Evidence map›Paper›PMID 29791449›Full record

ArticlePloS one2018

Impact of involvement of non-formal health providers on TB case notification among migrant slum-dwelling populations in Odisha, India.

Ambarish Dutta, Sarthak Pattanaik, Rajendra Choudhury, Pritish Nanda, Suvanand Sahu, Rajendra Panigrahi, Bijaya K Padhi, Krushna Chandra Sahoo, P R Mishra, Pinaki Panigrahi and 2 more

Abstract read
In one paragraph

Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 1 pooled it
–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

19 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Review
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Slums, Space, and State of Health-A Link between Settlement Morphology and Health Data.International journal of environmental research and public health · 2020
    Article
  17. Article
  18. Article
  19. Determination of Direct and Indirect Costs Incurred by Tuberculosis Patients During Diagnosis and Treatment in Urban Areas of South Gujarat: A Mixed Method Approach.Indian journal of community medicine : official publication of Indian Association of Preventive & Social Medicine
    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

12 authors.

Ambarish DuttaAsian Institute of Public Health, Bhubaneswar, India.ORCID 0000-0002-4019-7472
Sarthak PattanaikAsian Institute of Public Health, Bhubaneswar, India.
Rajendra ChoudhuryAsian Institute of Public Health, Bhubaneswar, India.
Pritish NandaAsian Institute of Public Health, Bhubaneswar, India.
Suvanand SahuStop TB Partnership, Geneva, Switzerland.
Rajendra PanigrahiWHO-RNTCP Technical Assistance Network, Odisha, India.
Bijaya K PadhiAsian Institute of Public Health, Bhubaneswar, India.
Krushna Chandra SahooAsian Institute of Public Health, Bhubaneswar, India.
P R MishraAsian Institute of Public Health, Bhubaneswar, India.
Pinaki PanigrahiUniversity of Nebraska Medical Centre, Omaha, Nebraska, United States of America.
Daisy LekharuIndependent consultant, Geneva, Switzerland.
Robert H StevensIndependent consultant, Geneva, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMigrant labourers living in the slums of urban and industrial patches across India make up a key sub-population so far controlling Tuberculosis (TB) in the country is concerned. This is because many TB patients from these communities- remain under reached by the Revised National Tuberculosis Control Programme (RNTCP) of India. This marginalized community usually seeks early-stage healthcare from "friendly neighbourhood" non-formal health providers (NFHPs). Because, RNTCP has limited capacity to involve the NFHPs, an implementation research project was conceived, whereby an external partner would engage with the NFHPs to enable them to identify early TB symptomatics from this key sub-population who would be then tested using Xpert MTB/RIF technology. Diagnosed TB cases among them would be referred promptly to RNTCP for treatment. This paper aimed to describe the project and its impact.

methodsAdopting a quasi-experimental before-after design, four RNTCP units from two major urban-industrial areas of Odisha were selected for intervention, which spanned five quarters and covered 151,400 people, of which 30% were slum-dwelling migrants. Two similar units comprised the control population. The hypothesis was, reaching the under reached in the intervention area through NFHPs would increase TB notification from these traditionally under-notifying units. RNTCP notification data during intervention was compared with pre-intervention era, adjusted for contemporaneous changes in control population.

resultsThe project detected 488 Xpert+ TB cases, of whom 466 were administered RNTCP treatment. This translated into notification of additional 198 new bacteriologically positive cases to RNTCP, a 30% notification surge, after adjustment for 2% decline in control. This meant an average quarterly increase in notification of 41.20(20.08, 62.31; p<0.001) cases. The increase was immediate, evident from the rise in level in the time series analysis by 50.42(10.28, 90.55; p = 0.02) cases.

conclusionEngagement with NFHPs contributed to an increase in TB notification to RNTCP from key under reached, slum-dwelling migrant populations.

Indexed as

Disease NotificationHealth PersonnelHumansIndiaResidence CharacteristicsTransients and MigrantsTuberculosis

Identifiers

PMID29791449
PMCPMC5965830

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.