Evidence map›Paper›PMID 30226154›Full record

Trial reportEmerging infectious diseases2018

Tuberculosis Treatment Monitoring by Video Directly Observed Therapy in 5 Health Districts, California, USA.

Richard S Garfein, Lin Liu, Jazmine Cuevas-Mota, Kelly Collins, Fatima Muñoz, Donald G Catanzaro, Kathleen Moser, Julie Higashi, Teeb Al-Samarrai, Paula Kriner and 6 more

Open access · diamondAbstract readClinical TrialMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Trial report 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 55 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
55citing papers in PubMed, 6 pooled it
5.2field-weighted citation impact, top 3% 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

55 citing papers in PubMed, 6 syntheses or guidelines pooled it, 95 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. eHealth in TB clinical management.The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease · 2022
    Pooled it
  6. Treatment of Drug-Resistant Tuberculosis. An Official ATS/CDC/ERS/IDSA Clinical Practice Guideline.American journal of respiratory and critical care medicine · 2019
    Guideline
  7. Trial
  8. Trial
  9. Challenges associated with electronic and in-person directly observed therapy during a randomized trial.The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease · 2023
    Trial
  10. Trial
  11. Review
  12. Article
  13. Article
  14. Review
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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

16 authors at 6 institutions in 1 country.

Richard S Garfein
Lin Liu
Jazmine Cuevas-Mota
Kelly Collins
Fatima Muñoz
Donald G Catanzaro
Kathleen Moser
Julie Higashi
Teeb Al-Samarrai
Paula Kriner
Julie Vaishampayan
Javier Cepeda
Michelle A Bulterys
Natasha K Martin
Phillip Rios
Fredric Raab
University of California, San Diego · USHealth and Human Services Agency · USSan Francisco Department of Public Health · USSan Joaquin County Public Health Services · USSanta Clara County Behavioral Health Services · USUniversity of Arkansas at Fayetteville · US

Funding

VirologyP30AI036214 · NIAID · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI SUSAN JANET LITTLE · 1994 to 2026
$78.4M
University of California Launching Future Leaders in Global Health Research Training ProgramD43TW009343 · FIC · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI CRAIG R COHEN, Sung-Jae Lee · 2017 to 2026
$14.7M
Modeling Structural HIV Determinants in Substance Users and related populationsR01DA037773 · NIDA · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI MARTIN, NATASHA, STRATHDEE, STEFFANIE A. · 2015 to 2017
$1.4M
MODELING THE IMPACT OF A POLICE EDUCATION PROGRAM ON HIV INCIDENCE AMONG PEOPLE WHO INJECT DRUGSK01DA043421 · NIDA · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI CEPEDA, JAVIER · 2017 to 2021
$941k
FIC NIH HHS D43 TW009343NIAID NIH HHS P30 AI036214NIDA NIH HHS K01 DA043421NIDA NIH HHS R01 DA037773
6 · The paper itself

Abstract

We assessed video directly observed therapy (VDOT) for monitoring tuberculosis treatment in 5 health districts in California, USA, to compare adherence between 174 patients using VDOT and 159 patients using in-person directly observed therapy (DOT). Multivariable linear regression analyses identified participant-reported sociodemographics, risk behaviors, and treatment experience associated with adherence. Median participant age was 44 (range 18-87) years; 61% of participants were male. Median fraction of expected doses observed (FEDO) among VDOT participants was higher (93.0% [interquartile range (IQR) 83.4%-97.1%]) than among patients receiving DOT (66.4% [IQR 55.1%-89.3%]). Most participants (96%) would recommend VDOT to others; 90% preferred VDOT over DOT. Lower FEDO was independently associated with US or Mexico birth, shorter VDOT duration, finding VDOT difficult, frequently taking medications while away from home, and having video-recording problems (p<0.05). VDOT cost 32% (range 6%-46%) less than DOT. VDOT was feasible, acceptable, and achieved high adherence at lower cost than DOT.

Indexed as

Directly Observed TherapyAdolescentAdultAgedAged, 80 and overAntitubercular AgentsCaliforniaCosts and Cost AnalysisFemaleHumansMaleMedication AdherenceMiddle AgedTuberculosisVideo RecordingYoung AdultAntitubercular Agentsantimicrobial resistancebacteriaCaliforniamedication adherence monitoringmHealthpatient-centered caresmartphonetuberculosis and other mycobacteriaUnited StatesVDOTvideo directly observed therapyvideo technology

Identifiers

PMID30226154
PMCPMC6154139
OpenAlexW2889020658

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.