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信息的广泛提供， 参考数据改变了疾病暴露评估的前景， 病因学大多数基于空间的疾病评估方法需要 了解研究参与者在空间和时间上的位置，并将其与 信息转换为空间参考数据，以估计潜在的暴露。 这种方法不仅可以估计当前和过去的情况， 暴露，但是传统收集方法的有效替代品 纵向暴露数据，通过反向分析增强现有大型队列的效用 当改进的曝光表面可用时，进行工程曝光。 然而，虽然曝光表面的空间分辨率已经大大提高， 我们在太空中定位人的能力（通过地理编码）没有，而且仍然是一个速率， 准确暴露评估的限制因素。致力于改善 在没有解决问题的情况下， 地理编码不确定性（Geocoding Uncertainty） 我们开发了一种地理编码方法，可记录精确的空间范围 完整描述研究参与者所在区域的最终地理编码 已知的位置，和一种新的统计方法，将变异性， 基于空间范围的暴露和协变量数据。这些组合方法可以 扩展到适当地纳入地理编码的空间不确定性 错误分类到总体暴露评估模型中。 我们的目标是测试将地理编码不确定性纳入暴露评估 模型，然后将该方法应用于农药在儿童期作用的研究中 白血病-具有高分辨率曝光表面、高分辨率 地理编码精度的可变性，是一些最糟糕的 假设地理编码具有统一的空间确定性。我们将确保 广泛传播这一办法，作为解决分类错误问题的全球办法， 研究参与者的地理位置。