Geographic Tools for Surveillance and Study of Disease

疾病制图和聚类检测方法是识别和总结疾病发生的地理模式的统计方法。 随着地理信息系统以及疾病和环境数据的广泛提供和使用，分析工具必须提供清晰、准确、精确和可解释的结果。为此，拟议项目将处理地理分析方面的三个关键问题。 首先，我们将研究各种方法，以适应风险人口较少地区的利率不稳定性。 如果没有充分解决，地图可能会显示虚假的峰值（即，集群）和山谷，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，我们将继续开发地理监测工具。 监测的一个主要目标是通过持续审查定期收集的数据，确定疾病发生率或模式的重要变化，以开展疾病预防和控制活动。 然而，大多数方法只考虑时间变化。 然而，对集群的看法往往是空间的，而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，以用于空间和时空数据，并将制定前瞻性而不仅仅是回顾性评价的方法。 第三，我们将扩展我们的工作方法，分析地理数据时，信息丢失的一些地理单位。通常，由于行政或司法限制，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的比率，或无法获得数据的地理单位的比率。 我们将探索估算值和调整边缘效应的方法，包括马尔可夫链蒙特卡罗模拟。

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.