Integrated treatment-decision algorithms for childhood TB: modelling diagnostic performance and costs
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Background
To improve childhood tuberculosis (TB) diagnosis, treatment-decision algorithms (TDAs) with and without chest X-ray (CXR) were developed for children under age 10. We aimed to model diagnostic performance and costs of implementing TDAs in primary healthcare (PHC) and district hospital (DH) settings in Uganda.
Methods
We developed decision-tree models following the TDA pathway from evaluation to treatment-decision. We compared six scenarios with combinations of diagnostic testing (stool and respiratory Xpert, urine lipoarabinomannan, and/or CXR) at PHCs and DHs. Outcomes were diagnostic accuracy and cost per correct treatment-decision for a cohort of 10,000 children with presumptive TB using a Monte Carlo simulation from a health system perspective. Costs were reported in 2024 International dollars.
Results
In all scenarios, TDA’s had high sensitivity (80.8–91.9%) but low specificity (51.2-60.9%). Total diagnostic and treatment costs for the cohort were I$1,768,958–2,458,790; largely driven by overtreatment of false-positive cases. Diagnostic costs were mostly offset by reducing overtreatment. The cost per treatment-decision was lowest using mobile CXR at PHC (I$287.40) and highest with DH referral (I$445.84).
Conclusion
The TDAs have high sensitivity and can be implemented at PHCs with lower costs than DHs. Improving specificity and reducing treatment costs would enable affordable, large-scale implementation.
