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An electronic health record driven algorithm to identify incident antidepressant medication users.

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This study validates an algorithm using prescription records to identify new and existing antidepressant users. The algorithm achieved high accuracy, confirming its utility in population health research.

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Area of Science:

  • Pharmacoepidemiology
  • Health Informatics
  • Population Health

Background:

  • Accurate identification of antidepressant users is crucial for population health studies.
  • Electronic health records and prescription data offer potential for large-scale user identification.
  • Existing methods may not efficiently distinguish new from prevalent users.

Purpose of the Study:

  • To validate an algorithm for identifying new and prevalent antidepressant medication users.
  • To assess the accuracy of using population-based drug prescription records.
  • To evaluate the algorithm's performance in Olmsted County, Minnesota.

Main Methods:

  • Utilized population-based drug prescription records from Olmsted County (2011-2012).
  • Employed the Rochester Epidemiology Project (REP) electronic medical records linkage infrastructure.
  • Manually reviewed and adjudicated records of new antidepressant users and antihistamine users to calculate positive predictive value (PPV) and negative predictive value (NPV).

Main Results:

  • The algorithm achieved a PPV of 81.3% for identifying new antidepressant users.
  • Refining the definition to typical starting doses for major depression increased PPV to 90.9%.
  • The NPV for antihistamine users was high at 98.5%.

Conclusions:

  • Rochester Epidemiology Project (REP) prescription records are effective for identifying prevalent and incident antidepressant users.
  • The validated algorithm demonstrates utility for pharmacoepidemiological research.
  • This method supports accurate population-level analysis of antidepressant use.