Impact of exposure measurement error in air pollution epidemiology: effect of error type in time-series studies.
Impact of exposure measurement error in air pollution epidemiology: effect of error type in time-series studies.
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DOI：
10.1186/1476-069x-10-61
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发表时间：
2011-06-22
期刊：
影响因子：
--
通讯作者：
Tolbert PE
中科院分区：
文献类型：
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作者：
Goldman GT;Mulholland JA;Russell AG;Strickland MJ;Klein M;Waller LA;Tolbert PE
两种截然不同的测量误差类型是Berkson和经典。在环境空气污染的流行病学研究中，测量误差的影响预计将取决于误差类型。我们将由于仪器不精确和空间变异性导致的测量误差描述为乘法（即对数尺度上的加法），并在一系列误差类型上对其进行建模，以评估对风险比估计的影响，这些影响是在亚特兰大的一项时间序列研究中基于每个测量单元和每个四分位距（IQR）的基础上进行的。分析了12种环境空气污染物的日常测量值：NO2，NOx，O3，SO2，CO，PM10质量，PM2.5质量和PM2.5组分硫酸盐，硝酸盐，铵，元素碳和有机碳。应用半变异函数分析评估空间变异性。使用蒙特卡罗模拟，将这种空间变异性引起的误差添加到对数尺度的参考污染物时间序列中。这些时间序列中的每一个都被取幂并引入到心血管疾病急诊就诊的泊松广义线性模型中。测量误差导致所有数量（对应于不同污染物）和误差类型的风险比估计值的统计显著性降低。当模拟为经典型误差时，风险比被衰减，特别是对于主要空气污染物，每测量单位基础上的风险比平均衰减范围从18%到92%，IQR基础上的风险比平均衰减范围从18%到86%。当建模为Berkson型错误时，每个测量单位的风险比偏离零假设2%至31%，而每个IQR的风险比衰减（即偏向零）5%至34%。对于CO建模的错误量，模拟了一系列错误类型，并观察了对风险比偏倚和显著性的影响。对于乘性误差，测量误差的数量和类型都会影响空气污染流行病学中的健康效应估计。通过将仪器不精确性和空间变异性建模为不同的误差类型，我们估计了误差在一系列误差类型中的影响方向和幅度。
Two distinctly different types of measurement error are Berkson and classical. Impacts of measurement error in epidemiologic studies of ambient air pollution are expected to depend on error type. We characterize measurement error due to instrument imprecision and spatial variability as multiplicative (i.e. additive on the log scale) and model it over a range of error types to assess impacts on risk ratio estimates both on a per measurement unit basis and on a per interquartile range (IQR) basis in a time-series study in Atlanta. Daily measures of twelve ambient air pollutants were analyzed: NO2, NOx, O3, SO2, CO, PM10 mass, PM2.5 mass, and PM2.5 components sulfate, nitrate, ammonium, elemental carbon and organic carbon. Semivariogram analysis was applied to assess spatial variability. Error due to this spatial variability was added to a reference pollutant time-series on the log scale using Monte Carlo simulations. Each of these time-series was exponentiated and introduced to a Poisson generalized linear model of cardiovascular disease emergency department visits. Measurement error resulted in reduced statistical significance for the risk ratio estimates for all amounts (corresponding to different pollutants) and types of error. When modelled as classical-type error, risk ratios were attenuated, particularly for primary air pollutants, with average attenuation in risk ratios on a per unit of measurement basis ranging from 18% to 92% and on an IQR basis ranging from 18% to 86%. When modelled as Berkson-type error, risk ratios per unit of measurement were biased away from the null hypothesis by 2% to 31%, whereas risk ratios per IQR were attenuated (i.e. biased toward the null) by 5% to 34%. For CO modelled error amount, a range of error types were simulated and effects on risk ratio bias and significance were observed. For multiplicative error, both the amount and type of measurement error impact health effect estimates in air pollution epidemiology. By modelling instrument imprecision and spatial variability as different error types, we estimate direction and magnitude of the effects of error over a range of error types.