Surveillance of Spatial Case Event Data in Cancer Studies

癌症研究中空间案例事件数据的监测

• 批准号: 8705128
• 负责人: Andrew B. Lawson
• 金额: $ 7.31万
• 依托单位: MEDICAL UNIVERSITY OF SOUTH CAROLINA
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-05-01 至 2016-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8705128
• 关键词: Address; Area; Atlas of Cancer Mortality in the United States; Bayesian Modeling; Cause of Death; Computer software; Coupled; Data; Data Set; Detection; Development; Diagnosis; Diagnostic; Disease; Disease Outbreaks; Early Diagnosis; Environment; Evaluation; Event; Goals; Health; Incidence; Individual; Lead; Link; Literature; Localized Disease; Logistics; Malignant Neoplasms; Measures; Medical Surveillance; Methodology; Methods; Modeling; Monitor; Monte Carlo Method; Morbidity - disease rate; Non-Hodgkin' s Lymphoma; Process; Programming Languages; Public Health; Records; Registries; Risk; Running; Sampling; Severities; South Carolina; Techniques; Testing; Time; base; case control; demographics; disease registry; disorder risk; mortality; neoplasm registry; novel; programs; prospective; public health relevance; software development; spatiotemporal; tool; user-friendly

DESCRIPTION (provided by applicant): The development of appropriate methods of analysis for surveillance of spatial health data is of critical importance to public health practitioners, yt surveillance for emerging outbreaks of disease in space and time is a relatively undeveloped arena of statistical methodology. Most of the vast literature in space-time surveillance has been developed for retrospective analyses of complete data sets. However, data in public health accumulate over time and sequential estimation of the model using all the data collected so far is a key concept to early detection of localized disease outbreaks. The impact derived from timely treatment and control measures can be dramatic, especially when monitoring maps of cancer disease-incidence, one of the leading causes of death worldwide. The goal of this application is to develop a statistical methodology for prospective spatio- temporal disease surveillance, with cancer surveillance being our primary focus. This technique will be used to detect areas of increased disease incidence as quickly as possible to reduce morbidity and mortality. The conditional predictive ordinate is a Bayesian diagnostic tool that detects unusual observations and it is proposed to extend its use to case event incidence. We will test these hypotheses in three specific aims. In specific Aim 1 we will adapt the conditional predictive ordinate in a case event surveillance setting. In specific Aim 2 the focus is the evaluation of computation approaches, and the implementation of the surveillance tools in an R package, a free statistical programming language available in many public health departments, and this will enable its use by public health practitioners.

描述（由申请人提供）：开发用于监测空间健康数据的适当分析方法对于公共卫生从业者至关重要，但对空间和时间上新出现的疾病爆发的监测是一个相对不发达的统计方法领域。大多数时空监测方面的大量文献都是为了对完整数据集进行回顾性分析而开发的。然而，公共卫生数据随着时间的推移而积累，使用迄今为止收集的所有数据对模型进行连续估计是早期发现局部疾病爆发的关键概念。及时治疗和控制措施所产生的影响可能是巨大的，特别是在监测癌症发病率地图时，癌症发病率是全球主要死亡原因之一。 该应用程序的目标是开发一种用于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关注点。该技术将用于尽快检测疾病发病率增加的区域，以降低发病率和死亡率。条件预测纵坐标是一种贝叶斯诊断工具，可检测异常观察结果，并建议将其用途扩展到案例事件发生率。 我们将在三个具体目标中测试这些假设。在具体目标 1 中，我们将在病例事件监测环境中调整条件预测坐标。具体目标 2 的重点是计算方法的评估以及 R 包中监测工具的实施，R 包是许多公共卫生部门提供的免费统计编程语言，这将使公共卫生从业者能够使用它。