A Comparison of Tools Used for Tuberculosis Diagnosis in Resource-Limited Settings: A Case Study at Mubende Referral Hospital, Uganda
Author(s) -
Adrian Muwonge,
Sydney Malama,
Mark Bronsvoort,
Demelash Biffa,
Willy Ssengooba,
Eystein Skjerve
Publication year - 2014
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0100720
Subject(s) - medicine , tuberculosis , referral , receiver operating characteristic , mycobacterium tuberculosis , logistic regression , algorithm , cross sectional study , pathology , family medicine , computer science
Background This study compared TB diagnostic tools and estimated levels of misdiagnosis in a resource-limited setting. Furthermore, we estimated the diagnostic utility of three-TB-associated predictors in an algorithm with and without Direct Ziehl-Neelsen (DZM). Materials and Methods Data was obtained from a cross-sectional study in 2011 conducted at Mubende regional referral hospital in Uganda. An individual was included if they presented with a two weeks persistent cough and or lymphadenitis/abscess. 344 samples were analyzed on DZM in Mubende and compared to duplicates analyzed on direct fluorescent microscopy (DFM), growth on solid and liquid media at Makerere University. Clinical variables from a questionnaire and DZM were used to predict TB status in multivariable logistic and Cox proportional hazard models, while optimization and visualization was done with receiver operating characteristics curve and algorithm-charts in Stata, R and Lucid-Charts respectively. Results DZM had a sensitivity and specificity of 36.4% (95% CI = 24.9–49.1) and 97.1%(95% CI = 94.4–98.7) compared to DFM which had a sensitivity and specificity of 80.3%(95% CI = 68.7–89.1) and 97.1%(95% CI = 94.4–98.7) respectively. DZM false negative results were associated with patient’s HIV status, tobacco smoking and extra-pulmonary tuberculosis. One of the false negative cases was infected with multi drug resistant TB (MDR). The three-predictor screening algorithm with and without DZM classified 50% and 33% of the true cases respectively, while the adjusted algorithm with DZM classified 78% of the true cases. Conclusion The study supports the concern that using DZM alone risks missing majority of TB cases, in this case we found nearly 60%, of who one was an MDR case. Although adopting DFM would reduce this proportion to 19%, the use of a three-predictor screening algorithm together with DZM was almost as good as DFM alone. It’s utility is whoever subject to HIV screening all TB suspects.
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