Boston University School of Public Health, Boston, MA, USA.
Environ Health. 2013 Sep 8;12(1):75. doi: 10.1186/1476-069X-12-75.
The growing interest in research on the health effects of near-highway air pollutants requires an assessment of potential sources of error in exposure assignment techniques that rely on residential proximity to roadways.
We compared the amount of positional error in the geocoding process for three different data sources (parcels, TIGER and StreetMap USA) to a "gold standard" residential geocoding process that used ortho-photos, large multi-building parcel layouts or large multi-unit building floor plans. The potential effect of positional error for each geocoding method was assessed as part of a proximity to highway epidemiological study in the Boston area, using all participants with complete address information (N = 703). Hourly time-activity data for the most recent workday/weekday and non-workday/weekend were collected to examine time spent in five different micro-environments (inside of home, outside of home, school/work, travel on highway, and other). Analysis included examination of whether time-activity patterns were differentially distributed either by proximity to highway or across demographic groups.
Median positional error was significantly higher in street network geocoding (StreetMap USA = 23 m; TIGER = 22 m) than parcel geocoding (8 m). When restricted to multi-building parcels and large multi-unit building parcels, all three geocoding methods had substantial positional error (parcels = 24 m; StreetMap USA = 28 m; TIGER = 37 m). Street network geocoding also differentially introduced greater amounts of positional error in the proximity to highway study in the 0-50 m proximity category. Time spent inside home on workdays/weekdays differed significantly by demographic variables (age, employment status, educational attainment, income and race). Time-activity patterns were also significantly different when stratified by proximity to highway, with those participants residing in the 0-50 m proximity category reporting significantly more time in the school/work micro-environment on workdays/weekdays than all other distance groups.
These findings indicate the potential for both differential and non-differential exposure misclassification due to geocoding error and time-activity patterns in studies of highway proximity. We also propose a multi-stage manual correction process to minimize positional error. Additional research is needed in other populations and geographic settings.
由于人们对研究近高速公路空气污染物对健康的影响的兴趣日益浓厚，因此需要评估依赖于住宅与道路接近程度的暴露评估技术中潜在的误差来源。
我们比较了三种不同数据源（宗地、TIGER 和 StreetMap USA）的地理编码过程中的位置误差量，以及使用正射照片、大型多建筑物宗地布局或大型多单元建筑物平面图的“黄金标准”住宅地理编码过程。在波士顿地区进行的一项高速公路附近流行病学研究中，评估了每种地理编码方法的潜在位置误差影响，研究对象是所有具有完整地址信息的参与者（N=703）。收集了最近工作日/周末和非工作日/周末的每小时活动数据，以检查在五个不同微环境（家中、家外、学校/工作、高速公路旅行和其他）中花费的时间。分析包括检查活动模式是否因靠近高速公路或在不同人群中分布不均。
街道网络地理编码（StreetMap USA=23 米；TIGER=22 米）的中位数位置误差明显高于宗地地理编码（8 米）。当限制为多建筑物宗地和大型多单元建筑物宗地时，所有三种地理编码方法都有很大的位置误差（宗地=24 米；StreetMap USA=28 米；TIGER=37 米）。街道网络地理编码在 0-50 米接近类别中也对高速公路附近研究中的位置误差引入了更大的误差。工作日/周末在家中度过的时间因人口统计学变量（年龄、就业状况、教育程度、收入和种族）而异。当按靠近高速公路分层时，活动模式也有明显差异，居住在 0-50 米接近类别中的参与者在工作日/周末的学校/工作微环境中报告的时间明显多于所有其他距离组。
这些发现表明，由于地理编码错误和高速公路接近程度研究中的活动模式，存在差异和非差异暴露分类错误的可能性。我们还提出了一个多阶段的手动校正过程，以最大限度地减少位置误差。需要在其他人群和地理环境中进行更多的研究。
