Long-term associations between time-varying exposure to ambient PM2.5 and mortality: an analysis of the UK Biobank.

Background: Evidence for long-term mortality risks of PM2.5 comes mostly from large administrative studies with incomplete individual information and limited exposure definitions. Here we assess PM2.5-mortality associations in the UK Biobank cohort using detailed information on confounders and exposure.

Methods: We reconstructed detailed exposure histories for 498,090 subjects by linking residential data with high-resolution PM2.5 concentrations from spatio-temporal machine learning models. We split the time-to-event data and assigned yearly exposures over a lag window of 8 years. We fitted Cox proportional hazard models with time-varying exposure controlling for contextual and individual-level factors, as well as trends. In secondary analyses, we inspected the lag structure using distributed lag models and compared results with alternative exposure sources and definitions.

Results: In fully adjusted models, an increase of 10 μg/m³ in PM2.5 was associated with hazard ratios (HRs) of 1.27 (95%CI: 1.06-1.53) for all-cause, 1.24 (1.03-1.50) for non-accidental, 2.07 (1.04-4.10) for respiratory, and 1.66 (0.86-3.19) for lung cancer mortality. We found no evidence of association with cardiovascular deaths (HR=0.88, 95%CI: 0.59-1.31). We identified strong confounding by both contextual- and individual-level lifestyle factors. The distributed lag analysis suggested differences in relevant exposure windows across mortality causes. Using more informative exposure summaries and sources resulted in higher risk estimates.

Conclusions: We found associations of long-term PM2.5 exposure with all-cause, non-accidental, respiratory, and lung cancer mortality, but not with cardiovascular mortality. This study benefits from finely reconstructed time-varying exposures and extensive control for confounding, further supporting a plausible causal link between long-term PM2.5 and mortality.