University of Tasmania
Browse

Randomised controlled trial of active case management to link hepatitis C notifications to treatment in Tasmania, Australia: a study protocol

Download (530.86 kB)
Version 2 2024-11-21, 01:03
Version 1 2023-05-21, 16:49
journal contribution
posted on 2024-11-21, 01:03 authored by T Marukutira, KP Moore, M Hellard, J Richmond, K Turner, AE Pedrana, S Melody, Fay JohnstonFay Johnston, Louise OwenLouise Owen, W Van Den Boom, N Scott, A Thompson, D Iser, T Spelman, M Veitch, MA Stoove, J Doyle

Introduction: By subsidising access to direct acting antivirals (DAAs) for all people living with hepatitis C (HCV) in 2016, Australia is positioned to eliminate HCV as a public health threat. However, uptake of DAAs has declined over recent years and new initiatives are needed to engage people living with HCV in care. Active follow-up of HCV notifications by the health department to the notifying general practitioner (GP) may increase treatment uptake. In this study, we explore the impact of using hepatitis C notifications systems to engage diagnosing GPs and improve patient access to treatment.

Methods and analysis: This study is a randomised controlled trial comparing enhanced case management of HCV notifications with standard of care. The intervention includes phone calls from a department of health (DoH) specialist HCV nurse to notifying GPs and offering HCV management support. The level of support requested by the GP was graded in complexity: level 1: HCV information only; level 2: follow-up testing advice; level 3: prescription support including linkage to specialist clinicians and level 4: direct patient contact. The study population includes all GPs in Tasmania who notified HCV diagnosis to the DoH between September 2020 and December 2021. The primary outcome is proportion of HCV cases who initiate DAAs after 12 weeks of HCV notification to the health department. Secondary outcomes are proportion of HCV notifications that complete HCV RNA testing, treatment workup and treatment completion. Multiple logistic regression modelling will explore factors associated with the primary and secondary outcomes. The sample size required to detect a significant difference for the primary outcome is 85 GPs in each arm with a two-sided alpha of 0.05% and 80% power.

History

Publication title

BMJ Open

Volume

12

Issue

3

Pagination

1-7

ISSN

2044-6055

Department/School

Menzies Institute for Medical Research, Medicine

Publisher

BMJ Publishing Group Ltd.

Publication status

  • Published

Place of publication

United Kingdom

Rights statement

© Author(s) (or their employer(s)) 2022. Published by BMJ. This is an open access article distributed in accordance with the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license, https://creativecommons.org/licenses/by-nc/4.0/ which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited.

Socio-economic Objectives

200105 Treatment of human diseases and conditions

UN Sustainable Development Goals

3 Good Health and Well Being

Usage metrics

    University Of Tasmania

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC