INNOVATIVE SOLUTIONS TO SPATIAL UNCERTAINTY IN GEOCODING

地理编码中空间不确定性的创新解决方案

• 批准号: 9489646
• 负责人: MYLES G COCKBURN
• 金额: $ 34.29万
• 依托单位: UNIVERSITY OF COLORADO DENVER
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9489646
• 关键词: INNOVATIVE; SOLUTIONS; SPATIAL; UNCERTAINTY; GEOCODING

The development of efficient spatial methods and the widespread availability of spatially referenced data have changed the landscape of exposure assessment in disease etiology. Most spatial-based exposure-disease assessment approaches require knowing where study participants are located in space and time, and linking that information to spatially-referenced data that estimate potential for exposure. Such approaches not only allow for estimation of both current and past exposures, but are efficient alternatives to traditional methods of collecting exposure data longitudinally, enhancing the utility of existing large cohorts by reverse engineering exposures when improved exposure surfaces become available. However, while the spatial resolution of exposure surfaces has greatly improved, our ability to locate people in space (with geocoding) has not, and remains a rate- limiting factor in accurate exposure assessment. The effort engaged in improving spatially referenced exposure data is compromised without addressing the problem of misclassification of the location of people (geocoding uncertainty). We have developed a geocoding approach that records the exact spatial extent of the final geocode that fully describes the area in which the study participant is known to be located, and a novel statistical approach to incorporate variability in exposure and covariate data based on spatial extent. These combined approaches can be extended to appropriately incorporate spatial uncertainty from geocoding misclassification into the overall exposure assessment model. We aim to test the inclusion of geocode uncertainty into an exposure assessment model, and then apply that approach in a study of the role of pesticides in childhood leukemia – an example which has a high resolution exposure surface, high variability in geocode accuracy, and is an excellent example of some of the worst case scenarios in assuming uniform spatial certainty of geocodes. We will ensure wide dissemination of the approach as a global solution to the problem of misclassified study participant geolocation.

有效的空间方法的发展和空间的宽度可用性 引用的数据改变了疾病暴露评估的情况 病因。大多数基于空间的曝光 - 疾病评估方法都需要 知道研究参与者位于时空中的位置，并将其联系起来 信息到空间引用的数据，以估计暴露的潜力。 这种方法不仅允许估计当前和过去 暴露，但是传统收集方法的有效替代品 纵向曝光数据，通过反向增强现有大型队列的实用性 当可用的曝光表面改善时，工程曝光可用。 但是，尽管暴露表面的空间分辨率大大改善，但 我们在太空中定位人员（使用地理编码）的能力尚未，并且仍然是一个速率 - 准确暴露评估的限制因素。致力于改善的努力 空间引用的暴露数据被损害而不解决问题 对人的位置的错误分类（地理编码不确定性）。 我们已经开发了一种地理编码方法，该方法记录了确切的空间范围 在最终的地理码中，完全描述了研究参与者所在的区域 已知所在的位置，以及一种新颖的统计方法，以纳入可变性 基于空间范围的暴露和协变数据。这些合并的方法可以 扩展到适当地纳入地理编码的空间不确定性 错误分类为整体暴露评估模型。 我们旨在测试将地理码不确定性纳入暴露评估 模型，然后将这种方法应用于农药在童年中的作用 白血病 - 一个具有高分辨率暴露表面的例子，高 地理位置准确性的可变性，这是一些最坏的一个很好的例子 假设地理编码的均匀空间确定性的情况。我们将确保 将方法广泛传播，作为解决错误分类问题的全球解决方案 研究参与者地理位置。