Evidence map›Paper›PMID 33263043›Full record

ReviewERJ open research2020

All nonadherence is equal but is some more equal than others? Tuberculosis in the digital era.

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

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in ERJ open research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

20 citing papers in PubMed, 1 synthesis or guideline pooled it.

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  19. "Journal of clinical tuberculosis and other mycobacterial diseases · 2021
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Helen R StaggUsher Institute, University of Edinburgh, Edinburgh, UK.ORCID https://orcid.org/0000-0003-4022-3447
Mary FlookUsher Institute, University of Edinburgh, Edinburgh, UK.
Antal MartineczDepartment of Biology, Pennsylvania State University, University Park, PA, USA.
Karina KielmannThe Institute for Global Health and Development, Queen Margaret University, Musselburgh, UK.
Pia Abel Zur WieschDepartment of Biology, Pennsylvania State University, University Park, PA, USA.ORCID https://orcid.org/0000-0001-9420-9005
Aaron S KaratThe Institute for Global Health and Development, Queen Margaret University, Musselburgh, UK.
Marc C I LipmanUCL Respiratory, Division of Medicine, University College London, London, UK.ORCID https://orcid.org/0000-0001-7501-4448
Derek J SloanSchool of Medicine, University of St Andrews, St Andrews, UK.ORCID https://orcid.org/0000-0002-7888-5449
Elizabeth F WalkerLondon School of Hygiene & Tropical Medicine, London, UK.
Katherine L FieldingDepartment of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK.ORCID https://orcid.org/0000-0002-6524-3754

Funding

Medical Research Council MR/R008345/1Medical Research Council MR/R010161/1
6 · The paper itself

Abstract

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.

Identifiers

PMID33263043
PMCPMC7682676

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.