Evidence map›Paper›PMID 29460737›Full record

ArticleEmerging infectious diseases2018

Use of Verbal Autopsy to Determine Underlying Cause of Death during Treatment of Multidrug-Resistant Tuberculosis, India.

Poonam Ramesh Naik, Patrick K Moonan, Abhay Subhashrao Nirgude, Hemant Deepak Shewade, Srinath Satyanarayana, Pracheth Raghuveer, Malik Parmar, Chinnappareddy Ravichandra, Anil Singarajipura

Abstract read
In one paragraph

Article in Emerging infectious diseases, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

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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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Long-term follow-up of persons diagnosed with multidrug-resistant TB in Chennai, India, 2013-2020.The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease · 2024
    Article
  4. Article
  5. Causes of Death among People Affected by Tuberculosis: A Cross-Sectional Study from Trivandrum, Kerala, India.Indian journal of community medicine : official publication of Indian Association of Preventive & Social Medicine
    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

9 authors.

Poonam Ramesh Naik
Patrick K Moonan
Abhay Subhashrao Nirgude
Hemant Deepak Shewade
Srinath Satyanarayana
Pracheth Raghuveer
Malik Parmar
Chinnappareddy Ravichandra
Anil Singarajipura

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Of patients with multidrug-resistant tuberculosis (MDR TB), <50% complete treatment. Most treatment failures for patients with MDR TB are due to death during TB treatment. We sought to determine the proportion of deaths during MDR TB treatment attributable to TB itself. We used a structured verbal autopsy tool to interview family members of patients who died during MDR TB treatment in India during January-December 2016. A committee triangulated information from verbal autopsy, death certificate, or other medical records available with the family members to ascertain the underlying cause of death. For 66% of patient deaths (47/71), TB was the underlying cause of death. We assigned TB as the underlying cause of death for an additional 6 patients who died of suicide and 2 of pulmonary embolism. Deaths during TB treatment signify program failure; accurately determining the cause of death is the first step to designing appropriate, timely interventions to prevent premature deaths.

Indexed as

AdolescentAdultAgedAnti-Bacterial AgentsAutopsyCause of DeathCross-Sectional StudiesDrug Resistance, Multiple, BacterialFemaleGeographyHumansIndiaMaleMiddle AgedMycobacterium tuberculosisTuberculosis, Multidrug-ResistantAnti-Bacterial AgentsbacteriadeathsIndiaMDR TBMultidrug-resistant tuberculosistuberculosis and other mycobacteriaunderlying cause of deathverbal autopsy

Identifiers

PMID29460737
PMCPMC5823351

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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.