A cost-effectiveness analysis of novel stool processing methods for diagnosis of tuberculosis in children under 5 years of age in Uganda

A cost-effectiveness analysis of novel stool processing methods for diagnosis of tuberculosis in children under 5 years of age in Uganda | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A cost-effectiveness analysis of novel stool processing methods for diagnosis of tuberculosis in children under 5 years of age in Uganda Mary Gaeddert, Devan Jaganath, Hoa Nguyen, Abdulkadir Civan, Pamela Nabeta, and 12 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6278387/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Oct, 2025 Read the published version in BMC Health Services Research → Version 1 posted 13 You are reading this latest preprint version Abstract Background: Stool-based molecular assays for childhood tuberculosis (TB) diagnosis have shown promise as an alternative to respiratory sample testing. While implementation is underway, evidence on cost-effectiveness is needed. Therefore, we aimed to evaluate the costs of stool testing with Xpert Ultra and model the cost-effectiveness of implementation scenarios at lower levels of care. Methods: We measured costs for three new stool processing methods and modeled implementation using the least costly method. For children under 5 years with presumptive TB at primary health clinics or district hospitals in Uganda, clinical diagnosis with treatment-decision algorithms was compared to stool testing at primary clinics, stool testing at primary clinics with referral to district hospitals if negative, or evaluation only at district hospitals with Xpert Ultra testing on respiratory samples. Using decision-tree models, we calculated the cost in international dollars (I$) per life-years saved (LYS) and the incremental cost-effectiveness ratio (ICER) assessed against the country-specific willingness to pay threshold. One-way and probabilistic sensitivity analyses were conducted. Results: The Simple One-Step (SOS) was the least costly stool processing method. Compared to diagnosis with only treatment-decision algorithms, the ICER of SOS/Ultra at primary clinics was I$1041.71/LYS, SOS/Ultra with referral was I$874.82/LYS, while the district hospital strategy was dominated. Sensitivity analyses showed stool testing was cost-effective compared to only clinical diagnosis if TB prevalence at primary clinics was above 5.7%, with higher diagnostic accuracy of stool-based testing, or lower testing costs. Conclusions: For young children, stool testing at primary clinics, with or without referral to district hospitals, lowered costs in relation to lives saved compared to implementing at district hospitals alone or only clinical diagnosis using the treatment-decision algorithms. Tuberculosis pediatric diagnostics cost-effectiveness stool Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background Of the estimated 1.3 million children with tuberculosis (TB) each year, about half are not reported to public health programs ( 1 ). This gap is in large part due to missed diagnoses that cause delays in treatment and increase morbidity and mortality. Young children present unique diagnostic challenges because they often show non-specific symptoms and cannot expectorate sputum for microbiologic testing ( 2 , 3 ). To address these barriers, non-sputum-based diagnostics, which can be implemented at or near the point of care at peripheral health facilities, are urgently needed. Stool has been a promising sample type for molecular TB testing, and the World Health Organization (WHO) has endorsed its use ( 4 ). To facilitate implementation in peripheral facilities, three centrifuge-free stool processing methods have been developed for use with Xpert Ultra MTB/RIF (Cepheid, USA): the Stool Processing Kit (SPK, FIND), Simple One-Step (SOS, KNCV), and Optimized Sucrose Flotation (OSF, TB Speed). Although each of these methods involves different levels of complexity and materials, a multi-center study has demonstrated that these methods perform similarly and are acceptable and usable for laboratory staff ( 5 ). However, there are several ongoing questions that limit implementation of stool Xpert Ultra. First, the costs of each processing method have not been compared. Second, although stool testing could be integrated into the WHO treatment decision algorithms (TDAs) for childhood TB in primary health centers (PHCs) ( 6 ), their cost-effectiveness compared to the symptom-based scoring system alone is unknown. Third, because Xpert Ultra on respiratory samples has higher sensitivity than stool Xpert Ultra, it is unclear if referral to a higher-level facility for respiratory testing is more cost-effective. In order to address these questions and inform implementation strategies, we calculated the costs of each stool processing method and modelled the potential cost-effectiveness of different scenarios for implementing stool testing at peripheral level outpatient clinics. Specifically, we projected the cost-effectiveness of implementing stool Xpert at PHCs compared to clinical diagnosis with the treatment decision algorithm alone. Furthermore, we estimated the cost-effectiveness of diagnosis at PHCs (stool and TDA) compared to referral for diagnosis at district hospitals (DH) only, or a mixed strategy which includes both evaluation at PHCs and referral to DH. Methods Micro-costing study We conducted a micro-costing study to calculate the costs of each stool processing method. Details of the stool processing methods are described elsewhere (5), including the materials and time needed to conduct each method. In general, all methods followed a similar approach to mix stool with the Xpert Sample Reagent buffer, incubate to allow sedimentation, and then dispense the supernatant into the Xpert Ultra cartridge. While the SPK included materials in a pre-assembled kit with its own additional buffer and a filter cap, OSF required making a sucrose solution monthly, and SOS used the Sample Reagent alone. Since these differences in processing methods impact cost, we used a bottom-up micro-costing approach following three sequential steps: identification of resources for laboratory processes, measurement of resource consumption, and valuation of resource consumption. Given that the three methods require similar infrastructure (buildings and electricity) and equipment, we included only recurrent costs (staff time, reagents, and consumables) related to conducting stool testing. Because laboratory staff time represents a major cost, we conducted a time and motion study to record the exact time that technicians spent processing the stool for each method. The time for incubation and sedimentation steps were specified in the protocol for each method, and actual procedure times were recorded for stool samples processed. The time for preparing the sucrose solution for OSF was also recorded. The time for Xpert Ultra testing was not included in the time and motion study, as it was the same for all methods. In clinical practice, an invalid or error result would be repeated, and require additional time and materials. The cost of invalid-repeat testing was thus calculated as a function of the invalid rate and cost per repeat test. The most cost-effective stool processing method was used in the implementation models. Model structure and comparisons The study population comprised children under 5 years of age with symptoms of presumptive TB being evaluated for pulmonary TB. The setting used for the analyses were outpatient clinics in PHCs and DHs in Uganda, one of the sites of the multi-center stool diagnostic accuracy study (5). In Uganda, most resources for diagnostic testing, including X-ray facilities, laboratories with GeneXpert, and clinical resources to perform gastric aspirates are located at centralized facilities such as district hospitals. Therefore, they often perform Xpert Ultra testing on samples received from primary health centers, including sputum and stool. We developed decision-tree models following the clinical pathway from initial evaluation to an outcome of survival or death based on the recommendations for the treatment-decision algorithms (6) (Figure 1). Children at high risk for rapid disease progression (under 2 years of age, with HIV, or with severe acute malnutrition) were tested for TB immediately with Ultra if a sample was available. Children with HIV also had urine collected for lipoarabinomannan (LAM) testing. The children not in a high-risk group returned for a follow-up visit after two weeks and those with persistent symptoms continued for additional evaluation, otherwise there were no further steps. If a urine or stool sample was not available, the child would move to the next step. While collecting these samples is non-invasive, practice has shown that children cannot always provide a sample during the clinic visit (5) . If a microbiologic diagnosis with urine LAM or Xpert Ultra is not reached, the last step is clinical evaluation with the treatment-decision algorithms. These use a scoring system to guide clinicians in reaching a diagnosis; i.e. if the score is above a set threshold then TB treatment should be initiated (6). There are two versions of the algorithms: the scoring system for TDA-A includes chest X-ray (CXR) findings for settings where X-ray is available, and TDA-B includes only clinical signs and symptoms. Once a final diagnosis of TB is reached, the child is initiated on treatment and the outcomes are classified as survival, death from TB on treatment, or death from other causes. If a TB diagnosis was missed or if a child was lost to follow-up during treatment, outcomes included death from TB with no or partial treatment, self-cure, or death from other causes. Four different strategies were compared. The standard of care at PHCs included only clinical diagnosis with the TDA-B and no Xpert Ultra testing (‘TDA-B’ strategy) . With stool testing at PHC (‘Stool’ strategy), a stool sample was collected and transported to the DH laboratory for processing and Xpert Ultra testing. If the stool was positive, the child was initiated on TB treatment. If the stool was negative, or a sample was not collected, clinical diagnosis was made using TDA-B. For the third strategy, all children were evaluated at a district hospital (‘DH’ strategy), and had a gastric aspirate collected for Xpert Ultra. If negative, clinical diagnosis was performed with the TDA-A, incorporating CXR findings. The final strategy started with stool testing at PHC, and if the stool was negative or not collected, a portion of children would be referred to district hospital for evaluation (‘Stool-Referral’). The estimates of diagnostic accuracy for each stool processing method were taken from the study by Jaganath et al. (5), which included children in Uganda, a majority of whom were younger than 5, and was the site of the micro-costing study. Additional clinical and cost parameters were taken from the literature (Table 1). Several clinical parameter estimates were obtained from TB-Speed’s multi-site study on decentralization of TB testing (7) and the authors (OM, EW) provided data for PHCs and DHs at the Ugandan site. Due to limited data available on children at PHC level, we also consulted expert opinion (see acknowledgement) for input on clinical parameters with the most uncertainty, including TB prevalence, stool sample collection, and resolution of symptoms after follow-up. Due to limited data available on pre-diagnostic loss and how implementation of new diagnostic strategies may impact the pathway, loss to follow-up before or during the diagnostic process was not included. Table 1. The model parameter estimates for children with presumptive TB under 5 years of age Clinical Parameters Base Case Range Distribution Reference Prevalence of TB, PHC 0.03 0.02-0.04 