Gregorio David I, Samociuk Holly
Department of Community Medicine and Health Care, University of Connecticut School of Medicine, 263 Farmington Ave., Farmington, CT 06030-6325, USA.
Spat Spatiotemporal Epidemiol. 2013 Sep;6:1-6. doi: 10.1016/j.sste.2013.04.002. Epub 2013 Apr 25.
Bias in spatial analyses that overlook compositional and contextual factors of communities can be substantial. We first examined spatial patterns among 11,728 prostate cancer cases across Connecticut, 1994-98. A spatial scan statistic (SatScan™) identified two locations where average annual incidence rates significantly exceeded the statewide level and two locations with significantly lower disease rates. Extending the analysis to adjust rates for age and race/ethnicity greatly minimized, but did not eliminate, geographic variation. Adjustment for age and poverty level of communities eliminated significant variability across locales. Similarly, analysis adjusted for age and covariation of colorectal cancer incidence rates across the state accounted for all significant variation previously observed. These results suggest that accounting for a "detection effect" due to clinical patterns of another screenable condition may be as useful as adjusting spatial data for variability of socio-economic conditions.
忽视社区构成和背景因素的空间分析中的偏差可能很大。我们首先研究了1994 - 1998年康涅狄格州11728例前列腺癌病例的空间模式。空间扫描统计（SatScan™）确定了两个平均年发病率显著超过全州水平的地点和两个疾病率显著较低的地点。将分析扩展到对年龄和种族/民族进行率调整，极大地减少了但并未消除地理变异。对社区年龄和贫困水平进行调整消除了各地之间的显著差异。同样，对年龄和全州结直肠癌发病率的协变量进行调整的分析解释了先前观察到的所有显著变异。这些结果表明，考虑另一种可筛查疾病的临床模式所产生的“检测效应”可能与针对社会经济状况的变异性调整空间数据一样有用。
