Data to Care for People Coinfected with HIV and Hepatitis C Virus

The Michigan Department of Health and Human Services (MDHHS) was one of seven health departments funded by Leveraging a Data to Care Approach to Cure Hepatitis C Virus (HCV) Within the Ryan White HIV/AIDS Program (RWHAP), a RWHAP Part F Special Projects of National Significance (SPNS) initiative implemented from 2020–2022. With the support of the Yale University School of Medicine, which served as the Technical Assistance Provider, MDHHS matched RWHAP and HIV and HCV surveillance data, calculated HCV viral clearance cascades for coinfected populations, and worked with three RWHAP clinics to generate clinic-based lists of coinfected clients and conduct outreach and linkage to HCV treatment.


Implementation Guide
Emerging Intervention
Emerging Intervention
Icon for Intervention Type
Data utilization approach; Outreach and reengagement activities
Icon for HIV Care Continuum
Beyond the care continuum
Icon for Focus Population
People with HCV
Icon for Priority Funding
Icon for Setting
State health department; RWHAP-funded clinic or organization
Need Addressed

Approximately 21% of people with HIV in the United States are coinfected with HCV; among people with HIV who inject drugs, the prevalence of HCV coinfection can be as high as 80%.1 Coinfection with both HIV and HCV dramatically increases the risk for serious liver complications and decreases life expectancy, even among people receiving antiretroviral therapy for HIV.1 Direct-acting antivirals offer a safe, well-tolerated, and curative treatment for HCV, but about half of all coinfected individuals remain untreated.2 Testing is limited, so many individuals are unaware of their HCV infection status.3 The high cost of HCV treatment has resulted in health care coverage restrictions, which also creates a barrier to care. In Michigan, Medicaid recently removed these restrictions, but providers are still gaining skills with treatment; thus, there are “hepatitis C treatment deserts,” where clients may have to wait two to four months for treatment.

Core Elements
Creating lists of coinfected clients

The first step of the project was to identify all coinfected individuals in the state and the subset of those enrolled in RWHAP. To do so, MDHHS matched HIV surveillance data (eHARS) with all available HCV data in the surveillance database through an algorithm that categorized pairs of records as exact matches, non-matches, and potential matches, which were then manually reviewed. The state used surveillance data through the end of 2019 to create the list. The subset of coinfected clients enrolled in RWHAP was developed by matching the list to the state’s networked CAREWare system, which captures Parts A, B, C, D and AIDS Drug Assistance Program (ADAP) data.

Calculating HCV viral clearance cascades

MDHHS then calculated HCV viral clearance cascades, aligned with current Centers for Disease Control and Prevention (CDC) guidance for overall and RWHAP populations based on HCV surveillance data. The clearance cascade identified:

  • The number of people with HIV and a positive hepatitis C antibody test;
  • The subset of those with a follow-up positive polymerase chain reaction (PCR) test, indicating chronic HCV;
  • The subset of those who demonstrated viral clearance, as indicated by a subsequent negative PCR test; and
  • The subset of those who were reinfected (subsequent positive PCR test).

MDHHS calculated these statuses by demographic and clinical characteristics to identify disparities in viral clearance rates. HIV surveillance data was primarily used for this analysis given this system has more robust demographic and risk factor information.

Outreach and linkage to care

MDHHS partnered with three RWHAP clinics for outreach and linkage to care activities. These clinics were selected based on area HCV prevalence, history of successful collaboration with MDHHS, and state access to clinic data. MDHHS developed clinic-based lists of coinfected clients using CAREWare and surveillance data. The clinics compared these lists to internal electronic health record (EHR) data, which often had more up-to-date information on HCV and RWHAP enrollment status (e.g., moved from area, incarcerated); this process was called “case conferencing” and involved discussions between MDHHS and clinic staff.  Clinic staff contacted coinfected clients on the list to encourage them to come into the clinic for follow-up HCV care, either a PCR test after a positive antibody test, HCV treatment, or a PCR test after HCV treatment. Using a tool developed by Yale, clinics were able to generate treatment cascades based on results.


The 2019 surveillance data cohort included 1,060 people who had HIV and a positive hepatitis C antibody test. Of those, 572 had a positive PCR test, indicating a diagnosis of chronic HCV. MDHHS calculated the percent of people with chronic HCV who had a subsequent negative PCR test, indicating cure or self-clearance, at baseline and quarterly through the end of 2021 for the entire state population of coinfected persons, and for the subset of those enrolled in RWHAP.

