Theme: Models of Care Year: 2018
Background: Eliminating HCV will require innovative strategies to engage and support patients at risk of non-adherence to treatment. With the advent of DAAs, risk of non-adherence should not be a reason to defer treatment. DAAs are highly effective – if taken – but optimal adherence is yet to be determined and all patients are at risk of non-adherence. The long-term success of DAAs and prevention of drug resistance will depend on achieving high levels of adherence and new tools to assess and support medication adherence are vital. Directly Observed Treatment (DOT) is a proven intervention to support TB patients to complete treatment but places a burden on both patients and providers. Video Observed Treatment (VOT) is a more effective, acceptable and cheaper alternative to promote adherence among socially complex patients. Approach: We report, to our knowledge, the world’s first cohort of socially complex HCV patients treated using a community peer led service model with VOT adherence support. All patients were currently or recently actively using drugs and homeless at start of treatment. A trained peer provided training on how to use the secure mobile app and then remotely / asynchronously viewed and acknowledged the patients dosing videos. Outcome: This pilot includes baseline data on risk factors and outcome data including interval SVR. We included a qualitative component to the pilot and validated measures of quality of life. ‘VOT was easy and helped me remember to take treatment and stick with it. The phone was a real bonus – like having support on hand.’ [First successful HCV VOT patient] Conclusion: VOT for socially complex HCV patients can promote treatment adherence and engagement with care. This promising digital health intervention needs further rigorous evaluation, including randomised controlled trials, to determine its impact on clinical outcomes and costeffectiveness. Disclosure of Interest Statement: J Gibbons was funded via a GILEAD Fellowship with the NGO Groundswell.
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