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Related Experiment Videos

Measuring medication: do interviews agree with medical record and pharmacy data?

P Todd Korthuis1, Steven Asch, Martha Mancewicz

  • 1Greater Los Angeles VA Health Care System, UCLA, Building 500, Room 3233, 11301 Wilshire Boulevard (111G), Los Angeles, CA 90073, USA. pkorthui@ucla.edu

Medical Care
|November 30, 2002
PubMed
Summary

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Medication data agreement between interviews, medical records, and pharmacy data varies. Relying on a single source may misclassify drug exposure, but combining sources doesn't improve predictive models for medication use.

Area of Science:

  • Health Services Research
  • Pharmacoepidemiology
  • Chronic Disease Management

Background:

  • Accurate medication measurement is vital for assessing chronic condition care quality.
  • The agreement between various data sources for medication information is not well-established.

Purpose of the Study:

  • To evaluate medication data agreement across interviews, medical records, and pharmacy records.
  • To determine how each data source contributes to attributing medication exposure.
  • To assess the impact of combining data sources on models predicting medication use.

Main Methods:

  • Prospective cohort study involving a probability sample of HIV-infected participants.
  • Compared medications reported in interviews (N=2267), medical records (N=1936), and pharmacy records (N=457).

Related Experiment Videos

  • Utilized kappa statistics, crude agreement, and logistic regression to analyze data.

    • Main Results:

    • Agreement (kappa) ranged from 0.38 (nucleoside reverse transcriptase inhibitors) to 0.70 (protease inhibitors).
    • Medical records provided a higher percentage of reported medications compared to interviews or pharmacy data.
    • Pharmacy data contributed minimally to attributing medication exposure; combining data sources did not significantly alter predictive models.

    Conclusions:

    • Fair to substantial agreement exists for specific medications, but is lower for key drug classes.
    • Sole reliance on one data source risks misclassification of drug exposure.
    • Combining medication data sources does not substantially change analytic models for predicting medication use.