures in epidemiological models: main effects models, the adaptive least absolute shrinkage and selection operator (LASSO) and a weighted exposure score. We assessed the performance of the methods by simulations under various scenarios for the pollutants’ correlations. We further applied the three methods to time series data from Athens, Greece for 2007–2012 to investigate the combined effect of short-term exposure to six regulated pollutants on all-cause and respiratory mortality.

Results The weighted exposure score provided the least biassed cumulative estimate under all correlation scenarios for both mortality outcomes. The adaptive LASSO performed well in the case of low and medium correlation between exposures while models including all exposures linearly seriously biassed the estimate of interest. In the real data application, the cumulative effect estimate on overall mortality was similar between approaches ranging from 1.12% increase in main effects models to 0.73% in the score, while the cumulative effect on respiratory mortality resulted in variable estimates, that ranged from −0.61% increase for adaptive LASSO to 2.77% for the score approach, with overlapping confidence intervals.

Conclusion The use of a weighted exposure score to address cumulative effects of correlated metrics may perform well under different exposure correlation structures and different variability in the health outcomes. Future work should assess the performance of methods under variable lag structures per pollutant or non-linear associations between pollutants and outcomes.

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