The objective was to determine the best strategy for identifying outpatients with hypertension-related diagnoses using Veterans Affairs (VA) administrative databases. We reviewed 1176 outpatient charts from 10 VA sites in 1999, taking the presence of 11 diagnoses relevant to hypertension management as the "gold standard" for identifying the comorbidity. We calculated agreement, sensitivity, and specificity for the chart versus several administrative data-based algorithms. Using 1999 data and requiring 1 administrative diagnosis, observed agreement ranged from 0.98 (atrial fibrillation) to 0.85 (hyperlipidemia), and kappas were generally high. Sensitivity varied from 38% (tobacco use) to 97% (diabetes); specificity exceeded 91% for 10 of 11 diagnoses. Requiring 2 years of data and 2 diagnoses improved most measures, with minimal sensitivity decrease. Agreement between the database and charts was good. Administrative data varied in its ability to identify all patients with a given diagnosis but identified accurately those without. The best strategy for case-finding required 2 diagnoses in a 2-year period.
Hajjar I, Kotchen T. Trends in prevalence, awareness, treatment, and control of hypertension in the United States, 1988-2000. JAMA. 2003;290:199-206.
2.
Chobanian AV, Bakris GL, Cushman WC, et al.The seventh report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure. The JNC 7 Report. JAMA. 2003;289:2560-2572.
3.
Leatherman S, Peterson E, Heinen L, et al.Quality screening and management using claims data in a managed care setting. QRB Qual Rev Bull. 1991;349-359.
4.
Weiner JP, Parente ST, Garnick DW, et al.Variation in office-based quality: a claims-based profile of care provided to Medicare patients with diabetes. JAMA. 1995;273:1503-1508.
5.
Powe NR, Weiner JP, Starfied B, et al.Systemwide provider performance in a Medicaid program: profiling the care of patients with chronic illness. Med Care. 1996;34:798-810.
6.
Hebert PL, Geiss LS, Teirney EF, et al.Identifying persons with diabetes using Medicare claims data. Am J Med Qual. 1999;14: 270-277.
7.
Quam L, Ellis LBM, Venus P, et al.Using claims data for epidemiologic research. Med Care. 1993;31:498-507.
8.
Robinson JR, Young TK, Roos LI, et al.Estimating the burden of disease: comparing administrative data and self-reports. Med Care. 1997;35:932-947.
9.
Lurie N, Popkin M, Dysken M, et al.Accuracy of diagnoses of schizophrenia in Medicaid claims. Hosp Community Psychiatry1992;43:69-71.
10.
Borzecki AM, Wong AT, Hickey EC, et al.Hypertension control: how well are we doing?Arch Intern Med. 2003;163:2705-2711.
11.
Deyo RA, Cherkin DC, Ciol MA. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol. 1992;45:613-619.
12.
Sackett DL, Haynes RB, Guyatt GH, et al.Clinical Epidemiology: A Basic Science for Clinical Medicine. 2nd ed.Boston, Mass: Little Brown and Company;1991.
13.
Landis JR, Koch GC. The measurement of observer agreement for categorical data. Biometrics. 1977;33:159-174.
14.
Szeto HC, Coleman RK, Gholami P, et al.Accuracy of computerized outpatient diagnoses in a Veterans Affairs general medicine clinic. Am J Manag Care. 2001;7:51-57.
15.
Halpern J. The measurement of quality of care in the Veterans Health Administration. Med Care. 1996;34(suppl 3);55-68.
16.
Fowles JB, Lawthers AG, Weiner JP, et al.Agreement between physicians' office records and Medicare. Part B claims data. Health Care Financ Rev. 1995;16:189-199.
