SummaryEnvironmental exposures are expected to change dramatically with climate change, including air pollution and temperature exposures. Previous research has demonstrated that there are associations between spikes in temperature and air pollution levels and increased incidence of pulmonary disease and pulmonary-related hospitalizations. However, there is limited research on the impact of environmental and socioeconomic neighborhood-level factors on pulmonary disease and forecasting future disease patterns under different climate change scenarios. Using baseline and follow-up data from the German National Cohort (NAKO), including self-reported pulmonary disease outcomes and Fractional exhaled Nitric Oxide (FeNO), we therefore aim to model the impact of air pollution and temperature on pulmonary outcomes, examine the role of neighborhood-level factors as mediators, and forecast pulmonary disease outcomes under climate change scenarios. Air pollution and temperature data, as well as further environmental exposures and neighborhood-level factors linked to the geocoded participants' home addresses, are available from the Environmental Data Unit (EDU). Various machine learning and deep learning algorithms will be used to model these associations, and model performance will be compared. The potential mediating effect of neighborhood-level factors, including neighborhood deprivation and socioeconomic status, will be assessed. After model selection, predicted air pollution and temperature conditions under climate change will be applied to the model to forecast pulmonary disease outcomes.

Keywords COPD air-pollution asthma climate-change environment respiratory-status temperature

InstitutionsHelmholtz Zentrum München, Helmtolz Zentrum Munchen, Helmholtz Munich, IUF - Leibniz Institut für umweltmedizinische Forschung