Evidence map›Paper›PMID 39126857›Full record

ArticleEpidemics2024

Improving the contribution of mathematical modelling evidence to guidelines and policy: Experiences from tuberculosis.

C Finn McQuaid, Nicolas A Menzies, Rein M G J Houben, Gabriella B Gomez, Anna Vassall, Nimalan Arinaminpathy, Peter J Dodd, Richard G White

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Epidemics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

C Finn McQuaidTB Modelling Group, TB Centre and Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK. Electronic address: finn.mcquaid@lshtm.ac.uk.
Nicolas A MenziesDepartment of Global Health and Population, Boston, MA, USA; Center for Health Decision Science, Harvard T H Chan School of Public Health, Boston, MA, USA.
Rein M G J HoubenTB Modelling Group, TB Centre and Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK.
Gabriella B GomezInternational AIDS Vaccine Initiative, Amsterdam, Netherlands.
Anna VassallGlobal Health Economics Centre, London School of Hygiene and Tropical Medicine, London, UK.
Nimalan ArinaminpathyMRC Centre for Global Infectious Disease Analysis, Imperial College, London, UK.
Peter J DoddSchool of Health and Related Research, University of Sheffield, Sheffield, UK.
Richard G WhiteTB Modelling Group, TB Centre and Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK.

Funding

Medical Research Council MR/R015600/1
6 · The paper itself

Abstract

We read with great interest the recent paper by Lo et al., who argue that there is an urgent need to ensure the quality of modelling evidence used to support international and national guideline development. Here we outline efforts by the Tuberculosis Modelling and Analysis Consortium, together with the World Health Organization Global Task Force on Tuberculosis Impact Measurement, to develop material to improve the quality and transparency of country-level tuberculosis modelling to inform decision-making.

Indexed as

Health PolicyModels, TheoreticalTuberculosisWorld Health OrganizationDecision MakingEvidence-Based MedicineGlobal HealthHumansPractice Guidelines as TopicGuidanceInfectious disease modellingQuality and transparencyTuberculosis

Identifiers

PMID39126857
PMCPMC11413515

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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.