How Does Automated Record Linkage Affect Inferences about Population Health?

自动记录链接如何影响人口健康的推断？

• 批准号: 9372797
• 负责人: Martha Jane Bailey
• 金额: $ 23.25万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-15 至 2019-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9372797
• 关键词: Affect; Aging; Algorithms; American; Benchmarking; Big Data; Birth; Birth Certificates; Birth Records; Censuses; Child; Computers; Data; Data Linkages; Data Set; Databases; Double-Blind Method; Economics; Family; Foundations; Four-dimensional; Funding; Genealogy; Generations; Genetic Transcription; Goals; Graph; Hand; Health; Heterogeneity; Human; Immigrant; Incidence; Individual; Infant; Joints; Knowledge; Life; Link; Longevity; Machine Learning; Maiden Name; Marriage; Measurement; Measures; Medicare; Methodology; Methods; Minnesota; Minor; Names; Outcome; Performance; Pilot Projects; Politics; Population; Process; Public Health; Records; Research; Research Infrastructure; Running; Sample Size; Sampling; Science; Scientist; Source; Speed; Subgroup; Techniques; Technology; Time; United States; United States National Institutes of Health; Universities; Variant; Veterans; Woman; aging population; child bearing; cost effective; ethnic minority population; family structure; health data; health disparity; health knowledge; improved; innovation; intergenerational; longitudinal database; population health; racial and ethnic; repository; social; vector

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.

摘要 我们的主要研究目标是建立纵向代际家庭电子微数据集 （LIFE-M）横跨19世纪末和20世纪世纪美国。利用自动记录连接技术， LIFE-M项目结合了数百万的重要记录，以重建个人健康如何以及为什么会 随着时间的推移而改变这个多代纵向微型数据库旨在改变对 对生育和家庭结构的影响，以及对生命早期的长期健康影响 环境和暴露。 然而，在创建LIFE-M的过程中，我们遇到了严重的知识缺陷， 自动记录连接技术。拟议的项目旨在评估最多的 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）提供有关自动记录性能的系统性证据 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 （2）检验语音名称清理方法对质量的影响 指标;以及（3）检查记录质量指标如何针对不同的代表性不足的子组（包括 妇女、种族/少数民族和移民），并确定联系方法如何影响 代表性和推理性。为了实现这些目标，我们与创纪录的 链接专家使我们能够在记录链接中采用最先进的方法。我们还将依靠 LIFE-M项目的独立、双盲人工审查过程产生的新“地面实况”。 这个项目将大大有助于现有的知识使用自动链接方法 用于创建纵向和代际健康数据。这也将增加有关潜力的知识， 健康研究中的偏见来源。这两项贡献应大大提高说明性和 关于人口健康和老龄化的因果推论以及这些结果的差异。