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）：开发适当的分析方法来监视空间健康数据对公共卫生从业人员至关重要，在时空和时间中疾病爆发的YT监测是一个相对不发达的统计方法。在时空监视中，大多数庞大的文献都是为了回顾完整数据集的回顾性分析。但是，迄今为止，使用所有收集的数据随着时间的推移而积累的数据随着时间的推移和顺序估计，这是早期发现局部疾病暴发的关键概念。及时的治疗和控制措施产生的影响可能是戏剧性的，尤其是当监视癌症症状的地图时，这是全球死亡的主要原因之一。 该应用的目的是开发一种统计方法，用于前瞻性时空疾病监测，癌症监测是我们的主要重点。该技术将用于检测尽可能快地降低发病率和死亡率的疾病发病率增加的区域。条件预测纵坐标是一种贝叶斯诊断工具，可检测异常的观察结果，并提议将其用于病例事件发病率的使用。 我们将以三个特定目标来检验这些假设。在特定目标1中，我们将在案例事件监视设置中调整条件预测纵坐标。在特定目标2中，重点是评估计算方法，以及在R软件包中实施监视工具，这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使用它。