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Tuberculosis, more commonly referred to as TB, is an infectious disease stemming from Mycobacterium tuberculosis. While it primarily impacts the lungs, TB can also affect other body areas. Given its severity and global impact, timely and accurate diagnosis is crucial for controlling its spread and improving patient outcomes.
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Choosing algorithms for TB screening: a modelling study to compare yield, predictive value and diagnostic burden.

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Choosing the best tuberculosis (TB) screening algorithm depends on the setting. Chest X-ray (CXR) followed by Xpert MTB/RIF (XP) offers high detection but is resource-intensive, while symptom screening with sputum-smear microscopy (SSM) is less effective but more accessible.

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Area of Science:

  • Public Health
  • Infectious Disease Epidemiology
  • Diagnostic Algorithm Development

Background:

  • Tuberculosis (TB) screening requires effective and context-appropriate algorithms.
  • Current methods for TB screening and diagnosis vary in yield and resource needs.

Purpose of the Study:

  • To compare different screening and diagnostic algorithms for tuberculosis (TB).
  • To inform the selection of optimal TB screening strategies across diverse epidemiological settings.

Main Methods:

  • Modeled twelve algorithms combining symptom screening (prolonged cough, any TB symptom) and/or chest radiography (CXR) abnormalities.
  • Evaluated algorithms using sputum-smear microscopy (SSM) or Xpert MTB/RIF (XP) as confirmatory tests.
  • Summarized algorithm outcomes including yield, number needed to screen (NNS), and positive predictive value (PPV) at varying TB prevalence levels.

Main Results:

  • Screening for prolonged cough showed low yield and high NNS, especially at low TB prevalence.
  • Symptom screening followed by SSM had low positive predictive value (<50%) even at 2% TB prevalence.
  • CXR screening followed by XP demonstrated the highest case detection (87%) and lowest NNS but is resource-intensive.
  • Using CXR as a secondary screen for symptom-positive individuals improved efficiency.

Conclusions:

  • No single ideal TB screening algorithm exists; choices are setting-specific.
  • CXR followed by XP generally offers the lowest NNS and highest PPV.
  • Resource limitations may necessitate symptom screening with SSM in some settings.
  • Further empirical data, cost-effectiveness, and transmission modeling are needed to refine TB control strategies.