How Does Automated Record Linkage Affect Inferences about Population Health?

摘要 我们广泛的研究目标是创建纵向代际家庭电子微观数据集 (LIFE-M)横跨19世纪末和20世纪的美国。使用自动记录链接技术， LIFE-M项目结合了数百万条重要记录，以重建个人健康如何以及为什么 随着时间的推移而改变。这个多代人的、纵向的微观数据库旨在转变对 健康和长寿，对生育和家庭结构的影响，以及对早期生活的长期健康影响 环境和曝光量。 然而，在创建LIFE-M时，我们遇到了关于LIFE-M表现的严重知识不足 自动记录链接技术。拟议的项目旨在评估社会变革管理计划的业绩 流行和尖端的自动链接技术，用于创建纵向健康数据。 我们的具体目标是：(1)提供关于自动记录性能的系统证据 根据匹配率、链接样本的代表性、错误匹配(类型I)的链接算法 错误)和系统性测量误差；(2)检查拼音清理方法对质量的影响 指标；以及(3)检查不同代表性不足的子组的记录质量指标如何变化(包括 妇女、种族/族裔少数群体和移民)，并确定联系方法如何影响 代表性和推论。为了实现这些目标，我们与Record建立了新的合作伙伴关系 链接专家允许我们在记录链接中采用最尖端的方法。我们还将依靠 LIFE-M项目的独立、双盲人类审查过程产生了新的“地面真相”。 这个项目将对现有的关于使用自动链接方法的知识做出重大贡献 用于创建纵向和代际健康数据。它还将增加关于潜在的知识 健康研究中偏颇的来源。这两项贡献都应大大提高描述性和 关于人口健康和老龄化的因果推断以及这些结果中的差异。

ABSTRACT Our broad research objective is to create the Longitudinal Intergenerational Family Electronic Micro-dataset (LIFE-M) spanning the late 19th and 20th century United States. Using automated record linkage technology, the LIFE-M project combines millions of vital records to reconstruct how and why individuals' health has changed across time. This multi-generational, longitudinal micro-database aims to transform research on health and longevity, on childbearing and family structure, and on the long-run health effects of early-life circumstances and exposures. In creating LIFE-M, however, we have encountered serious deficits in knowledge about the performance of automated record linkage technology. The proposed project seeks to evaluate the performance of the most popular and cutting-edge automated linking techniques for the purposes of creating longitudinal health data. Our specific aims are to (1) produce systematic evidence regarding the performance of automated record linking algorithms in terms of match rates, representativeness of the linked sample, erroneous matches (type I errors), and systematic measurement error; (2) examine how phonetic name-cleaning methods affect quality metrics; and (3) examine how record quality metrics vary for different underrepresented subgroups (including women, racial/ethnic minorities, and immigrants) and to determine how linking methods affect representativeness and inferences. To achieve these aims, we have developed new partnerships with record linking experts allowing us to incorporate the most cutting-edge methods in record linking. We will also rely on new “ground truth” generated by LIFE-M project's independent, double-blind human review process. This project will contribute significantly to existing knowledge about the use of automated linking methods for creating longitudinal and intergenerational health data. It will also increase knowledge about potential sources of bias in health studies. Both contributions should greatly enhance the quality of descriptive and causal inferences about population health and aging and disparities in these outcomes.