title: A comparison of tools used for tuberculosis diagnosis in resource-limited settings: a case study at Mubende referral hospital, Uganda.
abstract: 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).
  
  
  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.
  
  
  CONCLUSIONS
  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.
authors: Department of Food Safety and Infection Biology, Centre for Epidemiology and Biostatistics, Norwegian University of Life Sciences, Oslo, Norway; The Roslin Institute, College of Medicine and Veterinary Medicine, University of Edinburgh, Easter Bush, Midlothian, Edinburgh, United Kingdom.
authors: Adrian
authors: A
authors: Muwonge
authors: Institute of Economic and Social Research University of Zambia, Lusaka, Zambia.
authors: Sydney
authors: S
authors: Malama
authors: The Roslin Institute, College of Medicine and Veterinary Medicine, University of Edinburgh, Easter Bush, Midlothian, Edinburgh, United Kingdom.
authors: Barend M de C
authors: BM
authors: Bronsvoort
authors: College of Medicine, University of Arizona, Tucson, Arizona, United States of America.
authors: Demelash
authors: D
authors: Biffa
authors: Mycobacteriology Laboratory, Department of Medical Microbiology, Makerere University College of Health Sciences, Kampala, Uganda.
authors: Willy
authors: W
authors: Ssengooba
authors: Department of Food Safety and Infection Biology, Centre for Epidemiology and Biostatistics, Norwegian University of Life Sciences, Oslo, Norway.
authors: Eystein
authors: E
authors: Skjerve
mesh_terms: D000293:Adolescent; D000328:Adult; D000465:Algorithms; D003430:Cross-Sectional Studies; D005188:False Negative Reactions; D005260:Female; D006295:Health Resources / Q000600:supply & distribution*; D006761:Hospitals; D006801:Humans; D008297:Male; D008490:Medical Informatics / Q000379:methods*; D008853:Microscopy / Q000379:methods*; D012017:Referral and Consultation; D014376:Tuberculosis / Q000175:diagnosis* / Q000453:epidemiology / Q000473:pathology; D014454:Uganda / Q000453:epidemiology; D055815:Young Adult