Evidence map›Paper›PMID 37649031›Full record

ArticleBMC medicine2023

Tabby2: a user-friendly web tool for forecasting state-level TB outcomes in the United States.

Nicole A Swartwood, Christian Testa, Ted Cohen, Suzanne M Marks, Andrew N Hill, Garrett Beeler Asay, Jennifer Cochran, Kevin Cranston, Liisa M Randall, Andrew Tibbs and 3 more

Open access · goldAbstract read
In one paragraph

Article in BMC medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.8field-weighted citation impact, top 29% 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

3 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
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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

13 authors at 7 institutions in 1 country.

Nicole A Swartwood *Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, MA, 02120, USA. nswartwood@hsph.harvard.edu.ORCID http://orcid.org/0000-0003-4116-9243
Christian Testa *Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Ted CohenDepartment of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA.
Suzanne M MarksDivision of Tuberculosis Elimination, National Center for HIV, Viral Hepatitis, STD, and TB Prevention, Centers for Disease Control and Prevention, Atlanta, GA, USA.
Andrew N HillDivision of Tuberculosis Elimination, National Center for HIV, Viral Hepatitis, STD, and TB Prevention, Centers for Disease Control and Prevention, Atlanta, GA, USA.
Garrett Beeler AsayDivision of Tuberculosis Elimination, National Center for HIV, Viral Hepatitis, STD, and TB Prevention, Centers for Disease Control and Prevention, Atlanta, GA, USA.
Jennifer CochranBureau of Infectious Disease and Laboratory Sciences, Massachusetts Department of Public Health, Boston, MA, USA.
Kevin CranstonBureau of Infectious Disease and Laboratory Sciences, Massachusetts Department of Public Health, Boston, MA, USA.
Liisa M RandallBureau of Infectious Disease and Laboratory Sciences, Massachusetts Department of Public Health, Boston, MA, USA.
Andrew TibbsBureau of Infectious Disease and Laboratory Sciences, Massachusetts Department of Public Health, Boston, MA, USA.
C Robert HorsburghDepartments of Epidemiology, Biostatistics, Global Health and Medicine, Boston University Schools of Public Health and Medicine, Boston, MA, USA.
Joshua A SalomonCenter for Health Policy / Center for Primary Care and Outcomes Research, Stanford University, Stanford, USA.
Nicolas A MenziesDepartment of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, MA, 02120, USA.
Massachusetts Department of Public Health · USCenters for Disease Control and Prevention · USHarvard Global Health Institute · USBoston University · USHarvard University · USStanford University · USYale University · US

Funding

Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
NCATS NIH HHS UL1 TR001863
6 · The paper itself

Abstract

backgroundIn the United States, the tuberculosis (TB) disease burden and associated factors vary substantially across states. While public health agencies must choose how to deploy resources to combat TB and latent tuberculosis infection (LTBI), state-level modeling analyses to inform policy decisions have not been widely available.

methodsWe developed a mathematical model of TB epidemiology linked to a web-based user interface - Tabby2. The model is calibrated to epidemiological and demographic data for the United States, each U.S. state, and the District of Columbia. Users can simulate pre-defined scenarios describing approaches to TB prevention and treatment or create their own intervention scenarios. Location-specific results for epidemiological outcomes, service utilization, costs, and cost-effectiveness are reported as downloadable tables and customizable visualizations. To demonstrate the tool's functionality, we projected trends in TB outcomes without additional intervention for all 50 states and the District of Columbia. We further undertook a case study of expanded treatment of LTBI among non-U.S.-born individuals in Massachusetts, covering 10% of the target population annually over 2025-2029.

resultsBetween 2022 and 2050, TB incidence rates were projected to decline in all states and the District of Columbia. Incidence projections for the year 2050 ranged from 0.03 to 3.8 cases (median 0.95) per 100,000 persons. By 2050, we project that majority (> 50%) of TB will be diagnosed among non-U.S.-born persons in 46 states and the District of Columbia; per state percentages range from 17.4% to 96.7% (median 83.0%). In Massachusetts, expanded testing and treatment for LTBI in this population was projected to reduce cumulative TB cases between 2025 and 2050 by 6.3% and TB-related deaths by 8.4%, relative to base case projections. This intervention had an incremental cost-effectiveness ratio of $180,951 (2020 USD) per quality-adjusted life year gained from the societal perspective.

conclusionsTabby2 allows users to estimate the costs, impact, and cost-effectiveness of different TB prevention approaches for multiple geographic areas in the United States. Expanded testing and treatment for LTBI could accelerate declines in TB incidence in the United States, as demonstrated in the Massachusetts case study.

Indexed as

Latent TuberculosisTuberculosisAntibiotic ProphylaxisCost of IllnessFemaleHumansParturitionPregnancyUnited StatesEpidemiologyInfectious diseaseMathematical modelingTuberculosisWeb application

Identifiers

PMID37649031
PMCPMC10469407
OpenAlexW4386287071

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.