Evidence map›Paper›PMID 38723040›Full record

ArticlePLoS medicine2024

Measuring protective efficacy and quantifying the impact of drug resistance: A novel malaria chemoprevention trial design and methodology.

Andria Mousa, Gina Cuomo-Dannenburg, Hayley A Thompson, R Matthew Chico, Khalid B Beshir, Colin J Sutherland, David Schellenberg, Roly Gosling, Michael Alifrangis, Emma Filtenborg Hocke and 7 more

Abstract read
In one paragraph

Article in PLoS medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–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

5 citing papers in PubMed.

  1. Trial
  2. Article
  3. Article
  4. Article
  5. Article
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

17 authors.

Andria MousaFaculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom.ORCID 0000-0001-9406-7481
Gina Cuomo-DannenburgMRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom.ORCID 0000-0001-6821-0352
Hayley A ThompsonMalaria and Neglected Tropical Diseases, PATH, Seattle, Washington, United States of America.
R Matthew ChicoFaculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom.ORCID 0000-0003-1795-7100
Khalid B BeshirFaculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom.ORCID 0000-0001-5108-8362
Colin J SutherlandFaculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom.ORCID 0000-0003-1592-6407
David SchellenbergFaculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom.ORCID 0000-0001-8222-0186
Roly GoslingFaculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom.ORCID 0000-0001-6291-0024
Michael AlifrangisCenter for Medical Parasitology, Department of Immunology and Microbiology, University of Copenhagen, Copenhagen, Denmark.ORCID 0000-0002-3835-3333
Emma Filtenborg HockeCenter for Medical Parasitology, Department of Immunology and Microbiology, University of Copenhagen, Copenhagen, Denmark.ORCID 0000-0002-0040-7041
Helle HanssonCenter for Medical Parasitology, Department of Immunology and Microbiology, University of Copenhagen, Copenhagen, Denmark.
Ana Chopo-PizarroFaculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom.ORCID 0000-0002-4330-4085
Wilfred F MbachamThe Biotechnology Centre, University of Yaoundé, Yaoundé, Cameroon.ORCID 0000-0002-3934-3233
Innocent M AliThe Biotechnology Centre, University of Yaoundé, Yaoundé, Cameroon.ORCID 0000-0002-1112-6376
Mike ChapondaDepartment of Clinical Sciences, Tropical Diseases Research Centre, Ndola, Zambia.
Cally RoperFaculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom.
Lucy C OkellMRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom.ORCID 0000-0001-7202-6873

Funding

Medical Research Council MR/X020258/1Wellcome Trust 107741/A/15/Z
6 · The paper itself

Abstract

backgroundRecently revised WHO guidelines on malaria chemoprevention have opened the door to more tailored implementation. Countries face choices on whether to replace old drugs, target additional age groups, and adapt delivery schedules according to local drug resistance levels and malaria transmission patterns. Regular routine assessment of protective efficacy of chemoprevention is key. Here, we apply a novel modelling approach to aid the design and analysis of chemoprevention trials and generate measures of protection that can be applied across a range of transmission settings. METHODS AND

findingsWe developed a model of genotype-specific drug protection, which accounts for underlying risk of infection and circulating genotypes. Using a Bayesian framework, we fitted the model to multiple simulated scenarios to explore variations in study design, setting, and participant characteristics. We find that a placebo or control group with no drug protection is valuable but not always feasible. An alternative approach is a single-arm trial with an extended follow-up (>42 days), which allows measurement of the underlying infection risk after drug protection wanes, as long as transmission is relatively constant. We show that the currently recommended 28-day follow-up in a single-arm trial results in low precision of estimated 30-day chemoprevention efficacy and low power in determining genotype differences of 12 days in the duration of protection (power = 1.4%). Extending follow-up to 42 days increased precision and power (71.5%) in settings with constant transmission over this time period. However, in settings of unstable transmission, protective efficacy in a single-arm trial was overestimated by 24.3% if recruitment occurred during increasing transmission and underestimated by 15.8% when recruitment occurred during declining transmission. Protective efficacy was estimated with greater precision in high transmission settings, and power to detect differences by resistance genotype was lower in scenarios where the resistant genotype was either rare or too common.

conclusionsThese findings have important implications for the current guidelines on chemoprevention efficacy studies and will be valuable for informing where these studies should be optimally placed. The results underscore the need for a comparator group in seasonal settings and provide evidence that the extension of follow-up in single-arm trials improves the accuracy of measures of protective efficacy in settings with more stable transmission. Extension of follow-up may pose logistical challenges to trial feasibility and associated costs. However, these studies may not need to be repeated multiple times, as the estimates of drug protection against different genotypes can be applied to different settings by adjusting for transmission intensity and frequency of resistance.

Indexed as

AntimalarialsChemopreventionDrug ResistanceMalariaBayes TheoremGenotypeHumansResearch DesignAntimalarials

Identifiers

PMID38723040
PMCPMC11081503

What OpenQuestion holds

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LicenceCC BY
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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.