Thomas AvisJ, Carlin Bradley P
Division of Biostatistics, School of Public Health, University of Minnesota, Mayo Mail Code 303, Minneapolis, Minnesota 55455-0392, USA.
Stat Med. 2003 Jan 15;22(1):113-27. doi: 10.1002/sim.1215.
Cancers detected at a later disease stage are associated with significantly higher mortality risk. Lack of uniformity in county-level cancer detection rates is thus of substantial interest to state health departments and other public health professionals. In this paper, we perform several spatial analyses of breast and colorectal cancer detection data for Minnesota counties for 1995-1997. We look for outliers and clusters in the late detection rates using a number of techniques: (i). applying various mapping schemes, (ii). smoothing the data using Bayesian methods implemented via Markov chain Monte Carlo, and (iii). applying maximum likelihood techniques to test for the presence of clusters and to identify the most likely clustersOur results suggest a fairly uniform spatial pattern in both sets of detection rates. Spatially smoothed rates did not reveal clusters of counties with significantly higher late detection risk, nor were county-level covariates (such as income, education, and race) particularly helpful in explaining the rates. However, our spatial clustering approach (using the scan statistic) did produce statistically significant clusters of counties which may indicate differences of practical importance for public health.
在疾病晚期检测出的癌症与显著更高的死亡风险相关。因此，县级癌症检测率缺乏一致性引起了州卫生部门和其他公共卫生专业人员的极大关注。在本文中，我们对明尼苏达各县1995 - 1997年的乳腺癌和结直肠癌检测数据进行了多项空间分析。我们使用多种技术寻找晚期检测率中的异常值和聚类：(i). 应用各种制图方案，(ii). 使用通过马尔可夫链蒙特卡罗实现的贝叶斯方法对数据进行平滑处理，以及(iii). 应用最大似然技术来检验聚类的存在并识别最可能的聚类。我们的结果表明，两组检测率都呈现出相当均匀的空间模式。空间平滑率并未揭示出晚期检测风险显著更高的县的聚类，县级协变量（如收入、教育程度和种族）在解释这些比率方面也没有特别大的帮助。然而，我们的空间聚类方法（使用扫描统计量）确实产生了具有统计学意义的县聚类，这可能表明对公共卫生具有实际重要性的差异。
