From the Environment & Health Modelling (EHM) Lab, Department of Public Health Environments and Society, London School of Hygiene & Tropical Medicine, London, United Kingdom.
Department of Statistics, Computer Science and Applications "G. Parenti," University of Florence, Florence, Italy.
Epidemiology. 2025 Jan 1;36(1):1-10. doi: 10.1097/EDE.0000000000001796. Epub 2024 Oct 22.
Evidence for long-term mortality risks of PM 2.5 comes mostly from large administrative studies with incomplete individual information and limited exposure definitions. Here we assess PM 2.5 -mortality associations in the UK Biobank cohort using detailed information on confounders and exposure.
We reconstructed detailed exposure histories for 498,090 subjects by linking residential data with high-resolution PM 2.5 concentrations from spatiotemporal 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.
In fully adjusted models, an increase of 10 μg/m³ in PM 2.5 was associated with hazard ratios of 1.27 (95% confidence interval: 1.06, 1.53) for all-cause, 1.24 (1.03, 1.50) for nonaccidental, 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 (hazard ratio = 0.88, 95% confidence interval: 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.
We found associations of long-term PM 2.5 exposure with all-cause, nonaccidental, 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 PM 2.5 and mortality.
关于 PM2.5 长期死亡率风险的证据主要来自大型行政研究，这些研究存在个体信息不完整和暴露定义有限等问题。本研究利用有关混杂因素和暴露的详细信息，在英国生物库队列中评估 PM2.5 与死亡率的相关性。
通过将居住数据与时空机器学习模型生成的高分辨率 PM2.5 浓度相关联，我们为 498090 名受试者重建了详细的暴露史。我们将时间事件数据分割，并在 8 年的滞后窗口中分配每年的暴露量。我们使用时变暴露的 Cox 比例风险模型进行拟合，同时控制了上下文和个体水平的因素以及趋势。在二次分析中，我们使用分布式滞后模型检查滞后结构，并将结果与替代暴露源和定义进行比较。
在完全调整的模型中，PM2.5 每增加 10μg/m3，全因死亡率的风险比为 1.27（95%置信区间：1.06，1.53），非意外死亡率的风险比为 1.24（1.03，1.50），呼吸死亡率的风险比为 2.07（1.04，4.10），肺癌死亡率的风险比为 1.66（0.86，3.19）。我们没有发现与心血管死亡相关的证据（风险比=0.88，95%置信区间：0.59，1.31）。我们发现上下文和个体生活方式因素都存在强烈的混杂。分布式滞后分析表明，不同死亡率原因的相关暴露窗口存在差异。使用更具信息量的暴露总结和来源会导致更高的风险估计。
我们发现长期 PM2.5 暴露与全因、非意外、呼吸和肺癌死亡率相关，但与心血管死亡率无关。本研究受益于精细重建的时变暴露和广泛的混杂因素控制，进一步支持了长期 PM2.5 与死亡率之间存在潜在的因果关系。
