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An Introduction to Probabilistic Record Linkage with a Focus on Linkage Processing for WTC Registries

Author

Listed:
  • Jana Asher

    (Department of Mathematics and Statistics, Slippery Rock University, Slippery Rock, PA 16057, USA)

  • Dean Resnick

    (National Opinion Research Center, Boston, MA 02114, USA)

  • Jennifer Brite

    (Division of Epidemiology, New York City Department of Health and Mental Hygiene, World Trade Center Health Registry, New York, NY 11101, USA)

  • Robert Brackbill

    (Division of Epidemiology, New York City Department of Health and Mental Hygiene, World Trade Center Health Registry, New York, NY 11101, USA)

  • James Cone

    (Division of Epidemiology, New York City Department of Health and Mental Hygiene, World Trade Center Health Registry, New York, NY 11101, USA)

Abstract

Since its post-World War II inception, the science of record linkage has grown exponentially and is used across industrial, governmental, and academic agencies. The academic fields that rely on record linkage are diverse, ranging from history to public health to demography. In this paper, we introduce the different types of data linkage and give a historical context to their development. We then introduce the three types of underlying models for probabilistic record linkage: Fellegi-Sunter-based methods, machine learning methods, and Bayesian methods. Practical considerations, such as data standardization and privacy concerns, are then discussed. Finally, recommendations are given for organizations developing or maintaining record linkage programs, with an emphasis on organizations measuring long-term complications of disasters, such as 9/11.

Suggested Citation

  • Jana Asher & Dean Resnick & Jennifer Brite & Robert Brackbill & James Cone, 2020. "An Introduction to Probabilistic Record Linkage with a Focus on Linkage Processing for WTC Registries," IJERPH, MDPI, vol. 17(18), pages 1-16, September.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:18:p:6937-:d:417575
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    References listed on IDEAS

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    1. Alan F Karr & Matthew T Taylor & Suzanne L West & Soko Setoguchi & Tzuyung D Kou & Tobias Gerhard & Daniel B Horton, 2019. "Comparing record linkage software programs and algorithms using real-world data," PLOS ONE, Public Library of Science, vol. 14(9), pages 1-16, September.
    2. Joseph T. Lariscy, 2017. "Black–White Disparities in Adult Mortality: Implications of Differential Record Linkage for Understanding the Mortality Crossover," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 36(1), pages 137-156, February.
    3. Rebecca C. Steorts & Rob Hall & Stephen E. Fienberg, 2016. "A Bayesian Approach to Graphical Record Linkage and Deduplication," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(516), pages 1660-1672, October.
    4. Lariscy, Joseph, 2011. "Differential Record Linkage by Hispanic Ethnicity and Age in Linked Mortality Studies: Implications for the Epidemiologic Paradox," SocArXiv tw9a4, Center for Open Science.
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    Cited by:

    1. James E. Cone & Albeliz Santiago-Colón & Roberto Lucchini, 2021. "9/11 Health Update," IJERPH, MDPI, vol. 18(12), pages 1-5, June.

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