The Economics of TB Screening model simulates costs and yields of two screening algorithms to identify the diagnostic pathway with superior impact/cost trade-off.

Objective of online model

The Economics of TB Screening allows the user to identify the diagnostic pathway with superior impact/cost trade-off.

Scenarios

Cost and yield calculations can be customised by the user for two algorithms.

Scenario 1 Scenario 2
dCXR/CAD as a rapid triage before Xpert® MTB/RIF testing All tested directly on Xpert® MTB/RIF

Assumptions and estimates are based on recent publications and market intelligence, others are default values for user customization.

In Scenario 1 dCXR/CAD is used to screen risk group members (symptomatic and asymptomatic) and to detect the presumptive TB cases on the basis of the CAD4TB abnormality score. It is assumed that only individuals having a CAD score above the abnormality threshold (e.g. >60 if this is the optimal sensitivity and specificity point on the CAD ROC curve) will be eligible for Xpert® MTB/RIF testing. Using more patient data inputs can further enhance the CAD performance with detailed individual risk scoring systems.

  • CXR/CAD presumptive TB rate calculation: TP + FP = prevalence * (sensitivity) + (1-prevalence) * (1-specifity).
  • Total number of TB cases detected calculation: total number of people screened per year * TB prevalence rate * sensitivity CAD * sensitivity Xpert® MTB/RIF.

Sensitivity and specificity values of CAD and Xpert® MTB/RIF can vary with the HIV prevalence rate (percentage of smear negative PTB) in the risk group, which is automatically reflected in the model along the below estimated values:

Assumptions accuracy for HIV prevalence variations Low 0-4% Medium 4-12% High >12%
Sensitivity / specificity CAD [1] 94% / 84% 90% / 75% 85% / 75%
Sensitivity / specificity Xpert® [2] 95% / 99% 90% / 99% 86% / 99%

In Scenario 2 no screening with dCXR/CAD is done so all risk group members are diagnosed using Xpert® MTB/RIF.


Input

Users can customize the below default variables before pressing [calculate] to reflect the TB program settings. Some values such as depreciation period, maintenance cost and transport cost are fixed values in the model.

Variables to customize

  • TB prevalence % in risk group
  • Actual % of cases notified in risk group
  • Working days per year
  • HIV Prevalence % in risk group (low, medium or high)
  • Digital X-ray cost (stationary system vs. portable)
  • CAD cost per screen (cost depends on volume of the bundle)
  • Annual Salaries Clinician (X-ray) X-ray / Xpert® Technician
  • Throughput: people screened per day

Output

As output the model provides the user with the following information for both algorithms.

Economics

  • Total screening cost (per year)
  • Screening cost (per person screened)
  • Screening cost (per case notified)
  • Cases notified (per € 100,000 budget)

Yield

  • Future % of cases notified
  • Future number of cases notified (per year)
  • Net increase of cases notified
  • Number Needed to Screen