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.

抽象的 我们广泛的研究目标是创建纵向际际族家庭电子微数据 （Life-M）跨越19世纪后期的美国。使用自动记录链接技术， Life-M项目结合了数百万的重要记录，以重建个人健康状况如何以及为什么 随时间变化。这种多代的纵向微数据库旨在改变对 健康和长寿，关于生育和家庭结构以及早期健康的长期健康影响 情况和暴露。 但是，在创建Life-M时，我们在了解表现方面遇到了严重的防御 自动记录链接技术。拟议的项目旨在评估最多的绩效 流行和尖端的自动链接技术是为了创建纵向健康数据。 我们的具体目的是（1）产生有关自动记录性能的系统证据 链接算法在匹配率，链接样本的代表性方面，错误匹配（类型I） 错误）和系统的测量错误； （2）检查语音名称清洁方法如何影响质量 指标； （3）检查不同代表性不足的亚组的记录质量指标如何变化（包括 妇女，种族/族裔少数民族和移民），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，我们建立了具有记录的新合作伙伴关系 链接专家，使我们能够将最尖端的方法纳入记录链接中。我们也将依靠 Life-M Project独立的双盲人类审查过程产生的新“地面真相”。 该项目将对有关使用自动链接方法的现有知识产生重大贡献 用于创建纵向和代际健康数据。它还将增加有关潜在的知识 健康研究中的偏见来源。两种贡献都应大大提高描述性的质量和 关于这些结果的人口健康，衰老和差异的因果推断。