Evidence map›Paper›PMID 34235130›Full record

ArticleFrontiers in public health2021

Logically Inferred Tuberculosis Transmission (LITT): A Data Integration Algorithm to Rank Potential Source Cases.

Kathryn Winglee, Clinton J McDaniel, Lauren Linde, Steve Kammerer, Martin Cilnis, Kala M Raz, Wendy Noboa, Jillian Knorr, Lauren Cowan, Sue Reynolds and 6 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. 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. Mutation ofFrontiers in public health · 2021
    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

16 authors.

Kathryn WingleeDivision of Tuberculosis Elimination, Centers for Disease Control and Prevention, Atlanta, GA, United States.
Clinton J McDanielDivision of Tuberculosis Elimination, Centers for Disease Control and Prevention, Atlanta, GA, United States.
Lauren LindeTB Control Branch, California Department of Public Health, Richmond, CA, United States.
Steve KammererDivision of Tuberculosis Elimination, Centers for Disease Control and Prevention, Atlanta, GA, United States.
Martin CilnisTB Control Branch, California Department of Public Health, Richmond, CA, United States.
Kala M RazDivision of Tuberculosis Elimination, Centers for Disease Control and Prevention, Atlanta, GA, United States.
Wendy NoboaDivision of Tuberculosis Elimination, Centers for Disease Control and Prevention, Atlanta, GA, United States.
Jillian KnorrNew York City Department of Health and Mental Hygiene, Queens, NY, United States.
Lauren CowanDivision of Tuberculosis Elimination, Centers for Disease Control and Prevention, Atlanta, GA, United States.
Sue ReynoldsDivision of Tuberculosis Elimination, Centers for Disease Control and Prevention, Atlanta, GA, United States.
James PoseyDivision of Tuberculosis Elimination, Centers for Disease Control and Prevention, Atlanta, GA, United States.
Jeanne Sullivan MeissnerNew York City Department of Health and Mental Hygiene, Queens, NY, United States.
Shameer PoonjaDivision of Tuberculosis Elimination, Centers for Disease Control and Prevention, Atlanta, GA, United States.
Tambi ShawTB Control Branch, California Department of Public Health, Richmond, CA, United States.
Sarah TalaricoDivision of Tuberculosis Elimination, Centers for Disease Control and Prevention, Atlanta, GA, United States.
Benjamin J SilkDivision of Tuberculosis Elimination, Centers for Disease Control and Prevention, Atlanta, GA, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Understanding tuberculosis (TB) transmission chains can help public health staff target their resources to prevent further transmission, but currently there are few tools to automate this process. We have developed the Logically Inferred Tuberculosis Transmission (LITT) algorithm to systematize the integration and analysis of whole-genome sequencing, clinical, and epidemiological data. Based on the work typically performed by hand during a cluster investigation, LITT identifies and ranks potential source cases for each case in a TB cluster. We evaluated LITT using a diverse dataset of 534 cases in 56 clusters (size range: 2-69 cases), which were investigated locally in three different U.S. jurisdictions. Investigators and LITT agreed on the most likely source case for 145 (80%) of 181 cases. By reviewing discrepancies, we found that many of the remaining differences resulted from errors in the dataset used for the LITT algorithm. In addition, we developed a graphical user interface, user's manual, and training resources to improve LITT accessibility for frontline staff. While LITT cannot replace thorough field investigation, the algorithm can help investigators systematically analyze and interpret complex data over the course of a TB cluster investigation.

Indexed as

Mycobacterium tuberculosisTuberculosisAlgorithmsHumansMolecular EpidemiologyWhole Genome Sequencingcluster investigationgenomic epidemiologytransmissiontuberculosiswhole-genome sequencing

Identifiers

PMID34235130
PMCPMC8255782

What OpenQuestion holds

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Registered trials

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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.