All nonadherence is equal but is some more equal than others? Tuberculosis in the digital era
Adherence to treatment for tuberculosis (TB) has been a concern for many decades, resulting in the World Health Organization's recommendation of the direct observation of treatment in the 1990s. Recent advances in digital adherence technologies (DATs) have renewed discussion on how to best addr...
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2020-11-01
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doaj-ce84a4a2063e4ea6a10d99b783f798a22021-01-18T17:10:10ZengEuropean Respiratory SocietyERJ Open Research2312-05412020-11-016410.1183/23120541.00315-202000315-2020All nonadherence is equal but is some more equal than others? Tuberculosis in the digital eraHelen R. Stagg0Mary Flook1Antal Martinecz2Karina Kielmann3Pia Abel Zur Wiesch4Aaron S. Karat5Marc C.I. Lipman6Derek J. Sloan7Elizabeth F. Walker8Katherine L. Fielding9 Usher Institute, University of Edinburgh, Edinburgh, UK Usher Institute, University of Edinburgh, Edinburgh, UK Department of Biology, Pennsylvania State University, University Park, PA, USA The Institute for Global Health and Development, Queen Margaret University, Musselburgh, UK Department of Biology, Pennsylvania State University, University Park, PA, USA The Institute for Global Health and Development, Queen Margaret University, Musselburgh, UK UCL Respiratory, Division of Medicine, University College London, London, UK School of Medicine, University of St Andrews, St Andrews, UK London School of Hygiene & Tropical Medicine, London, UK Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK Adherence to treatment for tuberculosis (TB) has been a concern for many decades, resulting in the World Health Organization's recommendation of the direct observation of treatment in the 1990s. Recent advances in digital adherence technologies (DATs) have renewed discussion on how to best address nonadherence, as well as offering important information on dose-by-dose adherence patterns and their variability between countries and settings. Previous studies have largely focussed on percentage thresholds to delineate sufficient adherence, but this is misleading and limited, given the complex and dynamic nature of adherence over the treatment course. Instead, we apply a standardised taxonomy – as adopted by the international adherence community – to dose-by-dose medication-taking data, which divides missed doses into 1) late/noninitiation (starting treatment later than expected/not starting), 2) discontinuation (ending treatment early), and 3) suboptimal implementation (intermittent missed doses). Using this taxonomy, we can consider the implications of different forms of nonadherence for intervention and regimen design. For example, can treatment regimens be adapted to increase the “forgiveness” of common patterns of suboptimal implementation to protect against treatment failure and the development of drug resistance? Is it reasonable to treat all missed doses of treatment as equally problematic and equally common when deploying DATs? Can DAT data be used to indicate the patients that need enhanced levels of support during their treatment course? Critically, we pinpoint key areas where knowledge regarding treatment adherence is sparse and impeding scientific progress.http://openres.ersjournals.com/content/6/4/00315-2020.full |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Helen R. Stagg Mary Flook Antal Martinecz Karina Kielmann Pia Abel Zur Wiesch Aaron S. Karat Marc C.I. Lipman Derek J. Sloan Elizabeth F. Walker Katherine L. Fielding |
spellingShingle |
Helen R. Stagg Mary Flook Antal Martinecz Karina Kielmann Pia Abel Zur Wiesch Aaron S. Karat Marc C.I. Lipman Derek J. Sloan Elizabeth F. Walker Katherine L. Fielding All nonadherence is equal but is some more equal than others? Tuberculosis in the digital era ERJ Open Research |
author_facet |
Helen R. Stagg Mary Flook Antal Martinecz Karina Kielmann Pia Abel Zur Wiesch Aaron S. Karat Marc C.I. Lipman Derek J. Sloan Elizabeth F. Walker Katherine L. Fielding |
author_sort |
Helen R. Stagg |
title |
All nonadherence is equal but is some more equal than others? Tuberculosis in the digital era |
title_short |
All nonadherence is equal but is some more equal than others? Tuberculosis in the digital era |
title_full |
All nonadherence is equal but is some more equal than others? Tuberculosis in the digital era |
title_fullStr |
All nonadherence is equal but is some more equal than others? Tuberculosis in the digital era |
title_full_unstemmed |
All nonadherence is equal but is some more equal than others? Tuberculosis in the digital era |
title_sort |
all nonadherence is equal but is some more equal than others? tuberculosis in the digital era |
publisher |
European Respiratory Society |
series |
ERJ Open Research |
issn |
2312-0541 |
publishDate |
2020-11-01 |
description |
Adherence to treatment for tuberculosis (TB) has been a concern for many decades, resulting in the World Health Organization's recommendation of the direct observation of treatment in the 1990s. Recent advances in digital adherence technologies (DATs) have renewed discussion on how to best address nonadherence, as well as offering important information on dose-by-dose adherence patterns and their variability between countries and settings. Previous studies have largely focussed on percentage thresholds to delineate sufficient adherence, but this is misleading and limited, given the complex and dynamic nature of adherence over the treatment course. Instead, we apply a standardised taxonomy – as adopted by the international adherence community – to dose-by-dose medication-taking data, which divides missed doses into 1) late/noninitiation (starting treatment later than expected/not starting), 2) discontinuation (ending treatment early), and 3) suboptimal implementation (intermittent missed doses). Using this taxonomy, we can consider the implications of different forms of nonadherence for intervention and regimen design. For example, can treatment regimens be adapted to increase the “forgiveness” of common patterns of suboptimal implementation to protect against treatment failure and the development of drug resistance? Is it reasonable to treat all missed doses of treatment as equally problematic and equally common when deploying DATs? Can DAT data be used to indicate the patients that need enhanced levels of support during their treatment course? Critically, we pinpoint key areas where knowledge regarding treatment adherence is sparse and impeding scientific progress. |
url |
http://openres.ersjournals.com/content/6/4/00315-2020.full |
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