Abstract

Research quantifying the long-term health effects of ambient air pollution (AAP) exposure in China is scarce, limited by the need for large prospective cohorts plus assignment of individual AAP exposure. Existing studies often lack these accurate measures of individual exposure. We aimed to use spatio-temporal models to assign AAP exposure to participants within an existing prospective cohort study, the China Kadoorie Biobank (CKB). This abstract describes a pilot study using data from Suzhou, one of the 10 CKB regions.We obtained daily measurements of coarse and fine particulate matter (PM10, PM2.5), sulphur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3),from 13 fixed-site monitors in Suzhou, between 2013 and 2015. We also incorporated the spatial covariates of elevation, land use, and distance to nearest motorway. We applied Bayesian modelling using the Integrated Nested Laplace Approximation (INLA) approach to predict pollutant levels for 81 assessment centres matched to 53,260 participants, each living within approximately 1km of their respective centre. Predicted AAP estimates at each centre can then be used as proxies for individual exposure in further analyses of adverse health impacts. Predicted exposures will subsequently be incorporated as time-varying covariates in Cox proportional hazards models, examining the risk of cardiorespiratory (ICD10: I00-I99/J00-J99) mortality and morbidity associated with long-term AAP exposure. We will also be able to incorporate a wide range of potential confounders provided within CKB.AAP exposure measures that we obtain on a finer temporal and spatial resolution will allow us to minimise exposure assessment bias. Combined with the high data quality of CKB and availability of numerous potential confounding variables, this research will allow us to examine cardiorespiratory health impacts of long-term AAP exposure more accurately than previous studies.

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