Development and Evaluation of Spatiotemporal Predictive Health Surveillance Tools

描述（由申请人提供）：监测空间卫生数据的统计方法对公共卫生从业人员至关重要。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完整数据集的回顾性分析而开发的。然而，公共卫生登记处的数据随着时间的推移而积累，对迄今收集到的所有数据进行顺序分析是早期发现新趋势或疾病风险差异的关键概念。及时治疗和控制措施所产生的影响可能是巨大的，特别是在监测癌症等慢性疾病发病率图时，癌症是全世界的主要死亡原因之一。本提案的目标是开发前瞻性时空疾病监测的统计方法，其中癌症监测是我们的主要重点。条件预测纵坐标是一种贝叶斯诊断工具，用于检测异常观测。虽然它从未在监测环境中应用，但我们假设它是一种强大的技术，以一种改良的形式，用于检测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多种疾病的分析，因为监测系统往往侧重于一种以上的疾病。这种扩展，结合疾病之间的相关性，可能会提高集群检测能力。我们提出三个具体目标。在具体目标1中，我们将为监视设置调整条件预测坐标。公开可得的小区域癌症计数数据和模拟可能的真实疾病相对风险变化模式的模拟数据将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，我们将把这种方法推广到一个多变量环境，允许纳入疾病之间的相关性。将同时监测不同类型的癌症，以评估与个体分析相比多变量扩展的性能。在具体目标3中，在R包（许多公共卫生部门都可以使用的免费统计编程语言）中实施监测条件预测坐标，将使公共卫生从业人员能够使用。该项目完成后，我们将拥有一种贝叶斯监测技术，用于尽快发现疾病发病率增加的地区，以努力降低发病率和死亡率。所提出的监测技术的多元扩展将填补当前文献的主要空白。这种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软件环境中以用户友好的软件包实施监测方法将促进传播。

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. PUBLIC HEALTH RELEVANCE: Narrative In this project we will develop a novel model-based surveillance technique to monitor a map of disease over time. This technique will enable early detection of changes in disease risk helping to reduce undue morbidity and mortality. The implementation of the proposed technique in a user-friendly package within the R software environment will facilitate dissemination and use by public health practitioners.