An Integrated Care Model for Improving Hepatitis C Virus Patient Outcomes

Impact for Veterans: Treatments are available that can often eliminate the hepatitis C virus (HCV) in infected veterans, resulting in increased life expectancy and quality of life. However, many veterans do not get treated or fail treatment because of a variety of factors. Using a new protocol-based model of care that centers around integrating psychiatric care and case management into VA HCV clinics, it may be possible to increase treatment rates, improve rates of sustained viral response (SVR), and improve health outcomes for veterans with hepatitis C.

Background: The prevalence of hepatitis C virus (HCV) infection among VA patients is 3x higher than in the general population. VA patients with HCV have characteristics linked to increased risk for progression to cirrhosis, such as male sex, high body mass index, tobacco or marijuana use, and a history of heavy alcohol use. Antiviral treatment is the only evidence-based treatment that has been shown to eradicate the HCV virus and reduce the progression of liver fibrosis. However, recent VA data indicate that only about 14% of all HCV-infected VA patients have ever received antiviral therapy. Barriers to receiving antiviral treatment include factors such as pre-existing psychiatric illness, ongoing substance abuse, and other medical co-morbidity. Patients with psychiatric and substance use histories have benefitted from an integrated care approach to HCV care, but a more rigorous study design is required to demonstrate effectiveness.

Primary Objective: To determine the effectiveness of a protocol-based integrated care model for increasing treatment rates and the number of patients with successful antiviral treatment as measured by an increase in percentage of all new HCV patients achieving a sustained virologic response (SVR). We hypothesize that this model will increase the proportion of patients who are fully evaluated for treatment, who initiate treatment, and who complete treatment compared with patients that receive usual care. Secondary Objectives: Assess the effects of an integrated care model on patient involvement in care (appointment attendance) and PROs (substance abuse, depression, anxiety). We hypothesize that veterans managed with the integrated care model will have better attendance at HCV clinic appointments, have improved rates of drug and alcohol abstinence, and fewer psychological symptoms.

Project Methods: The intervention is an integrated clinical care model developed and initially studied at the Minneapolis VA HCV Clinic. The preliminary study found that integrated care resulted in higher treatment rates, higher SVRs, reduced substance use, and improved psychological functioning. Thus, we will use a more rigorous randomized design to study the effectiveness of this intervention at 3 major VA medical centers. All clinic patients will be screened for depression, anxiety, PTSD, or recent SUDs and all patients that screen positive on 1 or more of these measures will be asked to participate. Preliminary data suggest that 85% of HCV clinic patients screen positive on at least one measure. Eligible patients will be randomized to either usual or integrated care at each site. The integrated care intervention follows a manualized protocol consisting of a series of brief intervention tailored to the patients' main barriers to treatment along with a case management approach in which the integrated care mental health provider actively tracks each patients progress through the evaluation and treatment process. The integrated care mental health provider can be a clinical nurse specialist, psychologist, or licensed clinical social worker that has experience and training in the provision of psychiatric and SUD interventions. They will receive additional training on the integrated care protocol. Data will be collected at baseline, pre-treatment, and post-treatment intervals. Clinical data will be obtained from VA medical records by the study coordinator at each site. PROs will be assessed using validated measures. Data will be analyzed using hierarchical linear modeling (HLM) techniques.