Development and Evaluation of Spatiotemporal Predictive Health Surveillance Tools

时空预测健康监测工具的开发和评估

• 批准号: 8189463
• 负责人: Andrew B. Lawson
• 金额: $ 7.38万
• 依托单位: MEDICAL UNIVERSITY OF SOUTH CAROLINA
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8189463
• 关键词: Accounting; Acute; Admission activity; Area; Asthma; Behavior; Case Study; Cause of Death; Childhood Leukemia; Chronic Disease; Computer software; Data; Data Set; Detection; Development; Diagnostic; Disease; Early Diagnosis; Environment; Environmental Health; Etiology; Evaluation; Goals; Health; Health Information System; Incidence; Individual; Intervention; Intestinal Diseases; Laboratory Research; Literature; Malignant Neoplasms; Maps; Measures; Methodology; Methods; Modeling; Monitor; Morbidity - disease rate; Outcome; Pattern; Performance; Population; Procedures; Programming Languages; Public Health; Registries; Relative Risks; Research; Risk; Simulate; South Carolina; Statistical Methods; System; Techniques; Testing; Time; Variant; base; cancer type; disorder risk; flexibility; improved; insight; mortality; novel; performance tests; prospective; respiratory; simulation; spatiotemporal; tool; trend; user-friendly

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.

描述（由申请人提供）：空间健康数据监测的统计方法对于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一个相对不发达的统计方法领域。大多数时空监测方法都是为了对完整数据集进行回顾性分析而开发的。然而，公共卫生登记处的数据会随着时间的推移而积累，对迄今为止收集的所有数据进行连续分析是及早发现疾病风险的新趋势或差异的关键概念。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的发病率地图时，癌症是全球主要死亡原因之一。该提案的目标是开发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重点。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。尽管它从未应用于监测环境，但我们假设它是一种经过修改的强大技术，用于检测空间和时间上疾病的异常聚集。我们还将扩展我们的方法来分析多种疾病，因为监测系统通常关注不止一种疾病。这种扩展结合了疾病之间的相关性，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，我们将针对监视设置调整条件预测坐标。公开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，我们将这种方法推广到多变量环境，允许包含疾病之间的相关性。将同时监测不同类型的癌症，以与个体分析相比评估多变量扩展的性能。在具体目标 3 中，在 R 包（许多公共卫生部门提供的免费统计编程语言）中实施监测条件预测纵坐标，将使公共卫生从业人员能够使用。该项目完成后，我们将拥有贝叶斯监测技术，该技术将用于尽快检测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变量扩展将填补当前文献的主要空白。这种扩展允许包含疾病之间的相关性，可能包含早期检测变化的重要线索。最后，在 R 软件环境中以用户友好的软件包实施监视方法将有助于传播。 公共卫生相关性：叙述 在这个项目中，我们将开发一种基于模型的新型监测技术，以监测随时间变化的疾病图谱。该技术将能够及早发现疾病风险的变化，有助于降低过度发病率和死亡率。在 R 软件环境中以用户友好的软件包实施所提出的技术将有助于公共卫生从业者的传播和使用。