Accountability studies of air pollution and health effects: lessons learned and recommendations for future natural experiment opportunities.
Accountability studies of air pollution and health effects: lessons learned and recommendations for future natural experiment opportunities.
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DOI：
10.1016/j.envint.2016.12.019
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发表时间：
2017-03
影响因子：
11.8
通讯作者：
Rich DQ
中科院分区：
文献类型：
--
作者：
Rich DQ
为了解决空气污染和健康影响的观察流行病学研究的局限性，包括时间和空间因素的残留混杂，几项研究利用了“自然实验”，即环境政策或空气质量干预导致环境空气污染浓度降低。研究人员研究了受这些空气质量改善影响的人口是否在各种健康指数方面也有所改善(例如，发病率/死亡率降低)。在这篇文章中，我回顾了过去在北京、亚特兰大、伦敦、爱尔兰和其他地方进行的关键问责研究和过去几年中进行的新研究，描述了每个研究设计和分析的优点和局限性。随着新的“自然实验”机会的出现，在规划一项新的问责研究时，应该应用从这些研究中学到的几个教训。将干预期间的健康结果与目标人群干预前后的健康结果进行比较，并使用对照人口来评估目标人口的任何时间变化是否也出现在未受空气质量改善影响的人群中，这应该有助于将这些长期趋势造成的残余混淆降至最低。使用人群的详细健康记录，或预期收集的相关机械生物标记物的数据，再加上此类发病率/死亡率数据，可以更彻底地评估干预是否有益于影响社区的健康，如果是，通过什么机制(S)。此外，对一大批空气污染物进行前瞻性测量可能会更彻底地了解污染物的来源(S)对观察到的任何健康益处负有责任。还讨论了在每篇论文中使用多种统计分析方法的重要性，以及空气污染/结果关联的时间可能如何影响这些设计特征中最重要的因素的差异。基于这些和其他经验教训，研究人员可能会对空气质量干预或行动的特定原因的健康影响提供更严格的流行病学评估。
To address limitations of observational epidemiology studies of air pollution and health effects, including residual confounding by temporal and spatial factors, several studies have taken advantage of ‘natural experiments’, where an environmental policy or air quality intervention has resulted in reductions in ambient air pollution concentrations. Researchers have examined whether the population impacted by these air quality improvements, also experienced improvements in various health indices (e.g. reduced morbidity/mortality). In this paper, I review key accountability studies done previously and new studies done over the past several years in Beijing, Atlanta, London, Ireland, and other locations, describing study design and analysis strengths and limitations of each. As new ‘natural experiment’ opportunities arise, several lessons learned from these studies should be applied when planning a new accountability study. Comparison of health outcomes during the intervention to both before and after the intervention in the population of interest, as well as use of a control population to assess whether any temporal changes in the population of interest were also seen in populations not impacted by air quality improvements, should aid in minimizing residual confounding by these long term time trends. Use of either detailed health records for a population, or prospectively collected data on relevant mechanistic biomarkers coupled with such morbidity/mortality data may provide a more thorough assessment of if the intervention beneficially impacted the health of the community, and if so by what mechanism(s). Further, prospective measurement of a large suite of air pollutants may allow a more thorough understanding of what pollutant source(s) is/are responsible for any health benefit observed. The importance of using multiple statistical analysis methods in each paper and the difference in how the timing of the air pollution/outcome association may impact which of these design features is most important is also discussed. Based on these and other lessons learned, researchers may provide a more epidemiologically rigorous evaluation of cause-specific health impacts of an air quality intervention or action.
影响因子：
3.1
作者：
Granslo JT;Bråtveit M;Hollund BE;Irgens Å;Svanes C;Magerøy N;Moen BE
通讯作者：
Moen BE