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An algorithm to identify residential mobility from electronic health-record data.

Jessica R Meeker1, Heather Burris2,3,4, Mary Regina Boland1,4,5,6

  • 1Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.

International Journal of Epidemiology
|January 9, 2022
PubMed
Summary

A new algorithm, REMAP, accurately identifies patient moves during pregnancy, improving exposure assessment. This prevents significant misclassification of environmental exposures, providing more reliable data for researchers.

Keywords:
Residential mobilitycohort studieselectronic health recordenvironmentepidemiologyexposure measurement errorgeo-spatial exposuresneighbourhood deprivationpregnancyreproductive health

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Area of Science:

  • Environmental Health
  • Health Informatics
  • Epidemiology

Background:

  • Electronic health records contain address data for inferring environmental, social, and economic exposures.
  • Administrative errors and misspellings in address data hinder accurate patient relocation tracking.
  • Accurate identification of residential mobility is crucial for precise exposure assessment and avoiding misclassification.

Purpose of the Study:

  • To develop and validate an algorithm for identifying residential mobility events in pregnant patients.
  • To improve the accuracy of exposure assessment by accounting for patient relocation.
  • To compare the performance of the developed algorithm against using ZIP code differences alone.

Main Methods:

  • A cohort of 12,147 pregnant patients was obtained, with address data at delivery and one year prior for 9,959.
  • The Relocation Event Moving Algorithm for Patients (REMAP) was developed to detect residential mobility during pregnancy.
  • Area-deprivation scores were assigned to addresses, and the impact of residential mobility on these scores was assessed.

Main Results:

  • The REMAP algorithm demonstrated 95.7% accuracy in identifying residential mobility after manual review.
  • 41% of pregnant patients in the urban cohort relocated during pregnancy.
  • REMAP (95.7%) significantly outperformed using ZIP code differences alone (82.9%) in accuracy.
  • Failure to account for residential mobility led to a 39% misclassification of area deprivation.

Conclusions:

  • The REMAP algorithm accurately identifies residential mobility, addressing a key challenge in address-based exposure assessment.
  • ZIP code-based methods are insufficient for accurately determining patient relocation and its impact on exposure.
  • REMAP enhances the reliability of exposure assessment using electronic health record address data for researchers and policymakers.