Evidence map›Paper›PMID 35646807›Full record

ArticleFrontiers in public health2022

"DOST" Model to Link and Support Drug Resistant TB Patients From Private Sector: An Experience From Delhi, India.

Vindhya Vatsyayan, Theresa Pattery, Khasim Sayyad, Jason Williams, Arnab Pal, Vikas Panibatla, Ashwani Khanna

Open access · goldAbstract read
In one paragraph

Article in Frontiers in public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
0.5field-weighted citation impact, top 37% 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, 1 synthesis or guideline pooled it, 5 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. 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

7 authors at 1 institution in 1 country.

Vindhya VatsyayanClinton Health Access Initiative (CHAI), New Delhi, India.
Theresa PatteryJohnson & Johnson Global Public Health R&D, Beerse, Belgium.
Khasim SayyadTB Alert India, Hyderabad, India.
Jason WilliamsJohnson & Johnson Global Public Health R&D, Beerse, Belgium.
Arnab PalClinton Health Access Initiative (CHAI), New Delhi, India.
Vikas PanibatlaTB Alert India, Hyderabad, India.
Ashwani KhannaState TB Office, New Delhi, India.
New Delhi Tuberculosis Center · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The National TB Elimination Programme (NTEP) has quite successfully involved private sector for referral of presumptive drug resistant TB (DR-TB) patients for molecular testing and referral for DR-TB management. There was a challenge as all the referred patients were not reaching to the facilities. A "DOST" intervention model was implemented to strengthen the patient care pathway. We conducted this study to describe the patient care cascade, the clinico-demographic characteristics of patients linked to the treatment and to estimate the mean turn-around time for drug resistant TB care services. Methods: It is a cross-sectional study conducted at New Delhi during the period July 2019-December 2020 under programmatic settings. Results: A total of 9,331 patients were subjected to CB-NAAT test and 382 (4%) were found to be resistant for rifampicin and 231 (76%) were initiated on treatment in the public sector under NTEP. Conclusion: The DOST intervention model developed to link the DR-TB patients from private sector to the public sector DR-TB centers is found to be efficient and effective.

Indexed as

Private SectorTuberculosis, Multidrug-ResistantCross-Sectional StudiesHumansIndiaPublic SectorDelhiDOST modeldrug resistant tuberculosisIndialinkagesprivate sector

Identifiers

PMID35646807
PMCPMC9131023
OpenAlexW4280498638

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.