Abstract
Objectives
Previous mathematical analyses of diagnostic tests based on the categorization of
a continuous measure have found that test sensitivity and specificity varies significantly
by disease prevalence. This study determined if the accuracy of diagnostic codes varied
by disease prevalence.
Study Design and Setting
We used data from two previous studies in which the true status of renal disease and
primary subarachnoid hemorrhage, respectively, had been determined. In multiple stratified
random samples from the two previous studies having varying disease prevalence, we
measured the accuracy of diagnostic codes for each disease using sensitivity, specificity,
and positive and negative predictive value.
Results
Diagnostic code sensitivity and specificity did not change notably within clinically
sensible disease prevalence. In contrast, positive and negative predictive values
changed significantly with disease prevalence.
Conclusion
Disease prevalence had no important influence on the sensitivity and specificity of
diagnostic codes in administrative databases.
Keywords
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References
- Health services research: reporting on studies using secondary data sources.CMAJ. 1996; 155: 1697-1709
- Development and use of reporting guidelines for assessing the quality of validation studies of health administrative data.J Clin Epidemiol. 2011; 64: 821-829
- Administrative database research infrequently uses validated diagnostic or procedural codes.J Clin Epidemiol. 2011; 64: 1054-1059
- The interpretation of diagnostic data. Clinical epidemiology. A basic science for clinical medicine.Little, Brown, Boston1991: 69-152
- Quantitative aspects of clinical reasoning.in: Isselbacher K.J. Braunwald E. Wilson J.D. Martin J.B. Fauci A.S. Kasper D.L. Harrison's principles of internal medicine. 13th ed. McGraw-Hill, New York1994: 43-48
- Variation of sensitivity, specificity, likelihood ratios and predictive values with disease prevalence.Stat Med. 1997; 16: 981-991
- The usefulness of administrative databases for identifying disease cohorts is increased with a multivariate model.J Clin Epidemiol. 2010; 63: 1332-1341
- Prediction equations to estimate glomerular filtration rate: an update.Curr Opin Nephrol Hypertens. 2001; 10: 785-792
- K/DOQI clinical practice guidelines for chronic kidney disease: evaluation, classification, and stratification.Am J Kidney Dis. 2002; 39: S1-S266
- Definition and classification of chronic kidney disease: a position statement from kidney disease: improving global outcomes (KDIGO).Kidney Int. 2005; 67: 2089-2100
- Enriched administrative data can be used to retrospectively identify all known cases of primary subarachnoid hemorrhage.J Clin Epidemiol. 2016; 70: 146-154
Article info
Publication history
Published online: June 03, 2016
Accepted:
May 27,
2016
Footnotes
Funding: C.v.W. is supported by a University of Ottawa Department of Medicine Clinician Scientist Chair. P.C.A. is supported by a Heart and Stroke Foundation Career Investigator Award.
Conflict of interest: None.
Identification
Copyright
© 2016 Elsevier Inc. All rights reserved.
