ERS International Congress 2023: highlights from the Epidemiology and Environment Assembly.

In this article, early career members of the Epidemiology and Environment Assembly of the European Respiratory Society (ERS) summarise a selection of four poster and oral sessions from the ERS 2023 Congress. The topics covered the following areas: micro- and macro-environments and respiratory health, occupational upper and lower airway diseases, selected tobacco and nicotine research, and multimorbidity in people with lung diseases. The topics and studies covered in this review illustrate the broad range of the multifaceted research taking place within Assembly 6, from the identification of indoor and outdoor environmental risk factors for the development and worsening of respiratory diseases to the concerningly increasing use of nicotine products and their health consequences beyond respiratory health and comorbidity in respiratory diseases.

Residential exposure to microbial emissions from livestock farms: Implementation and evaluation of land use regression and random forest spatial models.

Adverse health effects have been linked with exposure to livestock farms, likely due to airborne microbial agents. Accurate exposure assessment is crucial in epidemiological studies, however limited studies have modelled bioaerosols. This study used measured concentrations in air of livestock commensals (Escherichia coli (E. coli) and Staphylococcus species (spp.)), and antimicrobial resistance genes (tetW and mecA) at 61 residential sites in a livestock-dense region in the Netherlands. For each microbial agent, land use regression (LUR) and random forest (RF) models were developed using Geographic Information System (GIS)-derived livestock-related characteristics as predictors. The mean and standard deviation of annual average concentrations (gene copies/m3) of E. coli, Staphylococcus spp., tetW and mecA were as follows: 38.9 (±1.98), 2574 (±3.29), 20991 (±2.11), and 15.9 (±2.58). Validated through 10-fold cross-validation (CV), the models moderately explained spatial variation of all microbial agents. The best performing model per agent explained respectively 38.4%, 20.9%, 33.3% and 27.4% of the spatial variation of E. coli, Staphylococcus spp., tetW and mecA. RF models had somewhat better performance than LUR models. Livestock predictors related to poultry and pig farms dominated all models. To conclude, the models developed enable enhanced estimates of airborne livestock-related microbial exposure in future epidemiological studies. Consequently, this will provide valuable insights into the public health implications of exposure to specific microbial agents.