INNOVATIVE SOLUTIONS TO SPATIAL UNCERTAINTY IN GEOCODING

高效空间方法的发展和空间数据的广泛使用 参考数据改变了疾病暴露评估的格局 病因学。大多数基于空间的暴露疾病评估方法需要 知道研究参与者在空间和时间上的位置，并将其联系起来 空间参考数据的信息，用于估计暴露的可能性。 这种方法不仅允许对当前和过去的估计 曝光，但是传统收集方法的有效替代 纵向暴露数据，通过反向提高现有大队列的效用 当改进的曝光面可用时进行工程曝光。 然而，虽然曝光表面的空间分辨率有了很大的提高， 我们在太空中定位人的能力(通过地理编码)没有，而且仍然是一种速度- 准确的暴露评估中的限制因素。致力于改进的努力 在没有解决该问题的情况下，空间参考曝光数据被泄露 人的位置的错误分类(地理编码的不确定性)。 我们开发了一种地理编码方法，用于记录准确的空间范围 完整描述研究参与者所在区域的最终地理编码的 以及一种新的统计方法来将变异性纳入到 基于空间范围的曝光度和协变量数据。这些组合方法可以 可进行扩展以适当地合并地理编码的空间不确定性 错误地归入整体暴露评估模型。 我们的目标是测试将地理编码不确定性纳入风险评估 模型，然后将该方法应用于研究杀虫剂在儿童中的作用 白血病-一个具有高分辨率曝光表面、高分辨率的例子 地理编码精度的可变性，是一些最差的 假设地理编码的统一空间确定性的情况场景。我们将确保 广泛传播这一办法，将其作为全球解决分类错误问题的办法 研究参与者的地理位置。

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.