Beta (7) Prevalence of TB, DH 0.10 0.08-0.12 Beta (7) Prevalence of HIV, PHC 0.05 0.04-0.06 Beta (8) Prevalence of HIV, DH 0.10 0.08-0.12 Beta (8) Proportion high-risk, PHC 0.60 0.48-0.72 Beta (7) Proportion high-risk, DH 0.70 0.56-0.84 Beta (7) Stool sample available 0.65 0.52-0.78 Beta (7) Urine sample available 0.65 0.52-0.78 Beta (9) Persistent symptoms, TB positive 0.80 0.64-0.96 Beta (10, 11) Persistent symptoms, TB negative 0.20 0.16-0.24 Beta (10, 11) Sensitivity of respiratory Xpert Ultra, in children without HIV 0.73 0.65-0.80 Beta (12-14) Specificity of respiratory Xpert Ultra, in children without HIV 0.97 0.96-0.98 Beta (12-14) Sensitivity of respiratory Xpert Ultra, in children with HIV 0.64 0.44-0.80 Beta (12-14) Specificity of respiratory Xpert Ultra, in children with HIV 0.98 0.93-1.0 Beta (12-14) Sensitivity of SPK 0.37 0.29-0.46 Beta (5) Specificity of SPK 0.98 0.96-0.99 Beta (5) Sensitivity of SOS 0.39 0.27-0.51 Beta (5) Specificity of SOS 0.97 0.94-0.99 Beta (5) Sensitivity of OSF 0.31 0.20-0.44 Beta (5) Specificity of OSF 0.97 0.93-0.99 Beta (5) Sensitivity of urine LAM 0.42 0.15-0.75 Beta (15) Specificity of urine LAM 0.81 0.75-0.86 Beta (15) Referral from PHC to DH, high risk 0.35 0.28-0.42 Beta (16) Referral from PHC to DH, low risk 0.20 0.16-0.24 Beta (16) Sensitivity of TDA-B 0.86 0.68-0.94 Beta (17) Specificity of TDA-B 0.30 0.13-0.56 Beta (17) Sensitivity of TDA-A with CXR 0.88 0.71-0.95 Beta (17) Specificity of TDA-A with CXR 0.37 0.15-0.67 Beta (17) Complete TB treatment 0.70 0.56-0.84 Beta (18, 19) TB mortality, on treatment 0.03 0.007-0.07 Beta (20) TB mortality, incomplete treatment 0.17 0.12-0.23 Beta (20) TB mortality, untreated 0.44 0.37-0.5 Beta (20) Self-cure 0.30 0.18-0.42 Beta (21) Lost to follow-up 0.20 0.12-0.28 Beta (10, 19) Mortality, non-TB 0.013 0.01-0.02 Beta (22) Life expectancy, Uganda 66.7 Beta (22) Cost Parameters, I$ Base Case Range Distribution Reference Outpatient visit, PHC $3.17 2.54-3.81 Gamma (23) Outpatient visit, DH $4.46 3.57-5.35 Gamma (23) HIV testing $8.89 7.11-10.67 Gamma (24) Urine LAM testing $4.79 3.83-5.75 Gamma (25) Chest X-ray $11.11 8.89-13.34 Gamma (26) Stool collection $1.94 1.55-2.33 Gamma (27) Gastric aspirate $5.09 4.07-6.11 Gamma (27) Sample transport $1.59 1.39-1.79 Gamma (28) Xpert Ultra testing $23.12 18.50-27.74 Gamma (28, 29) Cost of TB treatment, 6 months $354.31 238.16-516.02 Gamma (30) The analysis adopted a health system perspective, using a time horizon of one year for program implementation and costs of the entire pathway including treatment. Patient costs, such as transportation, were not included. Costs incurred in different years were converted to 2023 International dollars (I$) using World Bank inflation data (31, 32). Health outcomes were calculated as life year saved (LYS) over the child’s lifetime with a discount rate of 3% (33). The incremental cost-effectiveness ratio (ICER) per LYS was calculated by dividing the incremental costs by the incremental effects . When diagnosed with TB and initiated on treatment, children were assumed to live their full life expectancy, and death from TB would be averted. Results were appraised in reference to the maximum willingness to pay (WTP), calculated using a country-specific cost effectiveness threshold (34) based on the 2023 per capita GDP for Uganda (35). We extrapolated Uganda’s WTP in 2023 from its estimated PPP-adjusted threshold in 2013, resulting in a WTP threshold of I$569. One-way and probabilistic sensitivity analyses were conducted to evaluate how the range of uncertainty in model parameters impacted the ICER estimates. TreeAge Pro 2024 was used for analysis and the model is included in Additional File 1. The study was approved by the Heidelberg University Ethics Committee and reported following the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) 2022 guidance (Additional File 2) (36). Results Micro-costing study The time and motion results were recorded for 51 stool samples and four batches of sucrose solution processed between October 2020 to February 2021 (Additional file 3). The median procedure time was the longest for OSF, including time to prepare the sucrose solution, which resulted in the highest cost for staff time (Table 2 ). SOS had the lowest cost of the three stool processing methods at $ 12.50 per test, followed by SPK at $ 19.13 and OSF at $ 19.51 (Table 2 ). The higher rate of invalid Xpert Ultra results for the OSF method required more repeat testing and increased the procedure cost. The cost of consumables was the lowest for SOS as very few materials are required besides those provided with the Xpert Ultra cartridge. From the diagnostic accuracy study, SOS had the highest sensitivity (38.6%), compared to SPK (36.9%) and OSF (31.3%), although the differences were not statistically significant. Therefore, SOS has the lowest cost and highest effectiveness and was used in the implementation models. Table 2 Results of the micro-costing study for each stool processing method Item Simple One-Step (SOS) Stool Processing Kit (SPK) Optimized Sucrose Flotation (OSF) Time and motion results (in minutes) Incubation/sedimentation time 10/10 30* 30/15 Procedure time, median (range) 23 ( 21 – 30 ) 23 ( 18 – 40 ) 43 (32–74) Sucrose preparation time, per aliquot N/A N/A 2 Non-determinate results ( 5 ) 11.3% 9.7% 12.6% Costs, 2023 I $ Staff time (range) $ 2.31 (2.02–2.90) $ 2.31 (1.73–3.85) $ 4.27 (3.08–7.13) Consumables $ 0.95 $ 7.16 $ 5.09 Xpert MTB/RIF Ultra ( 37 ) $ 7.97 $ 7.97 $ 7.97 Cost of invalid-repeat testing $ 1.27 $ 1.69 $ 2.18 Total cost, range $ 12.50 (12.22–13.09) $ 19.13 (18.56–20.67) $ 19.51 (18.33–22.37) *The SPK method does not have a sedimentation step. Comparison of Implementation strategies The TDA-B strategy had the lowest cost at I $ 169.79, and the others had proportionately higher costs as more diagnostics were included (Fig. 2). The effectiveness was very similar for the TDA-B (28.23 LYS), Stool (28.24 LYS), and Stool-Referral strategies (28.25 LYS) as they all began with the same population at PHCs and the additional testing resulted in only small gains in incremental effectiveness. The Stool strategy had both a greater effectiveness and higher cost than diagnosis with only TDA-B at primary health clinics, but with an ICER of I $ 1,042/LYS was above the WTP threshold of I $ 569/LYS. The Stool-Referral strategy also had higher effectiveness and costs due to the increased proportion with microbiologic confirmation from Xpert Ultra on respiratory samples, with an ICER of $ 874.82/LYS compared to TDA-B alone. Evaluating children only at district hospitals had both the highest cost and was the least effective compared to other strategies, so the strategy was dominated. The additional diagnostic work-up detected more TB cases and was more costly. However, the higher prevalence of TB in this population resulted in more missed cases and a lower effectiveness overall. Sensitivity Analyses One-way sensitivity analyses explored how the uncertainty of individual parameters impacted cost-effectiveness of different scenarios. The sensitivity and specificity of the TDA’s, prevalence of TB disease, sensitivity of SOS/Ultra, cost of SOS/Ultra, and proportion of children in a high-risk group were the main drivers across all strategies (Figure S1 ). Stool testing at PHC would be cost-effective compared to TDA-B alone if the prevalence of TB disease were above 5.7% (vs. 3.0% in model), or if the cost of SOS/Ultra decreased from $ 29.62 to below $ 12.63 (Fig. 3). Furthermore, as the sensitivity of stool in young children is low, an increase in the sensitivity of SOS/Ultra by at least 30% to above 73.5% would be needed. Other changes in parameters that would make the Stool strategy cost-effective would be if the TDA-B sensitivity was lowered to < 73.9%, or if the proportion of children in the high-risk group was lowered from 60–12.9%. For children not in a high-risk group, the additional follow-up visit reduces costs because many children without TB will experience resolution of symptoms and not proceed for further testing. However, the proportion of children in the high-risk group varies by setting, and the risk of increased morbidity and mortality from delayed diagnosis or loss to follow-up may outweigh the reduced cost of testing. Additional sensitivity analyses showed that the main differences between the Stool and Stool-Referral strategies were related to the performance of the TDAs (Fig. 4). The Stool-Referral strategy would be cost-effective compared to both TDA-B and stool testing if the specificity of TDA-A was higher, or the sensitivity and specificity of TDA-B was lower. In probabilistic sensitivity analyses, incremental cost-effectiveness scatterplots for the Stool and Stool-Referral strategies compared to TDA-B showed that most values are clustered in the first quadrant (Fig. 5a-b), indicating that these strategies are more effective than TDA-B alone but more costly. However, both strategies exceed the WTP threshold in the majority of iterations. When the Stool-Referral strategy is compared to the Stool strategy, the values are also clustered in the first quadrant but distributed across the other quadrants and one-third of iterations would be cost-effective. The acceptability curve shows that, as the WTP threshold increases, the probability of the Stool-Referral strategy being cost-effective compared to the other strategies increases (Fig. 6 ). At a WTP threshold of I $ 1,000 the Stool-Referral strategy, with stool and respiratory testing, is more cost-effective than TDA-B alone. Both the Stool and DH strategies remain at a low probability of cost-effectiveness even with a high WTP threshold. Discussion Stool-based TB testing can increase access to diagnostics for children, but there is limited data on the cost-effectiveness of different implementation strategies. We found that of the current stool processing methods, SOS was the least costly. For children under 5 in Uganda, SOS/Ultra testing at a primary health center, for all children or with partial referral, was more cost-effective than evaluating all children at district hospitals with respiratory sampling and CXR. However, none of the implementation strategies proved to be more cost-effective than clinical diagnosis with TDA-B alone, and estimates were most impacted by TB prevalence, cost and sensitivity of SOS/Ultra, and diagnostic accuracy of the treatment-decision algorithms. These findings suggest that stool-based TB testing at primary health centers has better value than evaluation at district hospitals, but that further improvements in diagnostic accuracy and consideration of the appropriate setting are needed to improve cost-effectiveness. The SOS method was the least costly of the three processing methods compared to OSF and SPK. This was due to the low cost of consumables as it does not require supplies other than those included with Xpert. The low cost per test also resulted in a low cost for repeat testing in case of invalid results. Due to the low cost and relative ease of use, the SOS method has been recommended for implementation ( 38 , 39 ). In this analysis, the stool strategy was not cost-effective compared to diagnosis with only treatment decision algorithm at primary health clinics. The clinical scoring of the algorithm can be performed during a routine outpatient visit and has higher sensitivity than stool testing. A recent modelling analysis using a similar approach found that stool testing for children was cost-effective in Indonesia and Ethiopia ( 40 ). However, their approach estimated a higher prevalence of disease, a higher sensitivity of stool testing, and used estimates for clinical diagnosis with lower sensitivity and higher specificity than the newly recommended TDAs used in our models. Sensitivity analyses showed that TB prevalence