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Imputing Partial Birth Dates Using Day of the Week.

Candice Y Johnson1

  • 1Department of Epidemiology and Biostatistics, Michigan State University, USA.

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|April 9, 2026
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Summary
This summary is machine-generated.

Incorporating the day of the week into birth date imputation significantly reduces misclassification errors. This method improves accuracy in de-identified datasets, making research more reliable.

Keywords:
birth dateday of the weekdeidentificationimputationmissing data

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Area of Science:

  • Biostatistics
  • Data Science
  • Epidemiology

Background:

  • De-identified datasets often suppress exact birth dates for participant confidentiality.
  • When only month and year are available, researchers must impute the day of the month for precise date requirements.
  • The day of the week, when available, is rarely utilized in partial birth date imputation.

Purpose of the Study:

  • To assess the reduction in misclassification by including the day of the week in partial birth date imputation.
  • To evaluate the impact of day-of-the-week information on the accuracy of imputed birth dates.

Main Methods:

  • Simulated a population of 594,677 individuals based on 2024 England and Wales birthday distributions.
  • Imputed birth dates using four methods: fixed day (1st, 15th), random day, and random day conditional on the day of the week.
  • Quantified misclassification by median days difference and cumulative percentage within a specified timeframe.

Main Results:

  • Conditional day-of-the-week imputation reduced misclassification to a median of 7 days, compared to 8-15 days for other methods.
  • Nearly 25% of the population had their true birth date imputed using the conditional method, versus 3% with other methods.
  • Imputing the 15th of the month minimized misclassification to under 3 weeks for all individuals.

Conclusions:

  • Incorporating the day of the week into random birth date imputation effectively reduces misclassification.
  • This enhanced imputation technique is readily implementable using standard statistical software.
  • The findings support improved data accuracy in research utilizing de-identified health information.