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首页> 外文期刊>International Journal of Health Geographics >Geographical clustering of prostate cancer grade and stage at diagnosis, before and after adjustment for risk factors
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Geographical clustering of prostate cancer grade and stage at diagnosis, before and after adjustment for risk factors

机译：在调整危险因素之前和之后，前列腺癌在诊断时的等级和阶段的地理聚类

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Background Spatial variation in patterns of disease outcomes is often explored with techniques such as cluster detection analysis. In other types of investigations, geographically varying individual or community level characteristics are often used as independent predictors in statistical models which also attempt to explain variation in disease outcomes. However, there is a lack of research which combines geographically referenced exploratory analysis with multilevel models. We used a spatial scan statistic approach, in combination with predicted block group-level disease patterns from multilevel models, to examine geographic variation in prostate cancer grade and stage at diagnosis. Results We examined data from 20928 Maryland men with incident prostate cancer reported to the Maryland Cancer Registry during 1992–1997. Initial cluster detection analyses, prior to adjustment, indicated that there were four statistically significant clusters of high and low rates of each outcome (later stage at diagnosis and higher histologic grade of tumor) for prostate cancer cases in Maryland during 1992–1997. After adjustment for individual case attributes, including age, race, year of diagnosis, patterns of clusters changed for both outcomes. Additional adjustment for Census block group and county-level socioeconomic measures changed the cluster patterns further. Conclusions These findings provide evidence that, in locations where adjustment changed patterns of clusters, the adjustment factors may be contributing causes of the original clusters. In addition, clusters identified after adjusting for individual and area-level predictors indicate area of unexplained variation, and merit further small-area investigations.
机译：背景技术通常使用聚类检测分析等技术探索疾病结果模式中的空间变异。在其他类型的调查中，地理上变化的个人或社区级别特征经常被用作统计模型中的独立预测因素，这些统计模型还试图解释疾病结果的变化。但是，缺乏将地理参考的探索性分析与多层次模型相结合的研究。我们使用了空间扫描统计方法，并结合了多级模型中预测的分组组疾病模式，以检查前列腺癌分级和诊断阶段的地理差异。结果我们检查了1992年至1997年间向马里兰癌症登记处报告的20928名马里兰州患有前列腺癌的男性的数据。调整之前的初始聚类检测分析表明，在1992年至1997年期间，马里兰州的前列腺癌病例在每个结局（诊断的后期和肿瘤的组织学分级更高）上，有四个统计学上显着的高和低发生率聚类。在对个体病例属性（包括年龄，种族，诊断年份）进行调整后，两种结局的聚类模式均发生了变化。对普查区组和县级社会经济措施的其他调整进一步改变了集群模式。结论这些发现提供了证据，表明在调整改变集群模式的位置，调整因素可能是原始集群的原因。此外，在针对个体和区域水平的预测因素进行调整后确定的聚类表明了无法解释的变化区域，值得进一步进行小区域调查。

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