Evidence map›Paper›PMID 28279457›Full record

ArticleEpidemics2017

Measuring and modelling the effects of systematic non-adherence to mass drug administration.

Louise Dyson, Wilma A Stolk, Sam H Farrell, T Déirdre Hollingsworth

Abstract read
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In one paragraph

Article in Epidemics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 70 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
70citing papers in PubMed, 4 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

70 citing papers in PubMed, 4 syntheses or guidelines pooled it.

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10 more citing papers are in PubMed but not listed here.

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

4 authors.

Louise DysonMathematics Institute, University of Warwick, Coventry, UK; School of Life Sciences, University of Warwick, Coventry, UK. Electronic address: l.dyson@warwick.ac.uk.
Wilma A StolkDepartment of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands.
Sam H FarrellLondon Centre for Neglected Tropical Disease Research, Department of Infectious Disease Epidemiology, St Mary's Campus, Imperial College London, London WC2 1PG, UK.
T Déirdre HollingsworthMathematics Institute, University of Warwick, Coventry, UK; School of Life Sciences, University of Warwick, Coventry, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

It is well understood that the success or failure of a mass drug administration campaign critically depends on the level of coverage achieved. To that end coverage levels are often closely scrutinised during campaigns and the response to underperforming campaigns is to attempt to improve coverage. Modelling work has indicated, however, that the quality of the coverage achieved may also have a significant impact on the outcome. If the coverage achieved is likely to miss similar people every round then this can have a serious detrimental effect on the campaign outcome. We begin by reviewing the current modelling descriptions of this effect and introduce a new modelling framework that can be used to simulate a given level of systematic non-adherence. We formalise the likelihood that people may miss several rounds of treatment using the correlation in the attendance of different rounds. Using two very simplified models of the infection of helminths and non-helminths, respectively, we demonstrate that the modelling description used and the correlation included between treatment rounds can have a profound effect on the time to elimination of disease in a population. It is therefore clear that more detailed coverage data is required to accurately predict the time to disease elimination. We review published coverage data in which individuals are asked how many previous rounds they have attended, and show how this information may be used to assess the level of systematic non-adherence. We note that while the coverages in the data found range from 40.5% to 95.5%, still the correlations found lie in a fairly narrow range (between 0.2806 and 0.5351). This indicates that the level of systematic non-adherence may be similar even in data from different years, countries, diseases and administered drugs.

Indexed as

Drug MonitoringModels, TheoreticalAnthelminticsHelminthiasisHumansMass Drug AdministrationAnthelminticsCoverageModellingNeglected tropical diseasesnon-adherenceSystematicSystematic non-compliance

Identifiers

PMID28279457
PMCPMC5340860

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Registered trials

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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.