Evidence map›Paper›PMID 31720394›Full record

ArticleJournal of clinical tuberculosis and other mycobacterial diseases2018

Molecular clustering of patients with

Josephine W Mburu, Leonard Kingwara, Magiri Esther, Nyerere Andrew

Erratum issuedAbstract read
In one paragraph

Article in Journal of clinical tuberculosis and other mycobacterial diseases, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

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

1 citing paper in PubMed.

  1. Whole-Genome Sequencing ofMicroorganisms · 2023
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Josephine W MburuNational Tuberculosis Reference Laboratory, MOH, Kenya.
Leonard KingwaraNational Tuberculosis Reference Laboratory, MOH, Kenya.
Magiri EstherJomo Kenyatta University of Agriculture and Technology (JKUAT), Kenya.
Nyerere AndrewJomo Kenyatta University of Agriculture and Technology (JKUAT), Kenya.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSocial determinants of health, biological, and individual variants have been associated with Pulmonary TB (PTB) case clustering. None of the studies have focused on diabetes mellitus (DM) despite it being one of the co-morbidity affecting TB patients. Minimal data is available and it is not clear whether patients with DM and TB are more likely than TB patients without DM to be grouped into similar molecular clusters thus indicating a bias in transmission among TB/DM co-morbidity patients.

objectiveTo determine proportion of TB strains within TB and TB/DM cases that were clustered with their corresponding clinical outcomes and hence could be attributable to active TB transmission in the two urban counties of Nairobi, Kenya.

methodsWe carried out a prospective cohort study of non-pregnant patients aged 15 years and above that tested positive for TB in two peri‑urban counties in Kenya between February 2014 and August 2015. Clinical and socio-demographic data were obtained from a questionnaire and medical records of the National TB program patient data base at two, three, five and six months. Spoligotyping data was then obtained and compared from previously identified strains in a data bank from the spolDB4.

resultsWe identified 7 different TB strains out of which East Asia Beijing, Euro America and Indo oceanic being the most dominant strain within the two counties accounting for 92.4% of the infections. DM was not a significant factor in increasing the likelihood of PTB patients to cluster according to the genotype of the infecting

conclusionDiabetes mellitus is not a significant factor in increasing the molecular clustering among PTB patients.

Indexed as

DiabetesMolecular clusteringTuberculosis

Identifiers

PMID31720394
PMCPMC6830120

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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.