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）：开发适当的空间健康数据监测分析方法对公共卫生从业人员至关重要，但对空间和时间上新出现的疾病爆发的监测是统计方法学相对不发达的竞技场。大多数时空监测的大量文献都是为完整数据集的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。及时的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，癌症是世界范围内的主要死亡原因之一。 本申请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。这一技术将用于尽快查明发病率增加的地区，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，并建议将其用于病例事件发生率。 我们将在三个具体目标中检验这些假设。在具体目标1中，我们将在病例事件监测设置中调整条件预测纵坐标。在具体目标2中，重点是评估计算方法，并在R包中实施监测工具，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人员能够使用。