Evidence map›Paper›PMID 25164493›Full record

ArticleBMC research notes2014

Prevalence of national treatment algorithm defined smear positive pulmonary tuberculosis in HIV positive patients in Brazzaville, Republic of Congo.

Laure Stella Ghoma Linguissi, Pembe Issamou Mayengue, Anissa Sidibé, Jeannhey C Vouvoungui, Mitawa Missontsa, Igor Kevin Madzou-Laboum, Gaston Bango Essassa, Sunny Oyakhirome, Matthias Frank, Veronique Penlap and 1 more

Open access · goldAbstract read
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Article in BMC research notes, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.1field-weighted citation impact, top 24% of its field
1 · What the graph read from it

What it found

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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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3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 14 citations in OpenAlex.

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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

11 authors at 5 institutions in 3 countries.

Laure Stella Ghoma Linguissi
Pembe Issamou Mayengue
Anissa Sidibé
Jeannhey C Vouvoungui
Mitawa Missontsa
Igor Kevin Madzou-Laboum
Gaston Bango Essassa
Sunny Oyakhirome
Matthias Frank
Veronique Penlap
Francine NtoumiFondation Congolaise pour la Recherche Médicale, Brazzaville, Republic of Congo. fntoumi@fcrm-congo.com.
Marien Ngouabi University · CGCentre Hospitalier et Universitaire de Brazzaville · CGFondation Congolaise Pour La Recherche Médicale · CGUniversité de Yaoundé I · CMUniversity of Tübingen · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn the Republic in Congo, the national algorithm for the diagnosis of pulmonary tuberculosis (TB) relies on Ziehl-Neelsen (ZN) sputum smear microscopy, chest X-ray radiography (CXR) and clinical symptoms. Microscopy positive pulmonary TB (MPT+) is defined as symptoms of TB and a positive ZN smear. Microscopy negative pulmonary TB (MPT-) is defined as symptoms of TB, a negative ZN smear but CXR changes consistent with TB. The present cross-sectional study was designed to determine the prevalence of positive and negative MPT individuals among HIV positive and HIV negative individuals presenting to an ambulatory TB treatment center (CTA) in Brazzaville.

methodsAll study participants underwent a physical examination, chest radiography and three ZN sputum smear examinations and HIV testing. Viral load and CD4 counts were determined for HIV positive individuals.

results775 individuals presented with symptoms of TB. 425 individuals accepted the voluntary HIV test. 133 (31.3%) were HIV positive (HIV+) and 292 (68.7%) were HIV negative (HIV-). Of the 292 HIV- individuals 167 (57%) were classified as positive MPT and 125 (43%) as negative MPT. Of the 133 HIV positive individuals 39 (29%) were classified as MPT + and 94 (71%) as MPT-.

conclusionOur study shows that the prevalence of positive MPT individuals is lower among HIV positive individuals compared to HIV negative individuals in agreement to reports from other countries. The data suggest that a substantial number of HIV positive pulmonary TB cases are not detected by the national algorithm and highlight the need for new diagnostic tests in this population.

Indexed as

AlgorithmsAdultAIDS-Related Opportunistic InfectionsCD4-CD8 RatioCD4 Lymphocyte CountDemocratic Republic of the CongoFemaleHumansMalePrevalenceTuberculosis, PulmonaryViral Load

Identifiers

PMID25164493
PMCPMC4167258
OpenAlexW2037515843

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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.