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Impact of Xpert MTB/RIF in the Diagnosis of Childhood Tuberculosis in Rural Ethiopia.

Mario Pérez-ButragueñoJosé-Manuel Ramos-RincónAbraham TesfamariamBelén ComecheNurih MohammedGebre TizianoJacob EndiraysDejene BiruTamasghen ElalaAbu EdriLaura PrietoMiguel Górgolas
Published in: Journal of tropical pediatrics (2022)
Xpert MTB/RIF serves as an important adjunctive test for diagnosing childhood TB in rural settings, with microbiological confirmation in up to half the TB cases. Processes need to be optimized to achieve an early diagnosis. The diagnosis of childhood TB in high-burden countries such as Ethiopia still relies largely upon diagnostic algorithms and the clinician's skills.Lay summaryWorld Health Organization recommends the use of Xpert MTB/RIF to improve the microbiological diagnosis of childhood tuberculosis (TB) since 2014, but the impact of this test under real conditions in rural areas of low-income countries is not clear. We conducted a cross-sectional study in children evaluated for presumptive TB from 1 June 2016 to 31 May 2017 at the Gambo General Hospital in rural Southern Ethiopia. Children were evaluated according to a clinical protocol based on national guidelines and samples were submitted for Xpert MTB/RIF assay to the nearest reference laboratory.Of the 201 children assessed, 46.3% (93/201) were diagnosed with tuberculosis. Of these, 48.4% (45/93) were microbiologically confirmed by Xpert MTB/RIF [smear microscopy only diagnosed the 5.4% (5/93)]. Patients with microbiologically confirmed tuberculosis had a higher mean age, longer duration of fever and cough and had lymphadenopathy more frequently than those clinically diagnosed. A long delay in returning the results (median 15 days) was detected. Xpert MTB/RIF serves as an important test for diagnosing childhood TB in rural settings, with microbiological confirmation in up to half the cases. Processes need to be optimized to achieve an early diagnosis. The diagnosis of childhood TB in high-burden countries still relies largely upon diagnostic algorithms and the clinician's skills.
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