Development and Evaluation of Spatiotemporal Predictive Health Surveillance Tools

描述(由申请人提供)：空间健康数据监测的统计方法对公共卫生从业者至关重要。然而，对疾病风险在空间和时间上的变化进行前瞻性监测是统计方法中一个相对不发达的领域。大多数时空监测方法都是为了对完整的数据集进行回顾分析而开发的。然而，公共卫生登记册中的数据是随着时间积累的，对迄今收集的所有数据进行顺序分析是及早发现新出现的趋势或疾病风险差异的关键概念。及时治疗和控制措施产生的影响可能是巨大的，特别是在监测癌症等慢性疾病的发病率地图时，癌症是全球主要死亡原因之一。这项建议的目标是为前瞻性的时空疾病监测开发统计方法，癌症监测是我们的主要重点。条件预测纵坐标是一种检测异常观测的贝叶斯诊断工具。尽管它从未被应用在监测环境中，但我们假设它是一种强大的技术，经过修改后，可以检测到在空间和时间上不寻常的疾病聚集。我们还将把我们的方法扩展到对多种疾病的分析，因为监测系统往往集中在一种以上的疾病上。这一扩展纳入了疾病之间的相关性，可能会提高集群检测能力。我们提出了三个具体目标。在具体目标1中，我们将使条件预测纵坐标适应监视设置。公众可获得的小区域癌症计数数据和模拟可能真实的疾病相对风险变化模式的模拟数据将被用于测试所提出的方法在不同情况下的性能。在具体目标2中，我们将把这一方法推广到允许纳入疾病之间相关性的多变量环境。将同时监测不同类型的癌症，以评估与个别分析相比的多变量扩展的性能。在具体目标3中，在R包中实施监测条件预测纵坐标，这是许多公共卫生部门提供的一种免费统计编程语言，将使公共卫生从业人员能够使用。在这个项目完成后，我们将拥有一项贝叶斯监测技术，用于尽快发现疾病发病率上升的地区，以努力降低发病率和死亡率。拟议的监测技术的多变量扩展将填补目前文献中的一个主要空白。这一扩展允许纳入疾病之间的相关性，可能包含早期发现变化的重要线索。最后，在R软件环境内以便于用户使用的套餐形式实施监测方法将有助于传播。

DESCRIPTION (provided by applicant): Statistical methods for surveillance of spatial health data are of critical importance to public health practitioners. Yet, prospective surveillance for changes in disease risk over in space and time is a relatively undeveloped arena of statistical methodology. Most methods for space-time surveillance have been developed for retrospective analyses of complete data sets. However, data in public health registries accumulate over time and sequential analyses of all the data collected so far is a key concept to early detection of emerging trends or differences in disease risk. The impact derived from timely treatment and control measures can be dramatic, especially when monitoring maps of disease incidence of chronic diseases such as cancer, one of the leading causes of death worldwide. The goal of this proposal is to develop statistical methodology for prospective spatio-temporal disease surveillance, with cancer surveillance being our primary focus. The conditional predictive ordinate is a Bayesian diagnostic tool that detects unusual observations. Although it has never been applied in a surveillance context, we hypothesize it is a powerful technique, in a modified form, for detection of unusual aggregations of disease in space and time. We will also extend our approach to the analysis of multiple diseases, as surveillance systems are often focused on more than one disease. This extension, incorporating correlation between diseases, is likely to improve cluster detection capability. We propose three specific aims. In Specific Aim 1 we will adapt the conditional predictive ordinate for a surveillance setting. Publicly available small area cancer count data and simulated data mimicking possible true disease relative risk changing patterns will be used to test the performance of the proposed methodology in different scenarios. In Specific Aim 2 we will generalize this approach to a multivariate setting which allows for inclusion of correlation between diseases. Different types of cancer will be monitored simultaneously to assess the performance of the multivariate extension in comparison to the individual analyses. In Specific Aim 3, the implementation of the surveillance conditional predictive ordinate in an R package, a free statistical programming language available in many public health departments, will enable use by public health practitioners. Upon the completion of this project, we will have a Bayesian surveillance technique that will be used to detect areas of increased disease incidence as quickly as possible in an effort to reduce morbidity and mortality. The multivariate extension of the proposed surveillance technique will fill in a major gap on the current literature. This extension, allowing for inclusion of correlation between diseases, may contain important clues for the early detection of changes. Finally, the implementation of the surveillance methodology in a user-friendly package within the R software environment will facilitate dissemination.