has a significant impact on the cost-effectiveness. There is limited data available on the TB prevalence at the PHC level as most studies on childhood TB are done in referral hospitals. If the true prevalence of TB disease in children presenting to primary health clinics is higher than we modelled, then stool testing would be more cost-effective. However, we are confident that prevalence estimates from the TB Speed study, which enrolled a population similar to that modelled, are reliable. The sensitivity and cost of SOS/Ultra testing also influenced the cost-effectiveness. Other recently published studies have reported higher sensitivities of SOS/Ultra, ranging from 60–91% ( 38 , 41 ) although several of these were early-stage studies with a larger proportion of older children who were hospitalized. If SOS/Ultra performs better than our estimates, stool testing would be more cost-effective. However, since stool testing is mainly aimed for implementation at PHCs, where children are more likely to have early-stage paucibacillary disease, the sensitivity may be lower. Also, the sensitivity of SOS/Ultra was about 10% lower in the under 5 age group of the diagnostic accuracy study ( 5 ). We did not use these estimates in our model due to the small sample size but results with the overall estimate may be optimistic for children under 5. Regarding the cost of stool testing, other low-complexity PCR tests, such as Molbio Truenat, are currently used for sputum and can be performed at point-of-care ( 42 ). While the negotiated prices are similar to Xpert ( 29 ) and the Molbio assay would need to be validated for stool samples, testing at point-of-care would eliminate the expense and delays of sample transport, reducing the total cost per test. Each strategy includes a final step of clinical diagnosis with the TDA’s. These were developed using data mainly from children at referral hospitals and currently have a provisional recommendation. As the TDAs are evaluated in populations at PHC level with less severe disease presentation and lower prevalence, the estimates of diagnostic accuracy are likely to decrease, making stool testing more effective in comparison. For the stool testing to be cost-effective, a decrease in the TDA’s accuracy of at least 10–15% would be required if all other parameters were unchanged. The Stool-Referral strategy had a higher cost and was more effective at detecting TB cases than the stool only strategy due to the addition of respiratory Xpert testing and the algorithm with CXR. Both stool strategies at PHC were more cost-effective than district hospital strategy. Centralized testing is the current standard of care in most countries because there is no capacity at PHCs to collect respiratory samples from young children or expertise to make a clinical diagnosis. Also, there are additional benefits of bacteriological confirmation with Xpert Ultra not reflected in these models. In settings with a higher prevalence of drug resistance, the detection of rifampicin resistance and the ensuing appropriate treatment would likely result in better outcomes. The use of stool testing at PHC may also allow for the earlier detection and treatment, preventing progression to more severe disease. Our findings comparing different strategies will be useful to guide implementation of stool testing. Despite the limited cost-effectiveness, the added value of stool testing is considered highly in some settings and implementation is already underway in some countries. It will be important to update the cost-effectiveness models with country-specific parameters as additional data becomes available. Strengths and limitations This analysis had several limitations. First, there is limited data available for childhood TB, especially for populations presenting at primary health clinics. The models did not include pre-diagnostic loss to follow-up, patient costs, or patient perspectives on stool testing. Expanding diagnostic capacity to PHCs may improve access for more children and reduce follow-up visits, decreasing diagnostic delays and costs. Strengths of this analysis include utilizing primary data on stool testing from the diagnostic accuracy study and parameters from the TB Speed decentralization study. This is one of the first studies to assess the use and potential cost-effectiveness of the new TDAs, and the models accounted for complexity in the clinical pathway, including high-risk children and availability of stool samples. Furthermore, we consulted expert opinion and conducted multiple sensitivity analyses of clinical parameters to investigate the impact of uncertainty on the model outputs. Conclusions In summary, our findings show that the Simple One-Step was the least costly stool processing method. While stool testing was only cost-effective under some conditions, implementation at primary health centers has lower costs in relation to lives saved than evaluation only at district hospitals. Key drivers of cost effectiveness are TB prevalence and other factors including patient characteristics and initial screening. Lower cost molecular diagnostics that have similar or higher sensitivity should be explored for stool-based TB testing at the point-of-care to improve access and cost-effectiveness for young children. Abbreviations CXR: chest X-ray DH: district hospital ICER: incremental cost-effectiveness ratio I$: International dollars LAM: lipoarabinomannan LYS: life years saved OSF: Optimized Sucrose Flotation PHC: primary health clinic SPK: Stool Processing Kit SOS: Simple One-Step TB: tuberculosis TDA: treatment decision algorithm WHO: World Health Organization WTP: willingness to pay Declarations Ethics approval and consent to participate: The study was approved by the Heidelberg University Ethics Committee (S-856/2020) and does not include factors necessitating patient consent. The data on stool processing was recorded for samples collected from children enrolled in the diagnostic accuracy study, which received separate ethics approval. The modelling analysis used published data. This study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki and other applicable international and national ethical guidelines. Consent for publication: Not applicable Availability of data and materials: All data generated and analyzed during this study are included in this published article and its supplementary information files. Competing interests: The authors declare that they have no competing interests. Funding: This work was supported by the National Institute of Allergy and Infectious Diseases at the National Institutes of Health [U01AI152087 to AC and CMD]; the National Heart, Lung, and Blood Institute at the National Institutes of Health [K23HL153581 to DJ]; and the German Center for Infection Research (DZIF) [TTU.02.813, funding indicator 8029802813 to MG]. Author contributions : MG, DJ, HN, AC, MDA, and CMD contributed to conceptualization and methodology. MaN, MoN, EA, and PW collected cost and time data. MG analyzed the data and was a major contributor in writing the manuscript. All authors read and approved the final manuscript. Acknowledgements: We would like to acknowledge TB Speed, KNCV, and David Alland for their work on developing the stool processing methods. We would also like to thank the following experts for providing input on the clinical parameters for childhood TB: Beate Kampmann, Ben Marais, and Steve Graham. References World Health Organization. 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Tuberculosis diagnostic accuracy of stool Xpert MTB/RIF and urine AlereLAM in vulnerable children. Eur Respir J. 2021. Wobudeya EN, M.; Nguyet, MHTN.; Taguebue, J-V. Effect of decentralizing childhood tuberculosis diagnosis to primary health center and district hospital level - A pre-post study in six high tuberculosis incidence countries. Lancet. 2024;pre-print. Gunasekera KS, Marcy O, Muñoz J, Lopez-Varela E, Sekadde MP, Franke MF, et al. Development and validation of treatment-decision algorithms for children evaluated for pulmonary tuberculosis: an individual participant data meta-analysis. medRxiv. 2022:2022.09.13.22279911. Onyango DO, Yuen CM, Masini E, Borgdorff MW. Epidemiology of Pediatric Tuberculosis in Kenya and Risk Factors for Mortality during Treatment: A National Retrospective Cohort Study. J Pediatr. 2018;201:115-21. Flick RJ, Kim MH, Simon K, Munthali A, Hosseinipour MC, Rosenberg NE, et al. Burden of disease and risk factors for death among children treated for tuberculosis in Malawi. Int J Tuberc Lung Dis. 2016;20(8):1046-54. Jenkins HE, Yuen CM, Rodriguez CA, Nathavitharana RR, McLaughlin MM, Donald P, et al. Mortality in children diagnosed with tuberculosis: a systematic review and meta-analysis. Lancet Infect Dis. 2017;17(3):285-95. Tiemersma EW, van der Werf MJ, Borgdorff MW, Williams BG, Nagelkerke NJ. Natural history of tuberculosis: duration and fatality of untreated pulmonary tuberculosis in HIV negative patients: a systematic review. PLoS One. 2011;6(4):e17601. World Health Organization. Global Health Observatory [Available from: https://www.who.int/data/gho World Health Organization. WHO-CHOICE estimates of cost for inpatient and outpatient health service delivery 2021 [Available from: https://www.who.int/teams/health-systems-governance-and-financing/economic-analysis/costing-and-technical-efficiency/quantities-and-unit-prices-(cost-inputs). Mulogo E, Batwala V, Nuwaha F, Aden A, Baine O. Cost effectiveness of facility and home based HIV voluntary counseling and testing strategies in rural Uganda. African Health Sciences. 2013;13(2). Sun D, Dorman S, Shah M, Manabe YC, Moodley VM, Nicol MP, et al. Cost utility of lateral-flow urine lipoarabinomannan for tuberculosis diagnosis in HIV-infected African adults. The International Journal of Tuberculosis and Lung Disease. 2013;17(4):552-8. Sekandi JN, Dobbin K, Oloya J, Okwera A, Whalen CC, Corso PS. Cost-effectiveness analysis of community active case finding and household contact investigation for tuberculosis case detection in urban Africa. PLoS One. 2015;10(2):e0117009. Stop TB Partnership. POSEE Sample collection budgeting tool for children [Available from: http://www.stoptb.org/wg/dots_expansion/childhoodtb/posee.asp. Tucker A, Oyuku D, Nalugwa T, Nantale M, Ferguson O, Farr K, et al. Costs along the TB diagnostic pathway in Uganda. Int J Tuberc Lung Dis. 2021;25(1):61-3. Partnership ST. Global Drug Facility; Diagnostics, Medical Devices & Other Health Products Catalog 2025 [Available from: https://www.stoptb.org/sites/default/files/documents/23.01.25%20GDF_Diag%20and%20MD_catalog.pdf. Siapka MV, A; Cunnama, L; Pineda, C; Cerecero, D; Sweeny, S; Bautista-Arredondo, S; Bollinger, L; Cameron, D; Levin, C; Gomez, GB. Cost of tuberculosis treatment in low- and middle-income countries: systematic review and meta-regression. IJTLD. 2020;24(8):802-10. Turner HC, Lauer JA, Tran BX, Teerawattananon Y, Jit M. Adjusting for Inflation and Currency Changes Within Health Economic Studies. Value in Health. 2019;22(9):1026-32. Ha J, Kose MA, Ohnsorge F. One-stop source: A global database of inflation. Journal of International Money and Finance. 2023;137:102896. Bertram MY, Lauer JA, Stenberg K, Edejer TTT. Methods for the Economic Evaluation of Health Care Interventions for Priority Setting in the Health System: An Update From WHO CHOICE. International Journal of Health Policy and Management. 2021. Woods B, Revill P, Sculpher M, Claxton K. Country-Level Cost-Effectiveness Thresholds: Initial Estimates and the Need for Further Research. Value in Health. 2016;19(8):929-35. Bank W. Uganda Indicators 2024 [Available from: https://data.worldbank.org/country/uganda?view=chart. Husereau D, Drummond M, Augustovski F, De Bekker-Grob E, Briggs AH, Carswell C, et al. Consolidated Health Economic Evaluation Reporting Standards 2022 (CHEERS 2022) statement: updated reporting guidance for health economic evaluations. BMC Medicine. 