Comparative evaluation of Spatio-Temporal Exposure Assessment Methods for estimating the health effects of air pollution (STEAM)

用于评估空气污染对健康影响的时空暴露评估方法的比较评估（STEAM）

• 批准号: MR/N014464/1
• 负责人: Klea Katsouyanni
• 金额: $ 61.76万
• 依托单位: King's College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2016
• 资助国家: 英国
• 起止时间: 2016 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FN014464%2F1
• 关键词: Comparative; evaluation; Spatio; Temporal; Exposure

Background: Epidemiological studies have linked long- and short-term outdoor air pollution exposures to increased risks of death and morbidity. Cohort studies evaluate the health effects of long-term exposure by exploiting spatial heterogeneity in annual air pollution concentrations whereas time-series studies evaluate acute effects exploiting temporal variability in pollution concentrations. The extent to which the adverse health effects reported by these different study designs overlap has important implications for health impact assessment and policy development and to date this has not been investigated outside the US. In the past, both study designs have been limited by the availability of monitored pollution data but the development of dispersion and land use regression models have improved exposure assessment in cohort studies and enabled the investigation of a broader range of pollutants. The extension of these models to provide spatially resolved daily estimates of pollutant concentrations will enable an integrated assessment of health effects arising from both long- and short-term exposures to a wide range of pollutants and with applicability to locations with sparse or no monitoring.Objectives: 1.To develop, validate and compare dispersion, land use regression and satellite data based pollution models for London at both fine spatial (postcode level) and temporal (daily) scales. 2. To assess the performance of these methods and their combinations in estimating the health effects of long and short-term exposures to air pollutants. 3. To use the optimum integrated exposure method to estimate the relative importance of the effects of long-and short-term exposure for selected health outcomes. To achieve these objectives the project will use the expertise developed within the MRC-PHE Centre for Environment and Health and will need to draw upon the expertise of pioneering international groups (in the U.S. & Greece) in modelling, simulation studies and time series analyses.Methods: Models will be developed for particulate and gaseous pollutants and validated using measured concentrations from the London Air Quality Network. Their applicability to locations with sparse or no monitoring will be assessed using sequentially smaller parts of the available exposure information. An extensive simulation study, in which the "true" effect is known, will investigate the implications of using each exposure method on the health effect estimation (precision and bias).To enable proof of concept for the simultaneous estimation of long- and short-term effects on various health outcomes, the analysis of selected endpoints including GP consultations, hospital admissions and mortality data will be applied.Innovation aspects:1. Application of air pollution modelling approaches at a fine time and spatial resolution2. Integration of the different exposure assessment methods for optimizing their performance.3. Use of simulation methods to evaluate simultaneously the performance of models in assessing the effects of long- and short-term exposure to particulate and gaseous pollutants.4. Simultaneous estimation of health effects of long- and short-term exposure to air pollution in existing large health data sets to enable a comparison of their relative importance.Policy implications:1. Information on the advantages and disadvantages of each exposure model will inform the optimum extent and density of monitoring systems and other inputs to local models.2. The simultaneous estimation of the effects of short and long-term exposures will provide a rationale for the balance between emergency short-term action and long-term pollution management interventions.3. Informing the development of modelling in areas of Europe and the World where monitoring is not so dense.4. Informing health studies about the comparative importance of short vs long-term effects in areas of Europe and the World where health outcome data bases are limited

