Evidence map›Paper›PMID 31053179›Full record

ArticleThe international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease2019

Improving the quality of modelling evidence used for tuberculosis policy evaluation.

N A Menzies, C F McQuaid, G B Gomez, A Siroka, P Glaziou, K Floyd, R G White, R M G J Houben

Abstract read
In one paragraph

Article in The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

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

  1. Pooled it
  2. Review
  3. Review
  4. Article
  5. Article
  6. An approach for improving the quality of country-level TB modelling.The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease · 2021
    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

8 authors.

N A MenziesDepartment of Global Health and Population , Center for Health Decision Science, Harvard T H Chan School of Public Health, Boston, Massachusetts, USA.
C F McQuaidDepartment of Infectious Disease Epidemiology, TB Modelling Group, TB Centre and Centre for Mathematical Modelling of Infectious Diseases.
G B GomezDepartment of Global Health and Development, London School of Hygiene & Tropical Medicine, London, UK.
A SirokaGlobal TB Programme, World Health Organization, Geneva, Switzerland.
P GlaziouGlobal TB Programme, World Health Organization, Geneva, Switzerland.
K FloydGlobal TB Programme, World Health Organization, Geneva, Switzerland.
R G WhiteDepartment of Infectious Disease Epidemiology, TB Modelling Group, TB Centre and Centre for Mathematical Modelling of Infectious Diseases.
R M G J HoubenDepartment of Infectious Disease Epidemiology, TB Modelling Group, TB Centre and Centre for Mathematical Modelling of Infectious Diseases.

Funding

Medical Research Council MR/P002404/1World Health Organization 001
6 · The paper itself

Abstract

Mathematical modelling is commonly used to evaluate policy options for tuberculosis (TB) control in high-burden countries. Although major policy and funding decisions are made based on these analyses, there is concern about the variability of results produced using modelled policy analyses. We discuss new guidance for country-level TB policy modelling. The guidance was developed by the TB Modelling and Analysis Consortium in collaboration with the World Health Organization Global TB Programme, with input from a range of TB stakeholders (funders, modelling groups, country TB programme staff and subject matter experts). The guidance describes principles for country-level TB modelling, as well as good practices for operationalising the principles. The principles cover technical concerns such as model design, parameterisation and validation, as well as approaches for incorporating modelling into country-led policy making and budgeting. For modellers, this guidance suggests approaches to improve the quality and relevance of modelling undertaken to support country-level planning. For non-modellers, this guidance describes considerations for engaging modelling technical assistance, contributing to a modelling exercise and reviewing the results of modelled analyses. If routinely adopted, this guidance should improve the reliability, transparency and usefulness of modelling for country-level TB policy making. However, this guidance will not address all challenges facing modelling, and ongoing work is needed to improve the empirical evidence base for TB policy evaluation and develop stronger mechanisms for validating models. Increasing country ownership of the modelling process remains a challenge, requiring sustained engagement and capacity building.

Indexed as

Health PolicyModels, TheoreticalCapacity BuildingDecision MakingHumansPolicy MakingReproducibility of ResultsTuberculosis

Identifiers

PMID31053179
PMCPMC6490058

What OpenQuestion holds

Textmetadata
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.