Evidence map›Paper›PMID 33134905›Full record

ArticleEClinicalMedicine2020

The potential impact of the COVID-19 pandemic on the tuberculosis epidemic a modelling analysis.

Lucia Cilloni, Han Fu, Juan F Vesga, David Dowdy, Carel Pretorius, Sevim Ahmedov, Sreenivas A Nair, Andrei Mosneaga, Enos Masini, Suvanand Sahu and 1 more

Open access · goldAbstract read
In one paragraph

Article in EClinicalMedicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 201 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
201citing papers in PubMed, 5 pooled it
6.5field-weighted citation impact, top 2% 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

201 citing papers in PubMed, 5 syntheses or guidelines pooled it, 334 citations in OpenAlex.

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  17. Effects of the COVID-19 Pandemic on Tuberculosis Outcomes in the United States: A Bayesian Analysis.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2026
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141 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

11 authors at 4 institutions in 2 countries.

Lucia CilloniMRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, United Kingdom.
Han FuMRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, United Kingdom.
Juan F VesgaMRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, United Kingdom.
David DowdyDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Carel PretoriusAvenir Health, Glastonbury, Connecticut, USA.
Sevim AhmedovBureau for Global Health, Infectious Diseases Division, United States Agency for International Development, Washington, DC, USA.
Sreenivas A NairStop TB Partnership, Geneva, Switzerland.
Andrei MosneagaStop TB Partnership, Geneva, Switzerland.
Enos MasiniStop TB Partnership, Geneva, Switzerland.
Suvanand SahuStop TB Partnership, Geneva, Switzerland.
Nimalan ArinaminpathyStop TB Partnership, Geneva, Switzerland.
Imperial College London · GBAvenir Health · USJohns Hopkins University · USUnited States Agency for International Development · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundRoutine services for tuberculosis (TB) are being disrupted by stringent lockdowns against the novel SARS-CoV-2 virus. We sought to estimate the potential long-term epidemiological impact of such disruptions on TB burden in high-burden countries, and how this negative impact could be mitigated.

methodsWe adapted mathematical models of TB transmission in three high-burden countries (India, Kenya and Ukraine) to incorporate lockdown-associated disruptions in the TB care cascade. The anticipated level of disruption reflected consensus from a rapid expert consultation. We modelled the impact of these disruptions on TB incidence and mortality over the next five years, and also considered potential interventions to curtail this impact.

findingsEven temporary disruptions can cause long-term increases in TB incidence and mortality. If lockdown-related disruptions cause a temporary 50% reduction in TB transmission, we estimated that a 3-month suspension of TB services, followed by 10 months to restore to normal, would cause, over the next 5 years, an additional 1⋅19 million TB cases (Crl 1⋅06-1⋅33) and 361,000 TB deaths (CrI 333-394 thousand) in India, 24,700 (16,100-44,700) TB cases and 12,500 deaths (8.8-17.8 thousand) in Kenya, and 4,350 (826-6,540) cases and 1,340 deaths (815-1,980) in Ukraine. The principal driver of these adverse impacts is the accumulation of undetected TB during a lockdown. We demonstrate how long term increases in TB burden could be averted in the short term through supplementary "catch-up" TB case detection and treatment, once restrictions are eased.

interpretationLockdown-related disruptions can cause long-lasting increases in TB burden, but these negative effects can be mitigated with rapid restoration of TB services, and targeted interventions that are implemented as soon as restrictions are lifted.

fundingUSAID and Stop TB Partnership.

Indexed as

Covid-19EpidemiologyMathematical modellingabstractTuberculosis

Identifiers

PMID33134905
PMCPMC7584493
OpenAlexW3091178517

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.