Evidence map›Paper›PMID 32353315›Full record

ArticleThe Lancet. Global health2020

Risk-benefit analysis of tuberculosis infection testing for household contact management in high-burden countries: a mathematical modelling study.

Courtney M Yuen, James A Seddon, Salmaan Keshavjee, Peter J Dodd

Open access · goldAbstract read
In one paragraph

Article in The Lancet. Global health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 pooled it
0.3field-weighted citation impact, top 30% of its field
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

10 citing papers in PubMed, 1 synthesis or guideline pooled it, 16 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Testing and Scaling Interventions to Improve the Tuberculosis Infection Care Cascade.Journal of the Pediatric Infectious Diseases Society · 2022
    Review
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors at 4 institutions in 3 countries.

Courtney M YuenDivision of Global Health Equity, Brigham and Women's Hospital, Boston, MA, USA; Department of Global Health and Social Medicine, Harvard Medical School, Boston, MA, USA. Electronic address: courtney_yuen@hms.harvard.edu.
James A SeddonDepartment of Infectious Diseases, Imperial College London, London, UK; Desmond Tutu TB Centre, Department of Paediatrics and Child Health, Stellenbosch University, Cape Town, South Africa.
Salmaan KeshavjeeDivision of Global Health Equity, Brigham and Women's Hospital, Boston, MA, USA; Department of Global Health and Social Medicine, Harvard Medical School, Boston, MA, USA.
Peter J DoddSchool of Health and Related Research, University of Sheffield, Sheffield, UK.
Brigham and Women's Hospital · USHarvard Global Health Institute · USImperial College London · GBUniversity of Sheffield · GB

Funding

New approaches to optimizing the application and measuring the impact of community-based tuberculosis interventionsDP2MD015102 · NIMHD · BRIGHAM AND WOMEN'S HOSPITAL · PI YUEN, COURTNEY M. · 2019 to 2019
$2.4M
Medical Research Council MR/P022081/1Medical Research Council MR/R007942/1NIMHD NIH HHS DP2 MD015102
6 · The paper itself

Abstract

backgroundPreventive therapy for tuberculosis reduces the risk of disease in people who have been infected but who are not sick. Countries with a high burden of tuberculosis that are expanding preventive therapy use must decide how tuberculosis infection testing should be used for risk stratification among household contacts of patients with tuberculosis.

methodsWe modelled the risks of tuberculosis disease and severe adverse events, comparing the following two preventive therapy strategies: preventive therapy for all household contacts, or preventive therapy for only household contacts with a positive tuberculin skin test (TST) result. We used data from clinical trials and literature on tuberculosis natural history to model outcomes, assuming different preventive therapy regimens, ages, and TST positivity prevalence.

findingsAssuming 25% prevalence of TST positivity among 1000 household contacts aged 0-17 years, a treat-all approach with isoniazid and rifapentine compared with a treat-TST-only approach led to 13 fewer incident tuberculosis cases (IQR -5 to -18) and four additional severe adverse events (2 to 6). With rifampicin, the difference was 11 fewer incident tuberculosis cases (-3 to -17) and two additional severe adverse events (1 to 3). For adults, a treat-all approach led to fewer incident tuberculosis cases, and additional adverse events increased with age. Assuming 25% prevalence of TST positivity among adult contacts, a treat-all approach would lead to around two fewer tuberculosis cases per 1000 contacts for all regimens; the number of additional severe adverse events ranged from seven (IQR 5 to 8) for 18 to 34-year-olds treated with rifampicin to 63 (50 to 74) for people older than 64 years treated with isoniazid and rifapentine. A rifampicin-only regimen was associated with the fewest additional severe adverse events (seven [IQR 5 to 8] per 1000 adults aged 18-34 years and 35-64 years, and 17 [9 to 23] per 1000 adults older than 64 years).

interpretationBased on the available data, giving preventive therapy to all household contacts would probably reduce the incidence of tuberculosis cases in high-burden settings. Adverse events could be minimised by using non-isoniazid regimens and, in adults older than 18 years, focusing treatment on individuals with a positive infection test.

fundingBill & Melinda Gates Foundation, UK Medical Research Council, and UK Department for International Development.

Indexed as

Tuberculin TestAdolescentAdultAgedChildChild, PreschoolContact TracingFamily CharacteristicsGlobal HealthHumansInfantInfant, NewbornMiddle AgedModels, TheoreticalRisk AssessmentTuberculosis

Identifiers

PMID32353315
PMCPMC7196883
OpenAlexW3019135930

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.