Treatment decision algorithms for tuberculosis screening and diagnosis in children below 5 years hospitalised with severe acute malnutrition: a cost-effectiveness analysis

Abstract Background Children with severe acute malnutrition (SAM) are an important risk group for underdiagnosis and death from tuberculosis. In 2022, the World Health Organization (WHO) recommended use of treatment decision algorithms (TDAs) for tuberculosis diagnosis in children. There is currently no cost-effectiveness evidence for TDA-based approaches compared to routine practice. Methods The TB-Speed SAM study developed i) a one-step TDA including Xpert, clinical, radiological and echography features, and ii) a two-step TDA, which also included a screening phase, for children under 5 years hospitalised with SAM at tertiary hospitals in Uganda and Zambia. We assessed the diagnostic accuracy and cost-effectiveness of deploying TB-Speed and WHO TDA-based approaches compared to the standard of care (SOC). Estimated outcomes included children started on tuberculosis treatment, false positive rates, disability-adjusted life years (DALYs) and incremental cost-effectiveness ratios (ICERs). Findings Per 100 children hospitalised with SAM, averaging 19 children with tuberculosis, the one-step TDA initiated 17 true positive children (95% uncertainty intervals [UI]: 12-23) on tuberculosis treatment, the two-step TDA 15 (95%UI: 10-22), the WHO TDA 14 (95%UI: 9-19), and SOC 4 (95%UI: 2-9). The WHO TDA generated the most false positives (35, 95%UI: 24-46), followed by the one-step TDA (18, 95%UI: 6-29), the two-step TDA (14, 95%UI: 1-25), and SOC (11, 95%UI: 3-17). All TDA-based approaches had ICERs below plausible country cost-effectiveness thresholds compared to SOC (one-step: $44-51/DALY averted, two-step: $34-39/DALY averted, WHO: $40-46/DALY averted). Interpretation Our findings show that these TDA-based approaches are highly cost-effective for the vulnerable group of children hospitalised with SAM, compared to current practice. Funding Unitaid Research in context Evidence before this study In 2022, the WHO conditionally recommended the use of treatment decision algorithms (TDAs) for tuberculosis diagnosis in children aged <10 with presumptive pulmonary tuberculosis. Two TDAs were suggested for use in settings with (TDA A) and without (TDA B) access to chest X-ray. These WHO-suggested TDAs propose a single approach to TB diagnosis in all children. The TB Speed SAM study developed specific algorithms for children <5 years hospitalised with severe acute malnutrition. Aiming to identify studies assessing the cost-effectiveness of using TDAs for childhood TB, we searched the PubMed database using (“Decision Support Systems, Clinical”[MeSH] OR “clinical decision support” OR “decision support” OR “clinical decision-making”) AND (“Algorithms”[MeSH] OR “algorithm” OR “decision-making” OR “decision model” OR “treatment decision algorithm”) AND (“Tuberculosis”[MeSH] OR “tuberculosis” OR “TB”) AND (“Costs and Cost Analysis”[MeSH] OR “cost-effectiveness” OR “cost analysis” OR “costs”) between 1st January 2004 and 18th October 2024, without language restrictions. Of 31 articles identified, 2 articles reported on the cost-effectiveness of interventions aiming to improve clinical decision making for tuberculosis diagnosis. Other articles were excluded because they were not an economic evaluation, not on tuberculosis, or only compared microbiological testing approaches related to tuberculosis care (microscopic observation drug susceptibility test versus Xpert MTB/RIF test, QuantiFERON-TB Gold In-Tube versus tuberculin skin test for tuberculosis diagnosis). Debes et al. assessed the cost-effectiveness of tuberculosis diagnosis using microscopic observation drug susceptibility, Xpert MTB/RIF and empiric treatment for all patients, in addition to current clinical diagnostic practices in Ugandan children. Van’t Hoog et al. explored combinations of sensitivity, specificity and cost at which a hypothetical triage test would improve affordability of the Xpert assay. We found no economic evaluations of a treatment decision algorithm (TDA)-based approach (screening, testing, treatment) for tuberculosis diagnosis. Added value of this study This is the first study to assess the cost-effectiveness of using treatment decision algorithms in childhood tuberculosis diagnosis. It focuses on children <5 years hospitalised with severe acute malnutrition using the TB-Speed SAM one-step TDA and two-step TDA, which includes a screening step before the diagnostic step, and the WHO-suggested TDA A. We also evaluated the accuracy of the WHO-suggested TDA for paediatric tuberculosis in this patient group. This study found that for children hospitalised with SAM all three TDA-based approaches for paediatric tuberculosis diagnosis were cost-effective compared to the standard of care from a health systems perspective in Uganda and Zambia, including in lower tuberculosis prevalence settings. The TB-Speed two-step and WHO TDAs had lower costs than the TB-Speed one-step because their screening step resulted in fewer assessments overall, but also a smaller health impact due to a slightly lower sensitivity. The WHO TDA was less effective and more costly than the TB-Speed two-step TDA and involved substantial rates of overtreatment. The TB-Speed one-step TDA had the greatest health impact while remaining cost-effective, making it the preferred option. Implications of all the available evidence The WHO has conditionally recommended incorporating TDAs, pending validation, into existing case detection strategies to support the decentralisation of clinical tools and improve the identification of tuberculosis in children. Our findings show that TDA-based approaches are cost-effective for the vulnerable group of children hospitalised with SAM, compared to current practices, and our sensitivity analysis suggests that these results are robust. While not developed in children hospitalised with SAM, the WHO-suggested TDA for paediatric tuberculosis performs well in this patient group. This analysis contributes valuable evidence to support the interim WHO recommendation on decentralised models of care.