Evidence map›Paper›PMID 32584968›Full record

ArticleClinical infectious diseases : an official publication of the Infectious Diseases Society of America2021

Model-based Cost-effectiveness of State-level Latent Tuberculosis Interventions in California, Florida, New York, and Texas.

Youngji Jo, Sourya Shrestha, Isabella Gomes, Suzanne Marks, Andrew Hill, Garrett Asay, David Dowdy

Open access · greenAbstract read
In one paragraph

Article in Clinical infectious diseases : an official publication of the Infectious Diseases Society of America, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed, 3 pooled it
0.6field-weighted citation impact, top 20% 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

24 citing papers in PubMed, 3 syntheses or guidelines pooled it, 38 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Article
  5. Review
  6. Article
  7. Article
  8. Article
  9. Article
  10. Review
  11. Cost-effectiveness of expanded latent TB infection testing and treatment: Lynn City, Massachusetts, USA.The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease · 2024
    Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Review
  17. Article
  18. Article
  19. Article
  20. Identifying Priorities for Testing and Treatment of Latent Tuberculosis Infection in the United States.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2021
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors at 2 institutions in 1 country.

Youngji JoDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.ORCID 0000-0002-1440-2786
Sourya ShresthaDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.
Isabella GomesDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.
Suzanne MarksDivision of Tuberculosis Elimination, Centers for Disease Control and Prevention, Atlanta, Georgia, USA.
Andrew HillDivision of Tuberculosis Elimination, Centers for Disease Control and Prevention, Atlanta, Georgia, USA.
Garrett AsayDivision of Tuberculosis Elimination, Centers for Disease Control and Prevention, Atlanta, Georgia, USA.
David DowdyDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.
Johns Hopkins University · USCenters for Disease Control and Prevention · US

Funding

ENHANCING MODELS OF HIV, VIRAL HEPATITIS, STIs, AND TUBERCULOSIS TO INFORM AND IMPROVE PUBLIC HEALTH IMPACTU38PS004646 · PS · EMORY UNIVERSITY · PI SULLIVAN, PATRICK · 2014 to 2015
$2.9M
CDC HHSIntramural CDC HHS CC999999National Center for HIV/AIDS, Viral Hepatitis, STDNCHHSTP CDC HHS U38 PS004646
6 · The paper itself

Abstract

backgroundTargeted testing and treatment (TTT) for latent tuberculosis (TB) infection (LTBI) is a recommended strategy to accelerate TB reductions and further TB elimination in the United States. Evidence on cost-effectiveness of TTT for key populations can help advance this goal.

methodsWe used a model of TB transmission to estimate the numbers of individuals who could be tested by interferon-γ release assay and treated for LTBI with 3 months of self-administered rifapentine and isoniazid (3HP) under various TTT scenarios. Specifically, we considered rapidly scaling up TTT among people who are non-US-born, diabetic, living with human immunodeficiency virus (HIV), homeless or incarcerated in California, Florida, New York, and Texas-states where more than half of US TB cases occur. We projected costs (from the healthcare system perspective, in 2018 dollars), 30-year reductions in TB incidence, and incremental cost-effectiveness (cost per quality-adjusted life-year [QALY] gained) for TTT in each modeled population.

resultsThe projected cost-effectiveness of TTT differed substantially by state and population, while the health impact (number of TB cases averted) was consistently greatest among non-US-born individuals. TTT was most cost-effective among persons with HIV (from $2828/QALY gained in Florida to $11 265/QALY gained in New York) and least cost-effective among people with diabetes (from $223 041/QALY gained in California to $817 753/QALY in New York).

conclusionsThe modeled cost-effectiveness of TTT for LTBI varies across states but was consistently greatest among people with HIV; moderate among people who are non-US-born, incarcerated, or homeless; and least cost-effective among people with diabetes.

Indexed as

Latent TuberculosisCaliforniaCost-Benefit AnalysisFloridaHumansNew YorkTexasUnited Statescost effectivenesslatent tuberculosistargeting TB preventive therapyUnited States

Identifiers

PMID32584968
PMCPMC8077726
OpenAlexW3037401649

What OpenQuestion holds

Textmetadata
LicenceTDM
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.