BACKGROUND: Chronic infection with hepatitis C virus (HCV) is a major public health problem and is associated with over 10,000 deaths a year in the United States. In its early stages, HCV tends to be asymptomatic and can be detected only through screening. OBJECTIVES: To develop and validate a database risk algorithm for HCV infection using electronic data at HealthPartners, a health maintenance organization (HMO) in Minnesota. A secondary objective was to evaluate the benefit of screening health care workers for HCV. METHODS: A database risk algorithm was developed using diagnostic and procedure codes in the administrative database to identify at-risk enrollees. One thousand three hundred eighty enrollees (an at-risk sample and a control sample) and 502 health care workers participated in anonymous screening. Both descriptive statistics and logistic regression were used to examine the frequency of HCV infection, associations with risk factors, self-selection factors in participation, and concordance between the database risk algorithm and the risk profile questionnaire. RESULTS: Eleven enrollees tested positive for HCV, 9 from the at-risk sample and 2 from the control sample. All health care workers tested negative for HCV. Both lifestyle and medical risk factors were associated with positive test results for HCV. Enrollees with alcohol-drug diagnoses were less likely to participate in screening. A substantial proportion of enrollees with risk factors was identified either by the database risk algorithm or the risk profile questionnaire, but not by both. CONCLUSION: While the frequency of HCV infection was lower than previous estimates for the US population, the strong correlation with risk factors suggests that using the database risk algorithm for screening is a useful approach. Managed care plans with suitable data on their enrollee populations are in a key position to serve an important public health role in detecting asymptomatic patients who are infected with HCV.
BACKGROUND:Chronic infection with hepatitis C virus (HCV) is a major public health problem and is associated with over 10,000 deaths a year in the United States. In its early stages, HCV tends to be asymptomatic and can be detected only through screening. OBJECTIVES: To develop and validate a database risk algorithm for HCV infection using electronic data at HealthPartners, a health maintenance organization (HMO) in Minnesota. A secondary objective was to evaluate the benefit of screening health care workers for HCV. METHODS: A database risk algorithm was developed using diagnostic and procedure codes in the administrative database to identify at-risk enrollees. One thousand three hundred eighty enrollees (an at-risk sample and a control sample) and 502 health care workers participated in anonymous screening. Both descriptive statistics and logistic regression were used to examine the frequency of HCV infection, associations with risk factors, self-selection factors in participation, and concordance between the database risk algorithm and the risk profile questionnaire. RESULTS: Eleven enrollees tested positive for HCV, 9 from the at-risk sample and 2 from the control sample. All health care workers tested negative for HCV. Both lifestyle and medical risk factors were associated with positive test results for HCV. Enrollees with alcohol-drug diagnoses were less likely to participate in screening. A substantial proportion of enrollees with risk factors was identified either by the database risk algorithm or the risk profile questionnaire, but not by both. CONCLUSION: While the frequency of HCV infection was lower than previous estimates for the US population, the strong correlation with risk factors suggests that using the database risk algorithm for screening is a useful approach. Managed care plans with suitable data on their enrollee populations are in a key position to serve an important public health role in detecting asymptomatic patients who are infected with HCV.
Authors: Hermann E Wasmuth; Alexa Werth; Tobias Mueller; Thomas Berg; Christoph G Dietrich; Andreas Geier; Ramin Schirin-Sokhan; Carsten Gartung; Johann Lorenzen; Siegfried Matern; Frank Lammert Journal: J Mol Med (Berl) Date: 2003-12-13 Impact factor: 4.599