背景：流行病学研究表明，长期和短期的室外空气污染暴露与死亡和发病风险增加有关。队列研究通过利用年度空气污染浓度的空间异质性来评估长期暴露对健康的影响，而时间序列研究则利用污染浓度的时间变异性来评估急性影响。这些不同的研究设计报告的不良健康影响的重叠程度对健康影响评估和政策制定具有重要意义，迄今为止，美国以外的地区尚未对此进行调查。在过去，这两种研究设计都受到监测污染数据的限制，但分散和土地利用回归模型的发展改善了队列研究中的暴露评估，并使更广泛的污染物的调查成为可能。将这些模型扩展到提供污染物浓度的空间分辨每日估计数，将能够综合评估长期和短期接触各种污染物所产生的健康影响，并适用于监测稀少或没有监测的地点。1.开发、验证和比较离散度，在精细空间（邮政编码级别）和时间（每日）尺度上，基于土地利用回归和卫星数据的伦敦污染模型。2.评估这些方法及其组合在估计长期和短期暴露于空气污染物的健康影响方面的性能。3.使用最佳综合暴露方法估计长期和短期暴露对选定健康结果影响的相对重要性。为了实现这些目标，该项目将使用的专业知识开发的MRC-PHE中心的环境与健康，将需要借鉴的专业知识，开拓国际集团（在美国和希腊）在建模，模拟研究和时间序列analysis.Methods：模型将开发颗粒和气体污染物和验证使用测量浓度从伦敦空气质量网络。将使用现有暴露信息的顺序较小部分来评估其对监测稀疏或无监测地点的适用性。一项广泛的模拟研究，其中“真实”效应是已知的，将调查使用每种暴露方法对健康影响估计的影响（精度和偏差）。为了能够同时估计对各种健康结果的长期和短期影响的概念证明，将应用选定的终点分析，包括GP咨询，住院和死亡率数据。创新方面：1.在良好的时间和空间分辨率上应用空气污染模拟方法2。整合不同的暴露评估方法以优化其性能.使用模拟方法同时评价模型在评估长期和短期接触颗粒和气体污染物的影响方面的性能。在现有的大型健康数据集中同时估计长期和短期暴露于空气污染对健康的影响，以比较其相对重要性。政策含义：1.关于每种接触模式的优缺点的信息将为监测系统的最佳范围和密度以及对当地模式的其他投入提供信息。同时估计短期和长期接触的影响将为短期紧急行动和长期污染管理干预之间的平衡提供理论依据。为欧洲和世界上监测不那么密集的地区的建模发展提供信息。在欧洲和世界上健康结果数据库有限的地区，向健康研究报告短期与长期影响的比较重要性

项目成果

Predicting Fine Particulate Matter (PM2.5) in the Greater London Area: An Ensemble Approach using Machine Learning Methods

• DOI: 10.3390/rs12060914
• 发表时间: 2020-03-01
• 期刊: REMOTE SENSING
• 影响因子: 5
• 作者: Yazdi, Mahdieh Danesh;Kuang, Zheng;Schwartz, Joel
• 通讯作者: Schwartz, Joel

Additional file 1: of Measurement error in a multi-level analysis of air pollution and health: a simulation study

附加文件 1：空气污染与健康多级分析中的测量误差：模拟研究

• DOI: 10.6084/m9.figshare.7724735
• 发表时间: 2019
• 作者: Butland B

Spatio-temporal associations of air pollutant concentrations, GP respiratory consultations and respiratory inhaler prescriptions: a 5-year study of primary care in the borough of Lambeth, South London.

• DOI: 10.1186/s12940-021-00730-1
• 发表时间: 2021-05-07
• 期刊: Environmental health : a global access science source
• 作者: Ashworth M;Analitis A;Whitney D;Samoli E;Zafeiratou S;Atkinson R;Dimakopoulou K;Beavers S;Schwartz J;Katsouyanni K;STEAM project research group
• 通讯作者: STEAM project research group

Additional file 2: of Measurement error in a multi-level analysis of air pollution and health: a simulation study

附加文件 2：空气污染与健康多级分析中的测量误差：模拟研究

• DOI: 10.6084/m9.figshare.7724738
• 发表时间: 2019
• 作者: Butland B

Spatio-temporal associations of air pollutants concentrations and GP consultations by cause and inhaler prescriptions in the Borough of Lambeth, London

伦敦兰贝斯区空气污染物浓度的时空关联以及按原因和吸入器处方进行的全科医生咨询

• DOI: 10.1097/01.ee9.0000605720.85898.bf
• 发表时间: 2019
• 期刊: Environmental Epidemiology
• 影响因子: 3.6
• 作者: A A