2022;20(1). Fund TG. Briefing Note: New Pricing for Cepheid GeneXpert Tuberculosis Testing 2023 [Available from: chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://www.theglobalfund.org/media/13442/operational_2023-10-cepheid-genexpert-tb-testing_briefingnote_en.pdf. Yenew B, De Haas P, Babo Y, Diriba G, Sherefdin B, Bedru A, et al. Diagnostic accuracy, feasibility and acceptability of stool-based testing for childhood tuberculosis. ERJ Open Research. 2024;10(3):00710-2023. World Health Organization. Practical manual of processing stool samples for diagnosis of childhood TB. Geneva: World Health Organization; 2022. Mafirakureva N, Klinkenberg E, Spruijt I, Levy J, Shaweno D, De Haas P, et al. Xpert Ultra stool testing to diagnose tuberculosis in children in Ethiopia and Indonesia: a model-based cost-effectiveness analysis. BMJ Open. 2022;12(7):e058388. Carratalà-Castro L, Munguambe S, Saavedra-Cervera B, de Haas P, Kay A, Marcy O, et al. Performance of stool-based molecular tests and processing methods for paediatric tuberculosis diagnosis: a systematic review and meta-analysis. The Lancet Microbe. 2024:100963. Penn-Nicholson A, Gomathi SN, Ugarte-Gil C, Meaza A, Lavu E, Patel P, et al. A prospective multicentre diagnostic accuracy study for the Truenat tuberculosis assays. Eur Respir J. 2021;58(5). Additional Declarations No competing interests reported. Supplementary Files Additionalfile1.trex Additional material Additional file 1: Additional_file_1.trex Decision tree models. This file contains the decision-tree models used for the cost-effectiveness analysis. Additionalfile2.pdf Additional file 2: Additional_file_2.pdf CHEERS 2022 Checklist. This file contains a checklist for the Consolidated Health Economic Evaluation Reporting Standards guidance. Additionalfile3.xlsx Additional file 3. Additional_file_3. This file contains the results of the time and motion study. Cite Share Download PDF Status: Published Journal Publication published 13 Oct, 2025 Read the published version in BMC Health Services Research → Version 1 posted Editorial decision: Revision requested 27 Jun, 2025 Reviews received at journal 24 Jun, 2025 Reviewers agreed at journal 05 Jun, 2025 Reviewers agreed at journal 05 Jun, 2025 Reviewers agreed at journal 02 Jun, 2025 Reviews received at journal 28 Apr, 2025 Reviewers agreed at journal 25 Apr, 2025 Reviewers agreed at journal 23 Apr, 2025 Reviewers invited by journal 31 Mar, 2025 Editor assigned by journal 28 Mar, 2025 Editor invited by journal 27 Mar, 2025 Submission checks completed at journal 26 Mar, 2025 First submitted to journal 26 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6278387","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":441387158,"identity":"7f49f416-c90b-4932-a82d-733549b8022d","order_by":0,"name":"Mary Gaeddert","email":"","orcid":"","institution":"University Hospital Heidelberg","correspondingAuthor":false,"prefix":"","firstName":"Mary","middleName":"","lastName":"Gaeddert","suffix":""},{"id":441387159,"identity":"2f8b6917-df77-40cd-b660-1cd13a6e4340","order_by":1,"name":"Devan Jaganath","email":"","orcid":"","institution":"University of California San Francisco","correspondingAuthor":false,"prefix":"","firstName":"Devan","middleName":"","lastName":"Jaganath","suffix":""},{"id":441387160,"identity":"73e1e6a4-41fa-4f19-a89e-f2fa21a47c7a","order_by":2,"name":"Hoa Nguyen","email":"","orcid":"","institution":"Heidelberg University","correspondingAuthor":false,"prefix":"","firstName":"Hoa","middleName":"","lastName":"Nguyen","suffix":""},{"id":441387161,"identity":"5126ee8c-dd46-408b-99f9-d019f5b3b8d2","order_by":3,"name":"Abdulkadir Civan","email":"","orcid":"","institution":"University Hospital Heidelberg","correspondingAuthor":false,"prefix":"","firstName":"Abdulkadir","middleName":"","lastName":"Civan","suffix":""},{"id":441387162,"identity":"43af1f25-d6ca-4e51-b5a1-a753330f0b37","order_by":4,"name":"Pamela Nabeta","email":"","orcid":"","institution":"FIND","correspondingAuthor":false,"prefix":"","firstName":"Pamela","middleName":"","lastName":"Nabeta","suffix":""},{"id":441387163,"identity":"02f003d0-34cb-4877-b314-b8f620f7eb0d","order_by":5,"name":"Andre Trollip","email":"","orcid":"","institution":"FIND","correspondingAuthor":false,"prefix":"","firstName":"Andre","middleName":"","lastName":"Trollip","suffix":""},{"id":441387164,"identity":"172ddced-6573-4aff-98c0-86e831f84e77","order_by":6,"name":"Robert Castro","email":"","orcid":"","institution":"University of California San Francisco","correspondingAuthor":false,"prefix":"","firstName":"Robert","middleName":"","lastName":"Castro","suffix":""},{"id":441387165,"identity":"7085aa7a-21b0-4eba-8149-5ca48fe0beb4","order_by":7,"name":"Mariam Nakabuye","email":"","orcid":"","institution":"Makerere University College of Health Sciences","correspondingAuthor":false,"prefix":"","firstName":"Mariam","middleName":"","lastName":"Nakabuye","suffix":""},{"id":441387166,"identity":"ff924b5f-0bfc-45ed-9bad-94832f9a6d2b","order_by":8,"name":"Moses Nsereko","email":"","orcid":"","institution":"Makerere University College of Health Sciences","correspondingAuthor":false,"prefix":"","firstName":"Moses","middleName":"","lastName":"Nsereko","suffix":""},{"id":441387167,"identity":"9dbe3e61-2fe1-4660-b0e4-d7414512f5fe","order_by":9,"name":"Ernest Aben","email":"","orcid":"","institution":"Makerere University College of Health Sciences","correspondingAuthor":false,"prefix":"","firstName":"Ernest","middleName":"","lastName":"Aben","suffix":""},{"id":441387168,"identity":"9958420b-d534-4ff9-bba5-35d5bff5461a","order_by":10,"name":"Peter Wambi","email":"","orcid":"","institution":"Makerere University College of Health 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Denkinger","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABZUlEQVRIie2QMUvDQBTHnxSS5WrXC5XmK1wIpLg0g1/kjoBToIWCCNY202XRPUK/hIs4Bh60S3AuKNoiZMrQSRSkeEmrNLQ4i+Q3XP5c3u/evQOoqPiDNEK1EAbgAtSA51t6ACrEoAE/wHUV31YobhQr+FZI/KPAPoWd5EV5iJWyPqUoiIvKvUqtltKs1wE2vUa2uL9stW8yZz6Hp9ah7sXYO3/uBCqUFM0xxswDljx4XCRT++jRbzMOqa2RlGOU9L1AhZICTpOolc18OxZyIqKm71CxQiGpz7AuuQcqlBT9TSkjYC9ZoYwiI3HUODiStLvE+ipXusvS+CTvgqoLsbmQA04pKRSuUR+wHvCO6lJ+ZNI/HrMpsa58mwkZWxE5PcsVS5KUYTThXA21fbFGGN7Oss+LlqsntvEhhybV8c54BzTN0Fu89gbcbejeHHYhmy/u/hJyT/0Ww90t93ejoqKi4v/zBeeTe5wUnx/CAAAAAElFTkSuQmCC","orcid":"","institution":"University Hospital Heidelberg","correspondingAuthor":true,"prefix":"","firstName":"Claudia","middleName":"M.","lastName":"Denkinger","suffix":""}],"badges":[],"createdAt":"2025-03-21 14:23:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6278387/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6278387/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12913-025-13546-3","type":"published","date":"2025-10-13T15:58:22+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":81699096,"identity":"747badf6-e81d-4fcb-8d87-035fd463a795","added_by":"auto","created_at":"2025-04-30 12:59:58","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":37657,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStructure of decision tree models\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLegend: This figure shows a simplified version of the clinical pathway for each model. The sample for Xpert Ultra testing (stool or gastric aspirate) and treatment decision algorithm used (TDA-A or B) vary according to the strategy.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6278387/v1/13cb704b8c55b4cf3a655ec1.jpg"},{"id":81699143,"identity":"d75f879b-1885-4835-8f55-59f75a8a7dce","added_by":"auto","created_at":"2025-04-30 13:01:05","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":69682,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCost-effectiveness analysis results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLegend: LYS= life-years saved, I$ = International dollars, dotted line = Willingness to pay (WTP) threshold of $569\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6278387/v1/f7dfe0e0dac19f7820391a5b.jpg"},{"id":81699116,"identity":"2fddf3de-4a7f-4c47-b410-de3c33950837","added_by":"auto","created_at":"2025-04-30 13:00:10","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":32873,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOne-way sensitivity analyses for individual parameters of SOS compared to TDA-B at primary health centers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLegend: horizontal line = Willingness to Pay threshold, vertical line = threshold value for cost-effectiveness\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6278387/v1/88dfdfe92b4bb7b134bf5dc3.jpg"},{"id":81699080,"identity":"8f9060df-9aa8-4051-96f8-59cfa8d0cfae","added_by":"auto","created_at":"2025-04-30 12:59:07","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":69753,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOne-way sensitivity analyses of all parameters\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLegend: WTP=willingness to pay threshold; EV=expected value, blue=low range of parameter, red=high range of parameter. Variables are shown in order of decreasing impact.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6278387/v1/4f2ac480d3d4d956576b3c30.jpg"},{"id":81699041,"identity":"d963d9a8-e357-455a-ac98-8d02b84574c7","added_by":"auto","created_at":"2025-04-30 12:59:00","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":90760,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIncremental cost-effectiveness scatterplots\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLegend: Incremental Cost-Effective scatterplot using Monte-Carlo simulation with 10,000 iterations, circle = 95% confidence region, dotted line = WTP (willingness to pay threshold), green=strategy below WTP, red=strategy above WTP.\u003c/p\u003e\n\u003cp\u003eA: Comparator=Stool, Baseline=TDA-B. In 12.4% of the iterations, Stool was \u0026lt;WTP (green); in 87.6% Stool was \u0026gt;WTP (red).\u003c/p\u003e\n\u003cp\u003eB: Comparator=Referral, Baseline=TDA-B. In 23.1% of the iterations, the Referral strategy was \u0026lt;WTP (green); in 76.9% the Referral strategy was \u0026gt;WTP (red).\u003c/p\u003e\n\u003cp\u003eC: Comparator=Referral, Baseline=Stool. In 42.2% of the iterations, the Referral strategy was \u0026lt;WTP (green); in 57.8% the Referral strategy was \u0026gt;WTP (red).\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6278387/v1/10d0c196d7e782b6a46f16a3.jpg"},{"id":81699075,"identity":"f44aa0a9-d721-4b68-a0cd-457dc0c5f7d1","added_by":"auto","created_at":"2025-04-30 12:59:04","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":47906,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCost-effectiveness acceptability curve\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLegend: Probabilistic sensitivity analysis using Monte-Carlo simulation with 10,000 iterations; separate lines for each strategy showing the probability each strategy is cost-effective at a given WTP threshold.\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6278387/v1/c834d64cad27d79522afecd4.jpg"},{"id":93956227,"identity":"e1708e2f-ae7b-4077-8b32-707e6aeeac51","added_by":"auto","created_at":"2025-10-20 16:11:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1376758,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6278387/v1/646fc007-bd41-48fe-b2a9-a1816b329ac5.pdf"},{"id":81699089,"identity":"6cc7226c-fdb8-4609-886e-9ea5fe84baa8","added_by":"auto","created_at":"2025-04-30 12:59:20","extension":"trex","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1971205,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAdditional file 1: Additional_file_1.trex Decision tree models. This file contains the decision-tree models used for the cost-effectiveness analysis.