Measurement error in time-series analysis: a simulation study comparing modelled and monitored data.
Measurement error in time-series analysis: a simulation study comparing modelled and monitored data.
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DOI：
10.1186/1471-2288-13-136
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发表时间：
2013-11-13
影响因子：
4
通讯作者：
Vieno M
中科院分区：
文献类型：
--
作者：
Butland BK;Armstrong B;Atkinson RW;Wilkinson P;Heal MR;Doherty RM;Vieno M
由于污染监测网络的稀疏，评估暴露在空气污染中的背景对健康的影响常常受到阻碍。然而，区域大气化学传输模式(CTMS)可以在地理和时间分辨率上提供覆盖全国的污染数据。我们使用统计模拟来比较稀疏监测数据中的相加测量误差与地理和时间上完整的模型数据中的相加测量误差对流行病学时间序列分析的影响。统计模拟以4个区域的理论面积为基础，每个区域由25个5千米×5千米的方格组成。在对死亡率和单一污染物之间的关系进行为期3年的泊松回归时间序列分析的背景下，我们比较了使用每日特定网格的模型数据与使用每日区域平均监测数据的误差影响。我们调查了如果我们改变每个包含监视器的区域的栅格数量，这种比较会受到怎样的影响。为了给模拟提供信息，从2003-2006年英国国家网络站点的观测监测数据和EMEP-WRF CTM生成的相应模型数据中获得了估计(例如，污染物手段)。农村和城市臭氧日最大值(8h)和城市日最大值(NO2日最大值)的站内平均相关系数分别为0.73和0.76；当地区平均值基于每个地区5或10个监测员时，对健康影响的估计几乎没有偏差。然而，由于每个区域只有一个监测点，我们的时间序列分析中的回归系数估计衰减了6%的城市背景臭氧，13%的农村臭氧，29%的城市背景LOGE(NO2)和38%的农村LOGE(NO2)。对于特定网格的模型数据，相应的数字分别为19%、22%、54%和44%，即农村LOGE(NO2)相似，但城市LOGE(NO2)更显着。即使模型和监测数据之间的相关性看起来相当强，模型数据中的附加经典测量误差也可能导致健康效应估计的明显偏差。随着基于过程的空气污染模型在流行病学时间序列分析中得到越来越广泛的应用，包括统计模拟在内的误差影响评估可能是有用的。
Assessing health effects from background exposure to air pollution is often hampered by the sparseness of pollution monitoring networks. However, regional atmospheric chemistry-transport models (CTMs) can provide pollution data with national coverage at fine geographical and temporal resolution. We used statistical simulation to compare the impact on epidemiological time-series analysis of additive measurement error in sparse monitor data as opposed to geographically and temporally complete model data. Statistical simulations were based on a theoretical area of 4 regions each consisting of twenty-five 5 km × 5 km grid-squares. In the context of a 3-year Poisson regression time-series analysis of the association between mortality and a single pollutant, we compared the error impact of using daily grid-specific model data as opposed to daily regional average monitor data. We investigated how this comparison was affected if we changed the number of grids per region containing a monitor. To inform simulations, estimates (e.g. of pollutant means) were obtained from observed monitor data for 2003–2006 for national network sites across the UK and corresponding model data that were generated by the EMEP-WRF CTM. Average within-site correlations between observed monitor and model data were 0.73 and 0.76 for rural and urban daily maximum 8-hour ozone respectively, and 0.67 and 0.61 for rural and urban loge(daily 1-hour maximum NO2). When regional averages were based on 5 or 10 monitors per region, health effect estimates exhibited little bias. However, with only 1 monitor per region, the regression coefficient in our time-series analysis was attenuated by an estimated 6% for urban background ozone, 13% for rural ozone, 29% for urban background loge(NO2) and 38% for rural loge(NO2). For grid-specific model data the corresponding figures were 19%, 22%, 54% and 44% respectively, i.e. similar for rural loge(NO2) but more marked for urban loge(NO2). Even if correlations between model and monitor data appear reasonably strong, additive classical measurement error in model data may lead to appreciable bias in health effect estimates. As process-based air pollution models become more widely used in epidemiological time-series analysis, assessments of error impact that include statistical simulation may be useful.
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影响因子：
2.1
作者：
Peng, Roger D.;Bell, Michelle L.
通讯作者：
Bell, Michelle L.
空气污染流行病学中的混淆和暴露测量误差。
影响因子：
5.1
作者：
Sheppard, Lianne;Burnett, Richard T.;Szpiro, Adam A.;Kim, Sun-Young;Jerrett, Michael;Pope, C. Arden, III;Brunekreef, Bert
通讯作者：
Brunekreef, Bert
影响因子：
1.9
作者：
Lee, Duncan;Shaddick, Gavin
通讯作者：
Shaddick, Gavin
影响因子：
2.1
作者：
Szpiro, Adam A.;Sheppard, Lianne;Lumley, Thomas
通讯作者：
Lumley, Thomas
影响因子：
10.4
作者：
Zeger, SL;Thomas, D;Cohen, A
通讯作者：
Cohen, A