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Mortality Modeling of Partially Observed Cohorts Using Administrative Death Records.

Joshua R Goldstein1, Maria Osborne1, Serge Atherwood1

  • 1Department of Demography, University of California, Berkeley.

Population Research and Policy Review
|May 23, 2025
PubMed
Summary
This summary is machine-generated.

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New methods improve mortality research using large administrative datasets. This approach addresses data limitations for better estimation of mortality differentials.

Area of Science:

  • Demography
  • Biostatistics
  • Data Science

Background:

  • Advances in data linkage offer access to extensive administrative mortality records.
  • Administrative mortality data present technical challenges, including limited coverage and survivor observation.

Purpose of the Study:

  • To present a method for fitting truncated distributions to estimate mortality differentials.
  • To address limitations in administrative mortality data for high-resolution research.

Main Methods:

  • Developed a method for fitting truncated distributions.
  • Applied the method to the CenSoc dataset, linking 1940 US Census records with Social Security mortality records.
  • Released the R package 'gompertztrunc' to implement the described methods.
Keywords:
GompertzMortality EstimationStatistical MethodsTruncation

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Main Results:

  • The method allows for robust estimation of mortality differentials despite data limitations.
  • Demonstrated the utility of the approach using a large-scale linked dataset.

Conclusions:

  • The described method effectively handles truncated distributions in administrative mortality data.
  • This approach is applicable to other contexts with available administrative death data.
  • The 'gompertztrunc' R package provides a practical tool for researchers.