\u003c/p\u003e","description":"","filename":"Additionalfile1.trex","url":"https://assets-eu.researchsquare.com/files/rs-6278387/v1/59ab1b497dcc5fce44c84dff.trex"},{"id":81699101,"identity":"08814456-8e27-4e3b-b479-89350a53b4c7","added_by":"auto","created_at":"2025-04-30 13:00:01","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":137955,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 2: Additional_file_2.pdf CHEERS 2022 Checklist. This file contains a checklist for the Consolidated Health Economic Evaluation Reporting Standards guidance.\u003c/p\u003e","description":"","filename":"Additionalfile2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6278387/v1/b5e4accacd90b2cb7c2098bc.pdf"},{"id":81699151,"identity":"46779133-a367-45e1-bebc-a7c14fdd8e5c","added_by":"auto","created_at":"2025-04-30 13:01:19","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":23047,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 3. Additional_file_3. This file contains the results of the time and motion study.\u003c/p\u003e","description":"","filename":"Additionalfile3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6278387/v1/456635023b2849cb1a599fce.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"A cost-effectiveness analysis of novel stool processing methods for diagnosis of tuberculosis in children under 5 years of age in Uganda","fulltext":[{"header":"Background","content":"\u003cp\u003eOf the estimated 1.3\u0026nbsp;million children with tuberculosis (TB) each year, about half are not reported to public health programs (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). This gap is in large part due to missed diagnoses that cause delays in treatment and increase morbidity and mortality. Young children present unique diagnostic challenges because they often show non-specific symptoms and cannot expectorate sputum for microbiologic testing (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). To address these barriers, non-sputum-based diagnostics, which can be implemented at or near the point of care at peripheral health facilities, are urgently needed.\u003c/p\u003e \u003cp\u003eStool has been a promising sample type for molecular TB testing, and the World Health Organization (WHO) has endorsed its use (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). To facilitate implementation in peripheral facilities, three centrifuge-free stool processing methods have been developed for use with Xpert Ultra MTB/RIF (Cepheid, USA): the Stool Processing Kit (SPK, FIND), Simple One-Step (SOS, KNCV), and Optimized Sucrose Flotation (OSF, TB Speed). Although each of these methods involves different levels of complexity and materials, a multi-center study has demonstrated that these methods perform similarly and are acceptable and usable for laboratory staff (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). However, there are several ongoing questions that limit implementation of stool Xpert Ultra. First, the costs of each processing method have not been compared. Second, although stool testing could be integrated into the WHO treatment decision algorithms (TDAs) for childhood TB in primary health centers (PHCs) (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), their cost-effectiveness compared to the symptom-based scoring system alone is unknown. Third, because Xpert Ultra on respiratory samples has higher sensitivity than stool Xpert Ultra, it is unclear if referral to a higher-level facility for respiratory testing is more cost-effective.\u003c/p\u003e \u003cp\u003eIn order to address these questions and inform implementation strategies, we calculated the costs of each stool processing method and modelled the potential cost-effectiveness of different scenarios for implementing stool testing at peripheral level outpatient clinics. Specifically, we projected the cost-effectiveness of implementing stool Xpert at PHCs compared to clinical diagnosis with the treatment decision algorithm alone. Furthermore, we estimated the cost-effectiveness of diagnosis at PHCs (stool and TDA) compared to referral for diagnosis at district hospitals (DH) only, or a mixed strategy which includes both evaluation at PHCs and referral to DH.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cem\u003eMicro-costing study\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe conducted a micro-costing study to calculate the costs of each stool processing method. Details of the stool processing methods are described elsewhere (5), including the materials and time needed to conduct each method. In general, all methods followed a similar approach to mix stool with the Xpert Sample Reagent buffer, incubate to allow sedimentation, and then dispense the supernatant into the Xpert Ultra cartridge. While the SPK included materials in a pre-assembled kit with its own additional buffer and a filter cap, OSF required making a sucrose solution monthly, and SOS used the Sample Reagent alone. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSince these differences in processing methods impact cost, we used a bottom-up micro-costing approach following three sequential steps: identification of resources for laboratory processes, measurement of resource consumption, and valuation of resource consumption. Given that the three methods require similar infrastructure (buildings and electricity) and equipment, we included only recurrent costs (staff time, reagents, and consumables) related to conducting stool testing. Because laboratory staff time represents a major cost, we conducted a time and motion study to record the exact time that technicians spent processing the stool for each method. The time for incubation and sedimentation steps were specified in the protocol for each method, and actual procedure times were recorded for stool samples processed. The time for preparing the sucrose solution for OSF was also recorded. The time for Xpert Ultra testing was not included in the time and motion study, as it was the same for all methods. In clinical practice, an invalid or error result would be repeated, and require additional time and materials. The cost of invalid-repeat testing was thus calculated as a function of the invalid rate and cost per repeat test. The most cost-effective stool processing method was used in the implementation models.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eModel structure and comparisons\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe study population comprised children under 5 years of age with symptoms of presumptive TB being evaluated for pulmonary TB. The setting used for the analyses were outpatient clinics in PHCs and DHs in Uganda, one of the sites of the multi-center stool diagnostic accuracy study (5). In Uganda, most resources for diagnostic testing, including X-ray facilities, laboratories with GeneXpert, and clinical resources to perform gastric aspirates are located at centralized facilities such as district hospitals. Therefore, they often perform Xpert Ultra testing on samples received from primary health centers, including sputum and stool.\u003c/p\u003e\n\u003cp\u003eWe developed decision-tree models following the clinical pathway from initial evaluation to an outcome of survival or death based on the recommendations for the treatment-decision algorithms (6) (Figure 1). \u0026nbsp;Children at high risk for rapid disease progression (under 2 years of age, with HIV, or with severe acute malnutrition) were tested for TB immediately with Ultra if a sample was available. Children with HIV also had urine collected for lipoarabinomannan (LAM) testing. The children not in a high-risk group returned for a follow-up visit after two weeks and those with persistent symptoms continued for additional evaluation, otherwise there were no further steps. If a urine or stool sample was not available, the child would move to the next step. While collecting these samples is non-invasive, practice has shown that children cannot always provide a sample during the clinic visit (5)\u003cem\u003e.\u0026nbsp;\u003c/em\u003eIf a microbiologic diagnosis with urine LAM or Xpert Ultra is not reached, the last step is clinical evaluation with the treatment-decision algorithms. These use a scoring system to guide clinicians in reaching a diagnosis; i.e. if the score is above a set threshold then TB treatment should be initiated (6). There are two versions of the algorithms: the scoring system for TDA-A includes chest X-ray (CXR) findings for settings where X-ray is available, and TDA-B includes only clinical signs and symptoms. \u0026nbsp; Once a final diagnosis of TB is reached, the child is initiated on treatment and the outcomes are classified as survival, death from TB on treatment, or death from other causes. If a TB diagnosis was missed or if a child was lost to follow-up during treatment, outcomes included death from TB with no or partial treatment, self-cure, or death from other causes.\u003c/p\u003e\n\u003cp\u003eFour different strategies were compared. The standard of care at PHCs included only clinical diagnosis with the TDA-B and no Xpert Ultra testing (\u0026lsquo;TDA-B\u0026rsquo; strategy)\u003cem\u003e.\u003c/em\u003e With stool testing at PHC (\u0026lsquo;Stool\u0026rsquo; strategy), a stool sample was collected and transported to the DH laboratory for processing and Xpert Ultra testing. If the stool was positive, the child was initiated on TB treatment. If the stool was negative, or a sample was not collected, clinical diagnosis was made using TDA-B. For the third strategy, all children were evaluated at a district hospital (\u0026lsquo;DH\u0026rsquo; strategy), and had a gastric aspirate collected for Xpert Ultra. If negative, clinical diagnosis was performed with the TDA-A, incorporating CXR findings. The final strategy started with stool testing at PHC, and if the stool was negative or not collected, a portion of children would be referred to district hospital for evaluation (\u0026lsquo;Stool-Referral\u0026rsquo;).\u003c/p\u003e\n\u003cp\u003eThe estimates of diagnostic accuracy for each stool processing method were taken from the study by Jaganath et al.\u003cem\u003e\u0026nbsp;\u003c/em\u003e(5), which included children in Uganda, a majority of whom were younger than 5, and was the site of the micro-costing study. Additional clinical and cost parameters were taken from the literature (Table 1). Several clinical parameter estimates were obtained from TB-Speed\u0026rsquo;s multi-site study on decentralization of TB testing (7) and the authors (OM, EW) provided data for PHCs and DHs at the Ugandan site. Due to limited data available on children at PHC level, we also consulted expert opinion (see acknowledgement) for input on clinical parameters with the most uncertainty, including TB prevalence, stool sample collection, and resolution of symptoms after follow-up. Due to limited data available on pre-diagnostic loss and how implementation of new diagnostic strategies may impact the pathway, loss to follow-up before or during the diagnostic process was not included.