Choosing algorithms for TB screening: a modelling study to compare yield, predictive value and diagnostic burden
Abstract
Background
To inform the choice of
an appropriate screening and diagnostic algorithm for
tuberculosis (TB) screening initiatives in different
epidemiological settings, we compare algorithms composed of
currently available methods.
Methods
Of twelve algorithms composed of screening for
symptoms (prolonged cough or any TB symptom) and/or chest
radiography abnormalities, and either sputum-smear microscopy
(SSM) or Xpert MTB/RIF (XP) as confirmatory test we model
algorithm outcomes and summarize the yield, number needed to
screen (NNS) and positive predictive value (PPV) for different
levels of TB prevalence.
Results
Screening for prolonged cough has low yield, 22% if
confirmatory testing is by SSM and 32% if XP, and a high NNS,
exceeding 1000 if TB prevalence is ≤0.5%. Due to low
specificity the PPV of screening for any TB symptom followed by
SSM is less than 50%, even if TB prevalence is 2%. CXR screening
for TB abnormalities followed by XP has the highest case
detection (87%) and lowest NNS, but is resource intensive. CXR
as a second screen for symptom screen positives improves
efficiency.
Conclusions
The ideal algorithm does not exist. The choice will
be setting specific, for which this study provides guidance.
Generally an algorithm composed of CXR screening followed by
confirmatory testing with XP can achieve the lowest NNS and
highest PPV, and is the least amenable to setting-specific
variation. However resource requirements for tests and equipment
may be prohibitive in some settings and a reason to opt for
symptom screening and SSM. To better inform disease control
programs we need empirical data to confirm the modeled yield,
cost-effectiveness studies, transmission models and a better
screening test.
Source:
BMC Infectious Diseases
