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运用空间扫描统计量识别美国加利福尼亚州晚期结直肠癌的地理差异。

Use of the spatial scan statistic to identify geographic variations in late stage colorectal cancer in California (United States).

作者信息

Pollack Lori A, Gotway Carol A, Bates Janet H, Parikh-Patel Arti, Richards Thomas B, Seeff Laura C, Hodges Holly, Kassim Sidibe

机构信息

Centers for Disease Control and Prevention, 4770 Buford Hwy NE, Mailstop K55, Atlanta, GA 30341-3717, USA.

出版信息

Cancer Causes Control. 2006 May;17(4):449-57. doi: 10.1007/s10552-005-0505-1.

Abstract

OBJECTIVES

To identify geographic variations in colorectal cancer by stage at diagnosis in California using a descriptive analysis coupled with a spatial analysis and to discuss methodological considerations concerning the spatial statistical method.

METHODS

We analyzed 59,076 colorectal cancer cases diagnosed in California from 1996 to 2000 by logistic regression and by a spatial scan statistic to identify areas with a higher and lower relative risk of late-stage colorectal cancer.

RESULTS

In California, 57% of overall cases of colorectal cancer were diagnosed at a late stage. Californians diagnosed with late-stage colorectal cancer were more likely to be Hispanic and living in areas of lower socioeconomic status. The spatial scan identified two areas where the observed number of late-stage cancer was different than the number expected from the distribution in the rest of the state.

CONCLUSIONS

Spatial scan analyses can complement descriptive statistics, but results must be interpreted with consideration of factors that affect the ability to detect meaningful differences such as the number of events observed, accuracy in geocoding rural versus urban addresses, and the difficulty of adjusting for covariates.

摘要

目的

运用描述性分析结合空间分析,确定加利福尼亚州结直肠癌确诊时各阶段的地理差异,并讨论空间统计方法的方法学考量。

方法

我们通过逻辑回归和空间扫描统计分析了1996年至2000年在加利福尼亚州确诊的59076例结直肠癌病例,以确定晚期结直肠癌相对风险较高和较低的区域。

结果

在加利福尼亚州,57%的结直肠癌总体病例在晚期被确诊。被诊断为晚期结直肠癌的加利福尼亚人更有可能是西班牙裔,且居住在社会经济地位较低的地区。空间扫描识别出两个区域,其中晚期癌症的观察病例数与该州其他地区分布预期的病例数不同。

结论

空间扫描分析可以补充描述性统计,但在解释结果时必须考虑影响检测有意义差异能力的因素,如观察到的事件数量、农村与城市地址地理编码的准确性以及调整协变量的难度。

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