Evidence map›Paper›PMID 29899882›Full record

ArticleInternational journal of preventive medicine2018

The Use of Laypersons to Support Tuberculosis Screening at a Kenyan Referral Hospital.

Barbara Kabai Burmen, Joseph Mogunde, Timothy Malika

Open access · goldAbstract read
In one paragraph

Article in International journal of preventive medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.1field-weighted citation impact, top 49% 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

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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

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. Article
4 · The record

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5 · Who and what money

Authors and funding

3 authors at 1 institution in 1 country.

Barbara Kabai BurmenCenter for Global Health Research, Kenya Medical Research Institute, Kisumu, Kenya.
Joseph MogundeKenya Medical Research Institute, Center for Global Health Research, Kisumu Kenya.
Timothy MalikaDivision of Leprosy Tuberculosis and Lung Diseases, The former Nyanza province of Kenya.
Kenya Medical Research Institute · KE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe former Nyanza Province of Kenya bore the brunt of HIV-driven tuberculosis (TB); 62% of the 19,152 cases in 2010 were HIV co-infected. The use of laypersons to improve TB case finding in community settings has shown rewarding results in other countries. We have no documented Kenyan experience in health facility settings. We evaluated the benefit of using laypersons to support TB screening and referrals at the former Nyanza Province of kenya province's largest regional referral facility.

methodsIn 2010, five high school graduates were trained on symptomatic recognition of TB suspects and assisted sputum production by the region's District's TB and Leprosy Coordinator. They then identified and referred TB suspects (from hospital patients and visitors) at waiting-areas and wards to clinicians and documented their TB screening and referral outcomes. We describe results from one waiting-area with complete documentation between January and December 2011.

resultsOf the 217 TB suspects identified, majority were male (55%); their median age was 36 (range 1-70) years. 11% (23) were aged <15 years; 65% (15) were diagnosed with TB by, a combination of sputum microscopy and chest X-rays (5) followed by chest X-ray alone (50), then sputum microscopy alone (1), and TB score chart (4). Of those aged 15+ years, 72% (140) were diagnosed with TB by a combination of sputum microscopy and chest X-rays (75) followed by sputum microscopy alone (38), and chest X-ray alone (27). Excluding cases that transferred out, this process contributed to 33% of Jaramogi Oginga Odinga Teaching and Referral Hospital's annual TB case burden.

conclusionsTB case detection in high TB burden regions can be supported the use of laypersons in hospital settings.

Indexed as

Cough monitorsreferralstuberculosis identification

Identifiers

PMID29899882
PMCPMC5981661
OpenAlexW2900148577

What OpenQuestion holds

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LicenceCC BY-NC-SA
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Registered trials

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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.