Context: Public health surveillance systems for acute hepatitis are limited: clinician reporting is insensitive and electronic laboratory reporting is nonspecific. Insurance claims and electronic health records are potential alternative sources.
Objective: To compare the utility of laboratory data, diagnosis codes, and electronic health record combination data (current and prior viral hepatitis studies, liver function tests, and diagnosis codes) for acute hepatitis A and B surveillance.
Design: Retrospective chart review.
Setting: Massachusetts ambulatory practice serving 350 000 patients per year.
Participants: All patients seen between 1990 and 2008.
Main Outcome Measures: Sensitivity and positive predictive value of immunoglobulin M (IgM), International Classification of Disease—Ninth Revision (ICD-9) diagnosis codes, and combination electronic health record data for acute hepatitis A and B.
Results: During the study period, there were 111 patients with positive hepatitis A IgMs, 154 with acute hepatitis A ICD-9 codes, and 77 with positive IgM and elevated liver function tests. On review, 79 cases were confirmed. Sensitivity and positive predictive value were 100% and 71% (95% confidence interval, 62%–79%) for IgM, 94% (92%–100%) and 48% (40%–56%) for ICD-9 codes and 97% (92%–100%) and 100% (96%–100%) for combination electronic health record data. There were 14 patients with positive hepatitis B core IgMs, 2564 with acute hepatitis B ICD-9 codes, and 125 with suggestive combinations of electronic health record data. Acute hepatitis B was confirmed in 122 patients. Sensitivity and positive predictive value were 9.4% (5.2%–16%) and 86% (60%–98%) for hepatitis B core IgM, 73% (65%–80%) and 3.6% (2.9%–4.4%) for ICD-9 codes, and 96% (91%–99%) and 98% (94%–99%) for electronic health record data.
Conclusions: Laboratory surveillance using IgM tests overestimates the burden of acute hepatitis A and underestimates the burden of acute hepatitis B. Claims data are subject to many false positives. Electronic health record data are both sensitive and predictive. Electronic health record–based surveillance systems merit development.