title: The impact of new tuberculosis diagnostics on transmission: why context matters.
abstract: OBJECTIVE
  To estimate the impact of new tuberculosis diagnostics on tuberculosis transmission given the complex contextual factors that can lead to patient loss before diagnosis or treatment.
  
  
  METHODS
  An epidemic model of tuberculosis specifying discrete steps along the tuberculosis diagnostic pathway was constructed. The model was calibrated to the epidemiology of tuberculosis and human immunodeficiency virus (HIV) infection in the United Republic of Tanzania and was used to assess the impact of a new diagnostic tool with 70% sensitivity for smear-negative pulmonary tuberculosis. The influence of contextual factors on the projected epidemic impact of the new diagnostic tool over the decade following introduction was explored.
  
  
  RESULTS
  With the use of smear microscopy, the incidence of tuberculosis will decline by an average of 3.94% per year. If the new tool is added, incidence will decline by an annual 4.25%. This represents an absolute change of 0.31 percentage points (95% confidence interval: 0.04-0.42). However, the annual decline in transmission with use of the new tool is less when existing strategies for the diagnosis of smear-negative cases have high sensitivity and when symptomatic individuals delay in seeking care. Other influential contextual factors include access to tuberculosis care, patient loss before diagnosis, initial patient default after diagnosis and treatment success rate.
  
  
  CONCLUSIONS
  When implementing and scaling up the use of a new diagnostic tool, the operational context in which diagnosis and treatment take place needs to be considered.
authors.affiliation: Institute of Epidemiology and Preventive Medicine, National Taiwan University, 17 Xuzhou Road, 100 Taipei, Taiwan, China.
authors.forename: Hsien-Ho
authors.initials: HH
authors.lastname: Lin
authors.forename: David
authors.initials: D
authors.lastname: Dowdy
authors.forename: Christopher
authors.initials: C
authors.lastname: Dye
authors.forename: Megan
authors.initials: M
authors.lastname: Murray
authors.forename: Ted
authors.initials: T
authors.lastname: Cohen
mesh_terms: D015373:Bacterial Typing Techniques / Q000379:methods* / Q000639:trends; D001499:Bayes Theorem; D015658:HIV Infections / Q000175:diagnosis* / Q000453:epidemiology / Q000382:microbiology; D006801:Humans; D015994:Incidence; D008853:Microscopy / Q000379:methods / Q000639:trends; D008954:Models, Biological; D012680:Sensitivity and Specificity; D013183:Sputum / Q000382:microbiology; D013636:Tanzania / Q000453:epidemiology; D014376:Tuberculosis / Q000175:diagnosis* / Q000453:epidemiology / Q000382:microbiology / Q000517:prevention & control