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003eThe model parameter estimates for children with presumptive TB under 5 years of age\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"601\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eClinical Parameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBase Case\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRange\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDistribution\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReference\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003ePrevalence of TB, PHC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.02-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e(7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003ePrevalence of TB, DH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.08-0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e(7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003ePrevalence of HIV, PHC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.04-0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e(8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003ePrevalence of HIV, DH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.08-0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e(8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eProportion high-risk, PHC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.48-0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e(7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eProportion high-risk, DH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.56-0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e(7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eStool sample available\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.52-0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e(7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eUrine sample available\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.52-0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e(9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003ePersistent symptoms, TB positive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.64-0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e(10, 11)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003ePersistent symptoms, TB negative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.16-0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e(10, 11)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eSensitivity of respiratory Xpert Ultra, in children without HIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.65-0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e(12-14)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eSpecificity of respiratory Xpert Ultra, in children without HIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.96-0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e(12-14)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eSensitivity of respiratory Xpert Ultra, in children with HIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.44-0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e(12-14)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eSpecificity of respiratory Xpert Ultra, in children with HIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.93-1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e(12-14)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eSensitivity of SPK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.29-0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e(5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eSpecificity of SPK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.96-0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e(5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eSensitivity of SOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.27-0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e(5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eSpecificity of SOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.94-0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e(5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eSensitivity of OSF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.20-0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e(5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eSpecificity of OSF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.93-0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e(5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eSensitivity of urine LAM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.15-0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e(15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eSpecificity of urine LAM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.75-0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e(15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eReferral from PHC to DH, high risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.28-0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e(16)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eReferral from PHC to DH, low risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.16-0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e(16)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eSensitivity of TDA-B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.68-0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e(17)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eSpecificity of TDA-B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.13-0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e(17)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eSensitivity of TDA-A with CXR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.71-0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e(17)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eSpecificity of TDA-A with CXR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.15-0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e(17)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eComplete TB treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.56-0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e(18, 19)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eTB mortality, on treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.007-0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e(20)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eTB mortality, incomplete treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.12-0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e(20)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eTB mortality, untreated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.37-0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e(20)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eSelf-cure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.18-0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e(21)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eLost to follow-up\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.12-0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e(10, 19)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eMortality, non-TB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.01-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e(22)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eLife expectancy, Uganda\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e66.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e(22)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCost Parameters, I$\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBase Case\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRange\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDistribution\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReference\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eOutpatient visit, PHC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e$3.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e2.54-3.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eGamma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e(23)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eOutpatient visit, DH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e$4.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e3.57-5.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eGamma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e(23)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eHIV testing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e$8.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e7.11-10.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eGamma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e(24)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eUrine LAM testing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e$4.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e3.83-5.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eGamma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e(25)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eChest X-ray\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e$11.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e8.89-13.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eGamma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e(26)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eStool collection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e$1.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e1.55-2.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eGamma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e(27)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eGastric aspirate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e$5.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e4.07-6.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eGamma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e(27)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eSample transport\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e$1.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e1.39-1.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eGamma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e(28)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eXpert Ultra testing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e$23.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e18.50-27.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eGamma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e(28, 29)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eCost of TB treatment, 6 months\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e$354.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e238.16-516.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eGamma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e(30)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe analysis adopted a health system perspective, using a time horizon of one year for program implementation and costs of the entire pathway including treatment. Patient costs, such as transportation, were not included. Costs incurred in different years were converted to 2023 International dollars (I$) using World Bank inflation data (31, 32). Health outcomes were calculated as life year saved (LYS) over the child\u0026rsquo;s lifetime with a discount rate of 3% (33). The incremental cost-effectiveness ratio (ICER) per LYS was calculated by dividing the incremental costs by the incremental effects\u003cem\u003e.\u0026nbsp;\u003c/em\u003eWhen diagnosed with TB and initiated on treatment, children were assumed to live their full life expectancy, and death from TB would be averted. Results were appraised in reference to the maximum willingness to pay (WTP), calculated using a country-specific cost effectiveness threshold (34) based on the 2023 per capita GDP for Uganda (35). We extrapolated Uganda\u0026rsquo;s WTP in 2023 from its estimated PPP-adjusted threshold in 2013, resulting in a WTP threshold of I$569. One-way and probabilistic sensitivity analyses were conducted to evaluate how the range of uncertainty in model parameters impacted the ICER estimates. TreeAge Pro 2024 was used for analysis and the model is included in Additional File 1. The study was approved by the Heidelberg University Ethics Committee and reported following the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) 2022 guidance (Additional File 2) (36).