Exploration of dynamic spatiotemporal exposure profiles via patient residential and healthcare utilization history

项目摘要 人们一生居住的地方在他们的健康和生活中起着重要的作用。 易患癌症等疾病。然而，纵向时空背景的地方， 人们的生活通常不会被纳入癌症研究。信息技术的最新进展和 “大数据”和相关的分析方法使癌症登记和研究人员能够 在人口水平上捕捉居住历史。我们建议开发一个大型的多维 使用多个数据源来重建癌症患者的纵向居住地和 暴露历史，并使用数据挖掘技术识别潜在的患者暴露概况 以癌症流行病学和环境健康文献中的科学证据为指导。我们将 通过使用以下方法来证明这种方法的可行性，并确定其优点和挑战 间皮瘤为例。 我们假设有不同的时空环境暴露轨迹， 间皮瘤患者的暴露概况，可以确定使用的居住历史。我们的具体 目标是：目标1：开发一种优化算法，以简化编译、清理、验证和 构建2011年至2015年在纽约诊断的间皮瘤患者的居住史， 如向纽约州癌症登记处（NYSCR）报告的，利用多个商业和政府 数据源;目标2：开发一种优化算法，以简化编译，清理，验证， 以及通过以下步骤构建与每个间皮瘤患者的居住史相关的暴露史 在个人居住地级别利用暴露代理以及与以下各项相关联的区域级别信息： 患者的居住地址，利用多个商业和政府数据源;以及目标3： 可视化患者的居住和暴露历史的时空动态，并确定 他们的暴露概况，使用先进的数据挖掘技术，如聚类分析，潜在类别分析， 网络分析。该方案在数据库建设方法和数据库管理方法上都具有创新性， 用于揭示重要曝光曲线的分析方法，例如临界曝光窗口， 环境集群/热点，以及暴露在空间和时间上的相对贡献。对我们 据了解，目前还没有类似的数据库。本项目编制的住宅数据将 永久存储在NYSCR中，以便将来使用，这是任何癌症登记处的第一个这样的例子。的 确定的暴露表型将有助于更好地理解环境暴露所起的作用 间皮瘤疾病的发展。所开发的方法可以被测试，扩大规模，复制其他 国家，并通过其他癌症和非癌症相关的条件。这种生命历程视角的方法 在推进癌症研究和常规癌症登记监测方面具有巨大潜力。

PROJECT SUMMARY The places people reside throughout their lives play an important role in their health and in their propensity to develop diseases such as cancer. However, the longitudinal spatiotemporal contexts of where people live are not commonly incorporated into cancer studies. Recent advances in information technology and “big data” and associated analytic approaches have made it possible for cancer registries and researchers to capture residential histories at the population level. We propose to develop a large multi-dimensional database for cancer patients using multiple data sources to reconstruct their longitudinal residential and exposure histories, and to identify potential patient exposure profiles using data mining techniques guided by scientific evidence from the cancer epidemiology and environmental health literature. We will demonstrate the feasibility and identify advantages and challenges of such an approach by using mesothelioma as an example. We hypothesize that there are distinct spatiotemporal environmental exposure trajectories and exposure profiles among mesothelioma patients that can be identified using residential histories. Our specific aims are: Aim 1: Develop an optimal algorithm to streamline the process of compiling, cleaning, verifying, and constructing the residential histories of mesothelioma patients diagnosed between 2011 and 2015 in New York, as reported to the New York State Cancer Registry (NYSCR), utilizing multiple commercial and governmental data sources; Aim 2: Develop an optimal algorithm to streamline the process of compiling, cleaning, verifying, and constructing the exposure history associated with each mesothelioma patient's residential history by leveraging exposure proxies at the individual residence level and area-level information associated with patient's residential addresses, utilizing multiple commercial and governmental data sources; and Aim 3: Visualize the spatiotemporal dynamics of patients' residential and exposure histories, and identify predictors of their exposure profiles, using advanced data mining techniques such as cluster analysis, latent class analysis, and network analysis. The proposal is innovative in both the methods for constructing the database and the analytical methods for uncovering important exposure profiles, such as critical exposure windows, environmental clusters/hotspots, and the relative contributions of exposures across space and time. To our knowledge, no similar database exists at present. The residential data compiled in this project will be permanently stored within the NYSCR to allow future use, the first such example by any cancer registry. The identified exposure phenotypes will contribute to better understanding of the role environmental exposure plays in mesothelioma disease development. The methods developed can be tested, scaled up, replicated by other states, and adopted to other cancers and non-cancer related conditions. This life-course perspective approach holds great potential for advancing cancer research as well as for routine cancer registry surveillance.