Butland Barbara K, Samoli Evangelia, Atkinson Richard W, Barratt Benjamin, Beevers Sean D, Kitwiroon Nutthida, Dimakopoulou Konstantina, Rodopoulou Sophia, Schwartz Joel D, Katsouyanni Klea
Population Health Research Institute, St George's, University of London, London, United Kingdom.
Department of Hygiene, Epidemiology and Medical Statistics, Medical School, National and Kapodistrian University of Athens, Athens, Greece.
Environ Epidemiol. 2020 May 13;4(3):e093. doi: 10.1097/EE9.0000000000000093. eCollection 2020 Jun.
Using modeled air pollutant predictions as exposure variables in epidemiological analyses can produce bias in health effect estimation. We used statistical simulation to estimate these biases and compare different air pollution models for London.
Our simulations were based on a sample of 1,000 small geographical areas within London, United Kingdom. "True" pollutant data (daily mean nitrogen dioxide [NO] and ozone [O]) were simulated to include spatio-temporal variation and spatial covariance. All-cause mortality and cardiovascular hospital admissions were simulated from "true" pollution data using prespecified effect parameters for short and long-term exposure within a multilevel Poisson model. We compared: land use regression (LUR) models, dispersion models, LUR models including dispersion output as a spline (hybrid1), and generalized additive models combining splines in LUR and dispersion outputs (hybrid2). Validation datasets (model versus fixed-site monitor) were used to define simulation scenarios.
For the LUR models, bias estimates ranged from -56% to +7% for short-term exposure and -98% to -68% for long-term exposure and for the dispersion models from -33% to -15% and -52% to +0.5%, respectively. Hybrid1 provided little if any additional benefit, but hybrid2 appeared optimal in terms of bias estimates for short-term (-17% to +11%) and long-term (-28% to +11%) exposure and in preserving coverage probability and statistical power.
Although exposure error can produce substantial negative bias (i.e., towards the null), combining outputs from different air pollution modeling approaches may reduce bias in health effect estimation leading to improved impact evaluation of abatement policies.
在流行病学分析中使用模拟的空气污染物预测值作为暴露变量，可能会在健康效应估计中产生偏差。我们使用统计模拟来估计这些偏差，并比较伦敦的不同空气污染模型。
我们的模拟基于英国伦敦内1000个小地理区域的样本。模拟了“真实”的污染物数据（每日平均二氧化氮[NO]和臭氧[O]），以纳入时空变化和空间协方差。在多水平泊松模型中，使用预先指定的短期和长期暴露效应参数，从“真实”污染数据模拟全因死亡率和心血管疾病住院人数。我们比较了：土地利用回归（LUR）模型、扩散模型、将扩散输出作为样条纳入的LUR模型（混合1），以及结合LUR样条和扩散输出的广义相加模型（混合2）。使用验证数据集（模型与固定站点监测器）来定义模拟场景。
对于LUR模型，短期暴露的偏差估计范围为-56%至+7%，长期暴露为-98%至-68%；对于扩散模型，短期暴露偏差估计范围为-33%至-15%，长期暴露为-52%至+0.5%。混合1几乎没有额外益处，但在短期（-17%至+11%）和长期（-28%至+11%）暴露的偏差估计以及保持覆盖概率和统计效力方面，混合2似乎是最优的。
尽管暴露误差可能会产生显著的负偏差（即偏向无效值），但结合不同空气污染建模方法的输出可能会减少健康效应估计中的偏差，从而改进减排政策的影响评估。