\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003eMicro-costing study\u003c/h2\u003e\n \u003cp\u003eThe time and motion results were recorded for 51 stool samples and four batches of sucrose solution processed between October 2020 to February 2021 (Additional file 3). The median procedure time was the longest for OSF, including time to prepare the sucrose solution, which resulted in the highest cost for staff time (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). SOS had the lowest cost of the three stool processing methods at \u003cspan\u003e$\u003c/span\u003e12.50 per test, followed by SPK at \u003cspan\u003e$\u003c/span\u003e19.13 and OSF at \u003cspan\u003e$\u003c/span\u003e19.51 (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The higher rate of invalid Xpert Ultra results for the OSF method required more repeat testing and increased the procedure cost. The cost of consumables was the lowest for SOS as very few materials are required besides those provided with the Xpert Ultra cartridge. From the diagnostic accuracy study, SOS had the highest sensitivity (38.6%), compared to SPK (36.9%) and OSF (31.3%), although the differences were not statistically significant. Therefore, SOS has the lowest cost and highest effectiveness and was used in the implementation models.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eResults of the micro-costing study for each stool processing method\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eItem\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSimple One-Step (SOS)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStool Processing Kit (SPK)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOptimized Sucrose Flotation (OSF)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eTime and motion results (in minutes)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIncubation/sedimentation time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10/10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30/15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProcedure time, median (range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23 (\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23 (\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43 (32\u0026ndash;74)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSucrose preparation time, per aliquot\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-determinate results (\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eCosts, 2023 I\u003cspan\u003e$\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStaff time (range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e2.31 (2.02\u0026ndash;2.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e2.31 (1.73\u0026ndash;3.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e4.27 (3.08\u0026ndash;7.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConsumables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e7.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e5.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eXpert MTB/RIF Ultra (\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e7.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e7.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e7.97\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCost of invalid-repeat testing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e1.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e1.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e2.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal cost, range\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e12.50 (12.22\u0026ndash;13.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e19.13 (18.56\u0026ndash;20.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e19.51 (18.33\u0026ndash;22.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e*The SPK method does not have a sedimentation step.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003ch3\u003eComparison of Implementation strategies\u003c/h3\u003e\n\u003cp\u003eThe TDA-B strategy had the lowest cost at I\u003cspan\u003e$\u003c/span\u003e169.79, and the others had proportionately higher costs as more diagnostics were included (Fig.\u0026nbsp;2). The effectiveness was very similar for the TDA-B (28.23 LYS), Stool (28.24 LYS), and Stool-Referral strategies (28.25 LYS) as they all began with the same population at PHCs and the additional testing resulted in only small gains in incremental effectiveness. The Stool strategy had both a greater effectiveness and higher cost than diagnosis with only TDA-B at primary health clinics, but with an ICER of I\u003cspan\u003e$\u003c/span\u003e1,042/LYS was above the WTP threshold of I\u003cspan\u003e$\u003c/span\u003e569/LYS. The Stool-Referral strategy also had higher effectiveness and costs due to the increased proportion with microbiologic confirmation from Xpert Ultra on respiratory samples, with an ICER of \u003cspan\u003e$\u003c/span\u003e874.82/LYS compared to TDA-B alone. Evaluating children only at district hospitals had both the highest cost and was the least effective compared to other strategies, so the strategy was dominated. The additional diagnostic work-up detected more TB cases and was more costly. However, the higher prevalence of TB in this population resulted in more missed cases and a lower effectiveness overall.\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eSensitivity Analyses\u003c/h2\u003e\n \u003cp\u003eOne-way sensitivity analyses explored how the uncertainty of individual parameters impacted cost-effectiveness of different scenarios. The sensitivity and specificity of the TDA\u0026rsquo;s, prevalence of TB disease, sensitivity of SOS/Ultra, cost of SOS/Ultra, and proportion of children in a high-risk group were the main drivers across all strategies (Figure \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e). Stool testing at PHC would be cost-effective compared to TDA-B alone if the prevalence of TB disease were above 5.7% (vs. 3.0% in model), or if the cost of SOS/Ultra decreased from \u003cspan\u003e$\u003c/span\u003e29.62 to below \u003cspan\u003e$\u003c/span\u003e12.63 (Fig. 3). Furthermore, as the sensitivity of stool in young children is low, an increase in the sensitivity of SOS/Ultra by at least 30% to above 73.5% would be needed. Other changes in parameters that would make the Stool strategy cost-effective would be if the TDA-B sensitivity was lowered to \u0026lt;\u0026thinsp;73.9%, or if the proportion of children in the high-risk group was lowered from 60\u0026ndash;12.9%. For children not in a high-risk group, the additional follow-up visit reduces costs because many children without TB will experience resolution of symptoms and not proceed for further testing. However, the proportion of children in the high-risk group varies by setting, and the risk of increased morbidity and mortality from delayed diagnosis or loss to follow-up may outweigh the reduced cost of testing.\u003c/p\u003e\n \u003cp\u003eAdditional sensitivity analyses showed that the main differences between the Stool and Stool-Referral strategies were related to the performance of the TDAs (Fig. 4). The Stool-Referral strategy would be cost-effective compared to both TDA-B and stool testing if the specificity of TDA-A was higher, or the sensitivity and specificity of TDA-B was lower.\u003c/p\u003e\n \u003cp\u003eIn probabilistic sensitivity analyses, incremental cost-effectiveness scatterplots for the Stool and Stool-Referral strategies compared to TDA-B showed that most values are clustered in the first quadrant (Fig. 5a-b), indicating that these strategies are more effective than TDA-B alone but more costly. However, both strategies exceed the WTP threshold in the majority of iterations. When the Stool-Referral strategy is compared to the Stool strategy, the values are also clustered in the first quadrant but distributed across the other quadrants and one-third of iterations would be cost-effective. The acceptability curve shows that, as the WTP threshold increases, the probability of the Stool-Referral strategy being cost-effective compared to the other strategies increases (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). At a WTP threshold of I\u003cspan\u003e$\u003c/span\u003e1,000 the Stool-Referral strategy, with stool and respiratory testing, is more cost-effective than TDA-B alone. Both the Stool and DH strategies remain at a low probability of cost-effectiveness even with a high WTP threshold.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eStool-based TB testing can increase access to diagnostics for children, but there is limited data on the cost-effectiveness of different implementation strategies. We found that of the current stool processing methods, SOS was the least costly. For children under 5 in Uganda, SOS/Ultra testing at a primary health center, for all children or with partial referral, was more cost-effective than evaluating all children at district hospitals with respiratory sampling and CXR. However, none of the implementation strategies proved to be more cost-effective than clinical diagnosis with TDA-B alone, and estimates were most impacted by TB prevalence, cost and sensitivity of SOS/Ultra, and diagnostic accuracy of the treatment-decision algorithms. These findings suggest that stool-based TB testing at primary health centers has better value than evaluation at district hospitals, but that further improvements in diagnostic accuracy and consideration of the appropriate setting are needed to improve cost-effectiveness.\u003c/p\u003e \u003cp\u003eThe SOS method was the least costly of the three processing methods compared to OSF and SPK. This was due to the low cost of consumables as it does not require supplies other than those included with Xpert. The low cost per test also resulted in a low cost for repeat testing in case of invalid results. Due to the low cost and relative ease of use, the SOS method has been recommended for implementation (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this analysis, the stool strategy was not cost-effective compared to diagnosis with only treatment decision algorithm at primary health clinics. The clinical scoring of the algorithm can be performed during a routine outpatient visit and has higher sensitivity than stool testing. A recent modelling analysis using a similar approach found that stool testing for children was cost-effective in Indonesia and Ethiopia (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). However, their approach estimated a higher prevalence of disease, a higher sensitivity of stool testing, and used estimates for clinical diagnosis with lower sensitivity and higher specificity than the newly recommended TDAs used in our models.\u003c/p\u003e \u003cp\u003eSensitivity analyses showed that TB prevalence has a significant impact on the cost-effectiveness. There is limited data available on the TB prevalence at the PHC level as most studies on childhood TB are done in referral hospitals. If the true prevalence of TB disease in children presenting to primary health clinics is higher than we modelled, then stool testing would be more cost-effective. However, we are confident that prevalence estimates from the TB Speed study, which enrolled a population similar to that modelled, are reliable.