RT Journal Article
SR Electronic
T1 All nonadherence is equal but is some more equal than others? Tuberculosis in the digital era
JF ERJ Open Research
JO erjor
FD European Respiratory Society
SP 00315-2020
DO 10.1183/23120541.00315-2020
VO 6
IS 4
A1 Stagg, Helen R.
A1 Flook, Mary
A1 Martinecz, Antal
A1 Kielmann, Karina
A1 Abel Zur Wiesch, Pia
A1 Karat, Aaron S.
A1 Lipman, Marc C.I.
A1 Sloan, Derek J.
A1 Walker, Elizabeth F.
A1 Fielding, Katherine L.
YR 2020
UL http://openres.ersjournals.com/content/6/4/00315-2020.abstract
AB 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.Digital adherence technologies (DATs) provide a wealth of information on dose-by-dose anti-TB medication-taking. Studies of DAT data should place nonadherence in standardised taxonomic frameworks in order to best inform intervention and regimen design. https://bit.ly/3jq1D8a