Furthermore, current CDC case definitions may miss a substantial fraction of clinically apparent acute cases because they lack clinical markers that could improve case identification and help to distinguish between acute and chronic cases. Using data from electronic medical records, Klompas et al. (2008) found that CDC’s case definition of acute HBV had a positive predictive value of only 47.2% (that is, out of 1,000 people identified as having acute hepatitis B with the CDC case definition, only 472 of them were found to truly have acute hepatitis B). When patients with prior positive tests for HBV infection (or International Classification of Diseases, revision 9, codes for chronic HBV infection) were excluded, the positive predictive value increased to 68.4%. However, the positive predictive value was raised to above 96% by adding the requirement for peak ALT over 1,000 or total bilirubin over 1.5. Most important, when applying the most sensitive algorithm (the algorithm that detected the greatest number of cases of acute hepatitis B), the study found that only four of the eight cases of acute hepatitis B were in the state’s surveillance system and only one of the four was correctly classified as acute; this suggests that 88% of acute hepatitis B cases may be missed if current reporting algorithms are used (Klompas et al., 2008).
Similarly, detection of acute hepatitis C can be challenging because no single case definition is either sensitive or specific for it. HCV seroconversion may be missed, and there is no IgM-based assay that reliably distinguishes acute hepatitis C from chronic hepatitis C, unlike the situation with hepatitis A virus or HBV infection. Relatively low HCV ribonucleic acid (RNA) concentrations and more than one log fluctuation in HCV RNA concentration are features of acute HCV infection that may be useful for the development of more dynamic diagnostic algorithms, but the accuracy of these algorithms has not been validated (Cox et al., 2005; McGovern et al., 2009; Villano et al., 1999).
In summary, the identification of acute hepatitis infection is inherently flawed because the vast majority of cases are asymptomatic and patients do not seek medical care or testing. Such persons would be identified only in prospective studies that include routine serial testing of liver enzyme concentrations, such as those previously conducted to identify the incidence of transfusion-associated hepatitis. Underreporting of diagnosed cases and misclassification of reported cases seriously limit the accuracy of data on cases of acute viral hepatitis collected by state and territorial surveillance programs and transmitted to CDC. Thus, the estimates of the incidence of acute hepatitis in the United States are based solely on symptomatic cases. The majority of those cases may be missing from the surveillance system because of poor access to health care, underreporting, and misclassification. Taken together, published surveillance summaries of reported cases of acute viral hepatitis substantially underestimate the number of cases; these