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Administrative database code accuracy did not vary notably with changes in disease prevalence.

Carl van Walraven1, Shane English2, Peter C Austin3

  • 1Department of Medicine, University of Ottawa, 501 Smyth Road, Ottawa K1H 8L6, Canada; Clinical Epidemiology Program, Ottawa Hospital Research Institute, 501 Smyth Road, Ottawa K1H 8L6, Canada; Institute for Clinical Evaluative Sciences, 2075 Bayview Avenue, Toronto, Ontario M4N 3M5, Canada.

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Summary

Diagnostic codes show stable sensitivity and specificity regardless of disease prevalence. However, their positive and negative predictive values significantly fluctuate with prevalence, impacting diagnostic accuracy in administrative databases.

Keywords:
AccuracyAdministrative dataDiagnostic codeNegative predictive valueOperating characteristicsPositive predictive valuePrevalenceSensitivitySpecificity

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Area of Science:

  • Medical Informatics
  • Health Services Research
  • Epidemiology

Background:

  • Diagnostic test accuracy, particularly sensitivity and specificity, is known to vary with disease prevalence.
  • Mathematical analyses suggest prevalence significantly impacts continuous measures used in diagnostic testing.

Purpose of the Study:

  • To investigate if the accuracy of diagnostic codes, as used in administrative databases, is influenced by disease prevalence.
  • To determine the impact of varying disease prevalence on the sensitivity, specificity, and predictive values of diagnostic codes.

Main Methods:

  • Utilized data from two prior studies with established true disease status for renal disease and subarachnoid hemorrhage.
  • Employed stratified random sampling to create datasets with diverse disease prevalence.
  • Calculated sensitivity, specificity, positive predictive value, and negative predictive value for diagnostic codes within these varying prevalence samples.

Main Results:

  • Diagnostic code sensitivity and specificity remained consistent across a range of clinically relevant disease prevalences.
  • Positive and negative predictive values of diagnostic codes demonstrated significant variability in response to changes in disease prevalence.

Conclusions:

  • Disease prevalence does not substantially affect the sensitivity and specificity of diagnostic codes in administrative databases.
  • The predictive values of diagnostic codes are highly dependent on disease prevalence, which is a critical consideration for their interpretation.