Evaluation data
  • HIV and HCV surveillance data; CAREWare data
  • Percent of coinfected people in the whole state who had cleared HCV, as evidenced by a negative PCR test after a positive PCR test.
  • Percent of coinfected people in the RWHAP program who had cleared HCV.
  • The percentage of people in the 2019 state cohort who had cleared HCV rose from 28% at baseline (lab data through the end of 2019) to 47% (lab data through the end of 2021). 
  • The percent of the RWHAP population who had cleared HCV was higher at baseline compared to the state cohort, 31%, and rose to 56% with lab data as of the end of 2021.
Planning & Implementation

HIV and HCV surveillance matching algorithm. MDHHS used an algorithm originally developed by New York City and modified by the CDC to match HIV and HCV surveillance data and create a list of coinfected clients. MDHHS has used the algorithm since 2016, when the state first participated in a project with the CDC and 15 other states to match HIV and HCV surveillance databases.

Excel-based HCV viral clearance cascade template. Yale provided Excel-based templates that, once populated, were used to create the clearance cascades (see below in Resources & Tools). The Excel templates captured numbers of people who tested positive for HIV and an HCV antibody test, and had differing PCR test result statuses (e.g., no PCR test, negative PCR test, positive PCR test, and negative PCR test after a positive test). The template also captured the number of people reinfected with HCV. For each data element, the template allowed MDHHS to report by various demographic and clinical characteristics.

Case conferencing tool. Yale developed a second Excel tool, the “case conferencing tool,” to facilitate outreach and linkage activities (see below in Resources & Tools). MDHHS completed the first part of the tool with client demographic information and HCV status found in surveillance systems. The clinics completed the second section, confirming or updating HCV status with information in their EHR systems. For clients needing an intervention (e.g., HCV treatment, follow-up testing), clinics completed a third section on outreach and linkage activities, denoting barriers to care, client contacts, and outcomes of outreach and linkage activities (e.g., medical appointment scheduled). The tool calculated a clinic-specific HCV treatment cascade based on results.


The costs of the program include state staff time to match data, create the clearance cascade, and work with clinics, in addition to clinic time dedicated to EHR data review, outreach and linkage, and data entry activities. Existing RWHAP funds covered these activity costs. MDHHS plans to continue matching RWHAP and HIV and HCV surveillance data and creating clearance cascades. The state would also like to work with additional clinics on HCV data to care activities; however, the state recognizes the need for additional funding to cover a staff person dedicated to this work given competing priorities of existing staff.   

Lessons Learned
  • MDHHS’s HCV data to care initiative was successful in part due to the state’s larger and historic efforts to cure people of HCV. Through the We Treat Hepatitis C initiative, the MDHHS Medicaid program lifted prior authorization for a specific HCV medication and disseminated information to providers on new processes. The state also offers multiple provider capacity-building opportunities (e.g., warm lines), dedicates funding for HCV outreach and linkage activities, and works with correctional agencies to provide HCV medications to people incarcerated or recently released.  
  • While HCV surveillance systems have historically been underfunded compared to HIV surveillance systems nationwide, MDHHS has robust HCV surveillance data quality processes, including matching data to death records, de-duplicating data, and conducting case audits.  
  • MDHHS’s HCV surveillance data completeness and accuracy were greatly improved in 2019 when reporting requirements changed to include negative PCR tests. The process to integrate the negative results was initially slow, but MDHHS is now up to date in adding all negative values primarily through enhanced electronic laboratory reporting. The inclusion of these negative lab values greatly contributed to the increase in percent of people cured. 
  • To facilitate the reporting of comprehensive lab values, especially negative results, the state has onboarded as many laboratories as possible onto the electronic lab reporting system.
  • The state’s networked CAREWare system allowed for the efficient generation of the overall RWHAP list and clinic-based lists for outreach and linkage activities. 
  • Based on state public health statute, the jurisdiction can share HIV and HCV surveillance data with a patient’s treating provider. Therefore, the jurisdiction did not have to establish individual data sharing agreements with the clinics. Staff just confirmed the person was coinfected and seen at the clinic through the CAREWare system before sharing data with the person’s provider.
Resources & Tools

Implementation Resources

Additional Resources 

Michigan Department of Health and Human Services
Macey Ladisky

We'd like your feedback

Was this page helpful?
I found this page helpful because the content on the page:
Check all that apply
I did not find this page helpful because the content on the page:
Check all that apply
Please include an email address if you would like a response
Please include an email address if you would like a response
Did you use this approach in your work?
Not yet because
If no, why not?