Fischer L R, Tope D H, Conboy K S, Hedblom B D, Ronberg E, Shewmake D K, Butler J C
HealthPartners Research Foundation, Minneapolis, Minn 55440-1524, USA.
Arch Intern Med. 2000 Jun 12;160(11):1665-73. doi: 10.1001/archinte.160.11.1665.
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.
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.
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.
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.
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.
丙型肝炎病毒（HCV）慢性感染是一个重大的公共卫生问题，在美国每年导致超过10000人死亡。在其早期阶段，HCV往往没有症状，只能通过筛查检测出来。
利用明尼苏达州一家健康维护组织（HMO）HealthPartners的电子数据，开发并验证一种用于HCV感染的数据库风险算法。第二个目标是评估对医护人员进行HCV筛查的益处。
利用行政数据库中的诊断和程序代码开发一种数据库风险算法，以识别高危参保者。1380名参保者（一个高危样本和一个对照样本）以及502名医护人员参与了匿名筛查。描述性统计和逻辑回归均用于检查HCV感染的频率、与风险因素的关联、参与筛查的自我选择因素，以及数据库风险算法与风险概况问卷之间的一致性。
11名参保者HCV检测呈阳性，9名来自高危样本，2名来自对照样本。所有医护人员HCV检测均为阴性。生活方式和医疗风险因素均与HCV检测阳性结果相关。有酒精 - 药物诊断的参保者参与筛查的可能性较小。相当一部分有风险因素的参保者通过数据库风险算法或风险概况问卷被识别出来，但并非两者都能识别。
虽然HCV感染频率低于之前对美国人群的估计，但与风险因素的强相关性表明，使用数据库风险算法进行筛查是一种有用的方法。拥有参保人群合适数据的管理式医疗计划，在检测感染HCV的无症状患者方面，处于发挥重要公共卫生作用的关键地位。
