Visualizing statistical significance of disease clusters using cartograms.
Visualizing statistical significance of disease clusters using cartograms.
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DOI：
10.1186/s12942-017-0093-9
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发表时间：
2017-05-15
影响因子：
4.9
通讯作者：
Wong DWS
中科院分区：
文献类型：
--
作者：
Kronenfeld BJ;Wong DWS
卫生官员和流行病学研究人员经常使用发病率地图来识别潜在的疾病聚集性。由于这些地图夸大了低密度地区的突出程度，隐藏了城市(高密度)地区的潜在聚集性，许多研究人员使用密度均衡地图(地图)作为流行病学地图的基础。然而，我们目前没有关于统计不确定性的目视评估的指导方针。为了解决这一缺点，我们开发了视觉确定地图上跨越一个或多个地区的集群的统计意义的技术。我们在地理视觉分析框架内开发了这些技术，该框架不依赖于自动化重要性测试，因此可以促进视觉分析，以检测自动化技术可能遗漏的集群。在高危人群的地图上，疾病聚集性的统计意义是根据标准假设检验情景下的聚集率、面积和形状来确定的。在某些测试假设下，我们推导出公式来确定对于给定的比率，先验和后验指定区域的统计显著性所需的面积。独一无二的是，我们的方法能够动态推断由合并单个区域形成的聚合区域。该方法在交互式工具中实现，所述交互式工具提供全景图、自动图例构建和动态搜索工具，以促进集群检测和评估测试假设的有效性。加利福尼亚州白血病发病率分析的一个案例研究表明，能够在视觉上区分统计上显著和不显著的区域。拟议的地理视觉分析方法能够直观地评估地图上任意定义的区域的统计意义。我们的研究促使更广泛地讨论地理视觉探索性分析在疾病地图中的作用，以及视觉评估空间集群的统计意义的适当框架。本文的在线版本(doi：10.1186/s12942-0170093-9)包含补充材料，授权用户可以使用。
Health officials and epidemiological researchers often use maps of disease rates to identify potential disease clusters. Because these maps exaggerate the prominence of low-density districts and hide potential clusters in urban (high-density) areas, many researchers have used density-equalizing maps (cartograms) as a basis for epidemiological mapping. However, we do not have existing guidelines for visual assessment of statistical uncertainty. To address this shortcoming, we develop techniques for visual determination of statistical significance of clusters spanning one or more districts on a cartogram. We developed the techniques within a geovisual analytics framework that does not rely on automated significance testing, and can therefore facilitate visual analysis to detect clusters that automated techniques might miss. On a cartogram of the at-risk population, the statistical significance of a disease cluster is determinate from the rate, area and shape of the cluster under standard hypothesis testing scenarios. We develop formulae to determine, for a given rate, the area required for statistical significance of a priori and a posteriori designated regions under certain test assumptions. Uniquely, our approach enables dynamic inference of aggregate regions formed by combining individual districts. The method is implemented in interactive tools that provide choropleth mapping, automated legend construction and dynamic search tools to facilitate cluster detection and assessment of the validity of tested assumptions. A case study of leukemia incidence analysis in California demonstrates the ability to visually distinguish between statistically significant and insignificant regions. The proposed geovisual analytics approach enables intuitive visual assessment of statistical significance of arbitrarily defined regions on a cartogram. Our research prompts a broader discussion of the role of geovisual exploratory analyses in disease mapping and the appropriate framework for visually assessing the statistical significance of spatial clusters. The online version of this article (doi:10.1186/s12942-017-0093-9) contains supplementary material, which is available to authorized users.