title: Optimizing tuberculosis case detection through a novel diagnostic device placement model: the case of Uganda.
abstract: BACKGROUND
  Xpert MTB/RIF (Xpert) is being widely adopted in high TB burden countries. Analysis is needed to guide the placement of devices within health systems to optimize the tuberculosis (TB) case detection rate (CDR).
  
  
  METHODS
  We used epidemiological and operational data from Uganda (139 sites serving 87,600 individuals tested for TB) to perform a model-based comparison of the following placement strategies for Xpert devices: 1) Health center level (sites ranked by size from national referral hospitals to health care level III centers), 2) Smear volume (sites ranked from highest to lowest volume of smear microscopy testing), 3) Antiretroviral therapy (ART) volume (sites ranked from greatest to least patients on ART), 4) External equality assessment (EQA) performance (sites ranked from worst to best smear microscopy performance) and 5) TB prevalence (sites ranked from highest to lowest). We compared two clinical algorithms, one where Xpert was used only for smear microscopy negative samples versus another replacing smear microscopy. The primary outcome was TB CDR; secondary outcomes were detection of multi-drug resistant TB, number of sites requiring device placement to achieve specified rollout coverage, and cost.
  
  
  RESULTS
  Placement strategies that prioritized sites with higher TB prevalence maximized CDR, with an incremental rate of 6.2-12.6% compared to status quo (microscopy alone). Diagnosis of MDR-TB was greatest in the TB Prevalence strategy when Xpert was used in place of smear microscopy. While initial implementation costs were lowest in the Smear Volume strategy, cost per additional TB case detected was lowest in the TB prevalence strategy.
  
  
  CONCLUSIONS
  In Uganda, placement of Xpert devices in sites with high TB prevalence yielded the highest TB CDR at the lowest cost per additional case diagnosed. These results represent novel use of program level data to inform the optimal placement of new technology in resource-constrained settings.
authors.affiliation: Department of Medicine, Sections of Hospital Medicine and Infectious Diseases & Global Health, University of Chicago, Chicago, United States of America.
authors.forename: Mai T
authors.initials: MT
authors.lastname: Pho
authors.affiliation: Indian School of Business, Hyderabad, India.
authors.forename: Sarang
authors.initials: S
authors.lastname: Deo
authors.affiliation: Kellogg School of Management, Northwestern University, Evanston, United States of America.
authors.forename: Kara M
authors.initials: KM
authors.lastname: Palamountain
authors.affiliation: Department of Medical Microbiology, Makerere University College of Health Sciences, Kampala, Uganda.
authors.forename: Moses Lutaakome
authors.initials: ML
authors.lastname: Joloba
authors.affiliation: Department of Community Health, Mbarara University of Science and Technology, Mbarara, Uganda.
authors.forename: Francis
authors.initials: F
authors.lastname: Bajunirwe
authors.affiliation: Department of Medicine, Makerere University College of Health Sciences, Kampala, Uganda.
authors.forename: Achilles
authors.initials: A
authors.lastname: Katamba
mesh_terms: D060085:Coinfection / Q000175:diagnosis* / Q000453:epidemiology; D003362:Cost-Benefit Analysis; D019719:Diagnostic Equipment / Q000191:economics; D015658:HIV Infections / Q000175:diagnosis* / Q000453:epidemiology; D006268:Health Facilities; D006801:Humans; D020380:Needs Assessment; D015995:Prevalence; D014397:Tuberculosis, Pulmonary / Q000175:diagnosis* / Q000453:epidemiology; D014454:Uganda / Q000453:epidemiology
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