Gilboa Suzanne M, Mendola Pauline, Olshan Andrew F, Harness Catherine, Loomis Dana, Langlois Peter H, Savitz David A, Herring Amy H
Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA.
Environ Res. 2006 Jun;101(2):256-62. doi: 10.1016/j.envres.2006.01.004. Epub 2006 Feb 17.
Our population-based case-control study of air quality and birth defects in Texas relied on the geocoding of maternal residence from vital records for the assignment of air pollution exposures during early pregnancy. We attempted to geocode the maternal addresses for 5,338 birth defect cases and 4,574 frequency-matched controls using an automated procedure with standard matching criteria in ArcGIS 8.2 and 8.3. Initially, we matched 7,266 observations (73%). To increase the proportion of successful matches, we used an interactive procedure for the 2,646 addresses that were initially not geocoded by the software. This yielded an additional 985 matches (37%). Using the same 2,646 initially unmatched addresses, we compared the results of this interactive procedure to those of an automated procedure using lower standards. The automated procedure with lower standards yielded more matches (n=1,559, 59%) but with questionable accuracy. We included the interactively geocoded observations in our final data set. Their inclusion did not affect the estimates of air pollution exposure but increased our statistical power to detect associations between air quality and risk of selected birth defects. The geocoded and not geocoded populations differed in the distribution of Latino ethnicity (51% vs 59%) and ethnicity was independently associated with air pollution exposures (P<0.05). Geocoding status also appeared to modify the association between ethnicity and risk of birth defects; Latina women appeared to have a slightly lower risk of birth defects than non-Latina women in the geocoded population and to have a slightly higher risk in the not geocoded population. Incomplete geocoding may have resulted in a selection bias because of the under-representation of Latinas in our study population.
我们在德克萨斯州开展的一项基于人群的空气质量与出生缺陷病例对照研究，依靠从出生记录中对母亲居住地进行地理编码，来确定孕期早期的空气污染暴露情况。我们尝试使用ArcGIS 8.2和8.3中具有标准匹配标准的自动化程序，对5338例出生缺陷病例和4574例频率匹配对照的母亲地址进行地理编码。最初，我们成功匹配了7266条记录（73%）。为了提高成功匹配的比例，我们对软件最初未能进行地理编码的2646个地址采用了交互式程序。这又产生了985次匹配（37%）。我们使用相同的最初未匹配的2646个地址，将此交互式程序的结果与采用较低标准的自动化程序的结果进行了比较。采用较低标准的自动化程序产生了更多的匹配结果（n = 1559，59%），但其准确性存疑。我们将通过交互式地理编码的记录纳入了最终数据集。纳入这些记录并未影响空气污染暴露的估计，但增强了我们检测空气质量与特定出生缺陷风险之间关联的统计效力。地理编码和未地理编码人群在拉丁裔种族分布上存在差异（51%对59%），且种族与空气污染暴露独立相关（P<0.05）。地理编码状态似乎也改变了种族与出生缺陷风险之间的关联；在地理编码人群中，拉丁裔女性出生缺陷风险似乎略低于非拉丁裔女性，而在未地理编码人群中则略高。由于我们研究人群中拉丁裔女性代表性不足，地理编码不完整可能导致了选择偏倚。
