Optimizing Tuberculosis Case Detection through a Novel Diagnostic Device Placement Model: The Case of Uganda
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Date
2015
Journal Title
Journal ISSN
Volume Title
Publisher
PLoS ONE
Abstract
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.
Conclusion
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 resourceconstrained
settings.
Description
Keywords
Tuberculosis, Detection, Novel Diagnostic Device Placement Model
Citation
Pho MT, Deo S, Palamountain KM, Joloba ML, Bajunirwe F, Katamba A (2015) Optimizing Tuberculosis Case Detection through a Novel Diagnostic Device Placement Model: The Case of Uganda. PLoS ONE 10(4): e0122574. doi:10.1371/ journal.pone.0122574