Evidence map›Paper›PMID 24608234›Full record

ArticlePloS one2014

Disease control implications of India's changing multi-drug resistant tuberculosis epidemic.

Sze-Chuan Suen, Eran Bendavid, Jeremy D Goldhaber-Fiebert

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 2 pooled it
2.2field-weighted citation impact, top 13% 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

19 citing papers in PubMed, 2 syntheses or guidelines pooled it, 41 citations in OpenAlex.

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  16. First Line Anti-TB Drug Resistance in an Urban Area of Odisha, India.Journal of clinical and diagnostic research : JCDR · 2016
    Article
  17. Article
  18. Cost-effectiveness of improvements in diagnosis and treatment accessibility for tuberculosis control in India.The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease · 2015
    Article
  19. Observational
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

3 authors at 1 institution in 1 country.

Sze-Chuan SuenDepartment of Management Science and Engineering, Stanford University Stanford, Stanford, California, United States of America.
Eran BendavidDivision of General Medical Disciplines, Department of Medicine, Stanford University Stanford, Stanford, California, United States of America; Center for Health Policy and the Center for Primary Care and Outcomes Research, Stanford University, Stanford, California, United States of America.
Jeremy D Goldhaber-FiebertCenter for Health Policy and the Center for Primary Care and Outcomes Research, Stanford University, Stanford, California, United States of America.
Stanford University · US

Funding

Making Better Decisions: Policy Modeling for AIDS and Drug AbuseR01DA015612 · NIDA · STANFORD UNIVERSITY · PI OWENS, DOUGLAS K · 2002 to 2017
$9.4M
CORE--PROGRAM DEVELOPMENTP30AG017253 · NIA · STANFORD UNIVERSITY · PI BHATTACHARYA, JAY · 1999 to 2019
$5.8M
Improving the Public Health Impact of HIV Testing in Highly Endemic RegionsK01AI084582 · NIAID · STANFORD UNIVERSITY · PI BENDAVID, ERAN · 2009 to 2013
$652k
Cost-effective chronic disease prevention in developing countriesK01AG037593 · NIA · STANFORD UNIVERSITY · PI GOLDHABER-FIEBERT, JEREMY DAVID · 2011 to 2015
$637k
NIAID NIH HHS K01 AI084582NIAID NIH HHS K01-AI084582NIA NIH HHS K01 AG037593NIA NIH HHS K01 AG037593-01A1NIA NIH HHS P30 AG017253NIDA NIH HHS R01 DA015612
6 · The paper itself

Abstract

backgroundMulti-drug resistant tuberculosis (MDR TB) is a major health challenge in India that is gaining increasing public attention, but the implications of India's evolving MDR TB epidemic are poorly understood. As India's MDR TB epidemic is transitioning from a treatment-generated to transmission-generated epidemic, we sought to evaluate the potential effectiveness of the following two disease control strategies on reducing the prevalence of MDR TB: a) improving treatment of non-MDR TB; b) shortening the infectious period between the activation of MDR TB and initiation of effective MDR treatment. METHODS AND

findingsWe developed a dynamic transmission microsimulation model of TB in India. The model followed individuals by age, sex, TB status, drug resistance status, and treatment status and was calibrated to Indian demographic and epidemiologic TB time trends. The main effectiveness measure was reduction in the average prevalence reduction of MDR TB over the ten years after control strategy implementation. We find that improving non-MDR cure rates to avoid generating new MDR cases will provide substantial non-MDR TB benefits but will become less effective in reducing MDR TB prevalence over time because more cases will occur from direct transmission--by 2015, the model estimates 42% of new MDR cases are transmission-generated and this proportion continues to rise over time, assuming equal transmissibility of MDR and drug-susceptible TB. Strategies that disrupt MDR transmission by shortening the time between MDR activation and treatment are projected to provide greater reductions in MDR prevalence compared with improving non-MDR treatment quality: implementing MDR diagnostic improvements in 2017 is expected to reduce MDR prevalence by 39%, compared with 11% reduction from improving non-MDR treatment quality.

conclusionsAs transmission-generated MDR TB becomes a larger driver of the MDR TB epidemic in India, rapid and accurate MDR TB diagnosis and treatment will become increasingly effective in reducing MDR TB cases compared to non-MDR TB treatment improvements.

Indexed as

AdolescentAdultAgedAntitubercular AgentsChildChild, PreschoolFemaleHumansIndiaMaleMiddle AgedTuberculosis, Multidrug-ResistantYoung AdultAntitubercular Agents

Identifiers

PMID24608234
PMCPMC3946521
OpenAlexW1988932941

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.