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Finding Maternal Siblings in Birth Registration Data to form a Pregnancy Spine – Data Linkage Graph Based Methods for Unknown Cluster Sizes

机译:在出生登记数据中查找母体兄弟姐妹,以形成基于妊娠脊柱的脊柱 - 数据链接和图形的未知群集尺寸

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摘要

We have developed an innovative methodology to link maternal siblings within 2000-2005 England and Wales Birth Registration data, to form a Pregnancy Spine, a unification of all births to each unique mother. Key challenges were Blocking & Cluster resolution.To optimise geographic blocking, Internal Migration data was incorporated to map likely geographic movement of mothers between births.Following probabilistic linkage, sibling clusters were modelled as a graph and their structure optimised using community detection methods. Childhood statistics data relating to child DOB were incorporated to evaluate accuracy and remove false links.Our development has resulted in a new blocking and cluster resolution method. We developed new ways to assess sibling group accuracy, beyond traditional classifier metrics, and infer error rates.We applied our method to Registration Data used in earlier studies for QA of our methods.Using this, and other maternal sibling composition statistics, we present results showing that a high degree of accuracy was obtained for standard and new evaluation metrics.These methods will improve other linkage projects linking unknown clusters sizes/multiple datasets, or longer time period longitudinal linkage. To this Spine, researchers can append and link other data sources to answer questions about maternal and child health outcomes.
机译:我们开发了一种创新方法,将2000 - 2005年英格兰和威尔士出生登记数据中联系母亲兄弟姐妹,形成怀孕脊柱,对每个独特的母亲统一所有出生。关键挑战是阻止和集群解决方案。为了优化地理阻塞,内部迁移数据被纳入地图可能的母亲在出生之间的地理运动。在概率的连杆之后,兄弟筛簇被建模为使用社区检测方法优化的图表及其结构。童年统计数据与子DOB相关的数据被纳入评估准确性并删除错误链接。我们的开发导致了一种新的阻塞和集群解决方法。我们开发了评估兄弟组准确性的新方法,超越传统的分类器指标,并推断出错误率。我们将我们的方法应用于我们的方法的先前研究中使用的注册数据。使用此和其他母系兄弟组成统计,我们提出了结果表明标准和新的评估度量获得了高精度。这些方法将改善连接未知簇大小/多个数据集的其他联动项目,或者更长的时间段纵向连锁。对于这种脊椎,研究人员可以追加并链接其他数据来源,以回答有关母婴健康结果的问题。

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