Visualizing and testing the impact of place on late-stage breast cancer incidence: a non-parametric geostatistical approach.

Visualizing and testing the impact of place on late-stage breast cancer incidence: a non-parametric geostatistical approach.
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DOI:
10.1016/j.healthplace.2009.10.017
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发表时间:
2010-03
期刊:
影响因子:
4.8
通讯作者:
Goovaerts P
Goovaerts P
中科院分区:
医学2区
文献类型:
--
作者:
Goovaerts P

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本文描述了三向列联表和地质统计学的结合,以可视化两个假定协变量对个人健康结果的非线性影响,并测试这种影响的显着性,解释空间相关性模式并校正多重测试。该方法用于探讨密歇根州三个县到乳房X光检查诊所的距离和人口普查区贫困水平对晚期乳腺癌诊断率的影响。发病率显着低于全地区平均水平 (18.04%),主要发生在富裕社区 [0-5% 贫困],而较高的发病率主要受距诊所距离的影响。新的基于模拟的多重测试校正非常灵活,并且比导致大多数测试变得不显着的传统错误发现率方法保守。后期诊断频率明显较高的类别通常会转化为空间扫描统计无法检测到的地理集群。
This paper describes the combination of three-way contingency tables and geostatistics to visualize the non-linear impact of two putative covariates on individual-level health outcomes and test the significance of this impact, accounting for the pattern of spatial correlation and correcting for multiple testing. The methodology is used to explore the influence of distance to mammography clinics and census-tract poverty level on the rate of late-stage breast cancer diagnosis in three Michigan counties. Incidence rates are significantly lower than the area-wide mean (18.04%) mainly in affluent neighbourhoods [0-5% poverty], while higher incidences are mainly controlled by distance to clinics. The new simulation-based multiple testing correction is very flexible and less conservative than the traditional false discovery rate approach that results in a majority of tests becoming non-significant. Classes with significantly higher frequency of late-stage diagnosis often translate into geographic clusters that are not detected by the spatial scan statistic.
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