Geographic Tools for Surveillance and Study of Disease

疾病地图和聚集性检测方法是识别和总结疾病发生的地理模式的统计方法。随着地理信息系统(地理信息系统)以及疾病和环境数据的广泛获得和使用，分析工具必须提供清晰、准确、精确和可解释的结果。为此，拟议项目将解决地理分析方面的三个关键问题。首先，我们将研究各种方法，以适应人口较少、面临风险的地区的利率不稳定。如果处理不当，地图可能会显示虚假的峰(即簇)和谷，从而可能导致误解。传统的方法包括经验贝叶斯映射(即平滑)和相邻地理单元的分组。第二，我们将继续开展地理监控工具的开发工作。监测的一个主要目标是通过不断审查定期收集的数据，查明疾病预防和控制活动中疾病发生率或发病率的重要变化。然而，大多数方法只考虑时间上的变化。然而，对集群的看法往往是空间上的，环境污染物通常是根据空间或时空分布来描述的。该项目将扩大监测方法，使之适用于空间和时空数据，并将制定前瞻性评价方法，而不仅仅是回溯性评价。第三，我们将扩大我们在某些地理单元信息缺失时分析地理数据的方法方面的工作。通常，由于行政或司法管辖的限制，无法获得整个研究区域的数据。然而，可能需要能够估计整个区域或那些没有数据的地理单位的费率。我们将探索使用包括马尔科夫链蒙特卡罗模拟在内的方法来输入值和调整边缘效应的方法。

Disease mapping and cluster detection methods are statistical approaches to identify and summarize geographic patterns of disease occurrence. With the widespread availability and use of geographic information systems (GIS) and disease and environmental data, it is important that the analytic tools provide clear, accurate, precise and interpretable results. Toward that end, the proposed project will address three critical issues in geographical analysis. First, we will investigate a variety of methods for accommodating instability in rates from regions with small populations at risk. If not address adequately, maps may display spurious peaks (i.e., clusters) and valleys that can lead to misinterpretation. Traditional approaches include empirical Bayes mapping (i.e., smoothing) and grouping of neighboring geographical units. Second, we will continue our work on the development of geographic surveillance tools. One main goal of surveillance is to identify important changes in rates or patterns of disease occurrence for disease prevention and control activities by reviewing routinely collected data on an on-going basis. However, most approaches consider only temporal changes. Yet, perceptions of clusters are often spatial, and environmental pollutants typically are described in terms of the spatial or space-time distribution. This project will extend surveillance methods for use with spatial and space-time data, and will develop approaches for prospective rather than only retrospective evaluation. Third, we will extend our work on methods for analyzing geographic data when information is missing for some of the geographic units. Often, due to administrative or jurisdictional limitations, data are not available for an entire study region. However, there may be a need to be able to estimate rates for the entire region, or for those geographical units for which data are unavailable. We will explore methods for imputing values and adjusting for edge effects using methods including Markov Chain Monte Carlo simulations.