\u003c/p\u003e \u003cp\u003eThe sensitivity and cost of SOS/Ultra testing also influenced the cost-effectiveness. Other recently published studies have reported higher sensitivities of SOS/Ultra, ranging from 60\u0026ndash;91% (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e) although several of these were early-stage studies with a larger proportion of older children who were hospitalized. If SOS/Ultra performs better than our estimates, stool testing would be more cost-effective. However, since stool testing is mainly aimed for implementation at PHCs, where children are more likely to have early-stage paucibacillary disease, the sensitivity may be lower. Also, the sensitivity of SOS/Ultra was about 10% lower in the under 5 age group of the diagnostic accuracy study (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). We did not use these estimates in our model due to the small sample size but results with the overall estimate may be optimistic for children under 5. Regarding the cost of stool testing, other low-complexity PCR tests, such as Molbio Truenat, are currently used for sputum and can be performed at point-of-care (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). While the negotiated prices are similar to Xpert (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e) and the Molbio assay would need to be validated for stool samples, testing at point-of-care would eliminate the expense and delays of sample transport, reducing the total cost per test.\u003c/p\u003e \u003cp\u003eEach strategy includes a final step of clinical diagnosis with the TDA\u0026rsquo;s. These were developed using data mainly from children at referral hospitals and currently have a provisional recommendation. As the TDAs are evaluated in populations at PHC level with less severe disease presentation and lower prevalence, the estimates of diagnostic accuracy are likely to decrease, making stool testing more effective in comparison. For the stool testing to be cost-effective, a decrease in the TDA\u0026rsquo;s accuracy of at least 10\u0026ndash;15% would be required if all other parameters were unchanged.\u003c/p\u003e \u003cp\u003eThe Stool-Referral strategy had a higher cost and was more effective at detecting TB cases than the stool only strategy due to the addition of respiratory Xpert testing and the algorithm with CXR. Both stool strategies at PHC were more cost-effective than district hospital strategy. Centralized testing is the current standard of care in most countries because there is no capacity at PHCs to collect respiratory samples from young children or expertise to make a clinical diagnosis.\u003c/p\u003e \u003cp\u003eAlso, there are additional benefits of bacteriological confirmation with Xpert Ultra not reflected in these models. In settings with a higher prevalence of drug resistance, the detection of rifampicin resistance and the ensuing appropriate treatment would likely result in better outcomes. The use of stool testing at PHC may also allow for the earlier detection and treatment, preventing progression to more severe disease.\u003c/p\u003e \u003cp\u003eOur findings comparing different strategies will be useful to guide implementation of stool testing. Despite the limited cost-effectiveness, the added value of stool testing is considered highly in some settings and implementation is already underway in some countries. It will be important to update the cost-effectiveness models with country-specific parameters as additional data becomes available.\u003c/p\u003e\n\u003ch3\u003eStrengths and limitations\u003c/h3\u003e\n\u003cp\u003eThis analysis had several limitations. First, there is limited data available for childhood TB, especially for populations presenting at primary health clinics. The models did not include pre-diagnostic loss to follow-up, patient costs, or patient perspectives on stool testing. Expanding diagnostic capacity to PHCs may improve access for more children and reduce follow-up visits, decreasing diagnostic delays and costs.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eStrengths of this analysis include utilizing primary data on stool testing from the diagnostic accuracy study and parameters from the TB Speed decentralization study. This is one of the first studies to assess the use and potential cost-effectiveness of the new TDAs, and the models accounted for complexity in the clinical pathway, including high-risk children and availability of stool samples. Furthermore, we consulted expert opinion and conducted multiple sensitivity analyses of clinical parameters to investigate the impact of uncertainty on the model outputs.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, our findings show that the Simple One-Step was the least costly stool processing method. While stool testing was only cost-effective under some conditions, implementation at primary health centers has lower costs in relation to lives saved than evaluation only at district hospitals. Key drivers of cost effectiveness are TB prevalence and other factors including patient characteristics and initial screening. Lower cost molecular diagnostics that have similar or higher sensitivity should be explored for stool-based TB testing at the point-of-care to improve access and cost-effectiveness for young children.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCXR: chest X-ray\u003c/p\u003e\n\u003cp\u003eDH: district hospital\u003c/p\u003e\n\u003cp\u003eICER: incremental cost-effectiveness ratio\u003c/p\u003e\n\u003cp\u003eI$: International dollars\u003c/p\u003e\n\u003cp\u003eLAM: lipoarabinomannan\u003c/p\u003e\n\u003cp\u003eLYS: life years saved\u003c/p\u003e\n\u003cp\u003eOSF: Optimized Sucrose Flotation\u003c/p\u003e\n\u003cp\u003ePHC: primary health clinic\u003c/p\u003e\n\u003cp\u003eSPK: Stool Processing Kit\u003c/p\u003e\n\u003cp\u003eSOS: Simple One-Step\u003c/p\u003e\n\u003cp\u003eTB: tuberculosis\u003c/p\u003e\n\u003cp\u003eTDA: treatment decision algorithm\u003c/p\u003e\n\u003cp\u003eWHO: World Health Organization\u003c/p\u003e\n\u003cp\u003eWTP: willingness to pay\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/strong\u003eThe study was approved by the Heidelberg University Ethics Committee (S-856/2020) and does not include factors necessitating patient consent. The data on stool processing was recorded for samples collected from children enrolled in the diagnostic accuracy study, which received separate ethics approval. The modelling analysis used published data. This study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki and other applicable international and national ethical guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u003c/strong\u003e All data generated and analyzed during this study are included in this published article and its supplementary information files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis work was supported by the National Institute of Allergy and Infectious Diseases at the National Institutes of Health [U01AI152087 to AC and CMD]; the National Heart, Lung, and Blood Institute at the National Institutes of Health [K23HL153581 to DJ]; and the German Center for Infection Research (DZIF) [TTU.02.813, funding indicator 8029802813 to MG].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e: MG, DJ, HN, AC, MDA, and CMD contributed to conceptualization and methodology. MaN, MoN, EA, and PW collected cost and time data. MG analyzed the data and was a major contributor in writing the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eWe would like to acknowledge TB Speed, KNCV, and David Alland for their work on developing the stool processing methods. We would also like to thank the following experts for providing input on the clinical parameters for childhood TB: Beate Kampmann, Ben Marais, and Steve Graham.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWorld Health Organization. Global tuberculosis report 2024. 2024.\u003c/li\u003e\n\u003cli\u003eNicol MP, Zar HJ. Advances in the diagnosis of pulmonary tuberculosis in children. Paediatr Respir Rev. 2020;36:52-6.\u003c/li\u003e\n\u003cli\u003ePerez-Velez CM, Marais BJ. Tuberculosis in children. 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Journal of International Money and Finance. 2023;137:102896.\u003c/li\u003e\n\u003cli\u003eBertram MY, Lauer JA, Stenberg K, Edejer TTT. Methods for the Economic Evaluation of Health Care Interventions for Priority Setting in the Health System: An Update From WHO CHOICE. International Journal of Health Policy and Management. 2021.\u003c/li\u003e\n\u003cli\u003eWoods B, Revill P, Sculpher M, Claxton K. Country-Level Cost-Effectiveness Thresholds: Initial Estimates and the Need for Further Research. Value in Health. 2016;19(8):929-35.\u003c/li\u003e\n\u003cli\u003eBank W. Uganda Indicators 2024 [Available from: https://data.worldbank.org/country/uganda?view=chart.\u003c/li\u003e\n\u003cli\u003eHusereau D, Drummond M, Augustovski F, De Bekker-Grob E, Briggs AH, Carswell C, et al. Consolidated Health Economic Evaluation Reporting Standards 2022 (CHEERS 2022) statement: updated reporting guidance for health economic evaluations. BMC Medicine. 2022;20(1).\u003c/li\u003e\n\u003cli\u003eFund TG. Briefing Note: New Pricing for Cepheid GeneXpert Tuberculosis Testing 2023 [Available from: chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://www.theglobalfund.org/media/13442/operational_2023-10-cepheid-genexpert-tb-testing_briefingnote_en.pdf.\u003c/li\u003e\n\u003cli\u003eYenew B, De Haas P, Babo Y, Diriba G, Sherefdin B, Bedru A, et al. Diagnostic accuracy, feasibility and acceptability of stool-based testing for childhood tuberculosis. ERJ Open Research. 2024;10(3):00710-2023.\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. Practical manual of processing stool samples for diagnosis of childhood TB. Geneva: World Health Organization; 2022.\u003c/li\u003e\n\u003cli\u003eMafirakureva N, Klinkenberg E, Spruijt I, Levy J, Shaweno D, De Haas P, et al. Xpert Ultra stool testing to diagnose tuberculosis in children in Ethiopia and Indonesia: a model-based cost-effectiveness analysis. BMJ Open. 2022;12(7):e058388.\u003c/li\u003e\n\u003cli\u003eCarratal\u0026agrave;-Castro L, Munguambe S, Saavedra-Cervera B, de Haas P, Kay A, Marcy O, et al. Performance of stool-based molecular tests and processing methods for paediatric tuberculosis diagnosis: a systematic review and meta-analysis. The Lancet Microbe. 2024:100963.\u003c/li\u003e\n\u003cli\u003ePenn-Nicholson A, Gomathi SN, Ugarte-Gil C, Meaza A, Lavu E, Patel P, et al. A prospective multicentre diagnostic accuracy study for the Truenat tuberculosis assays. Eur Respir J. 2021;58(5).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Tuberculosis, pediatric, diagnostics, cost-effectiveness, stool","lastPublishedDoi":"10.21203/rs.3.rs-6278387/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6278387/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eStool-based molecular assays for childhood tuberculosis (TB) diagnosis have shown promise as an alternative to respiratory sample testing. While implementation is underway, evidence on cost-effectiveness is needed. Therefore, we aimed to evaluate the costs of stool testing with Xpert Ultra and model the cost-effectiveness of implementation scenarios at lower levels of care.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eWe measured costs for three new stool processing methods and modeled implementation using the least costly method. \u0026nbsp;For children under 5 years with presumptive TB at primary health clinics or district hospitals in Uganda, clinical diagnosis with treatment-decision algorithms was compared to stool testing at primary clinics, stool testing at primary clinics with referral to district hospitals if negative, or evaluation only at district hospitals with Xpert Ultra testing on respiratory samples. Using decision-tree models, we calculated the cost in international dollars (I$) per life-years saved (LYS) and the incremental cost-effectiveness ratio (ICER) assessed against the country-specific willingness to pay threshold. One-way and probabilistic sensitivity analyses were conducted.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThe Simple One-Step (SOS) was the least costly stool processing method. Compared to diagnosis with only treatment-decision algorithms, the ICER of SOS/Ultra at primary clinics was I$1041.71/LYS, SOS/Ultra with referral was I$874.82/LYS, while the district hospital strategy was dominated. Sensitivity analyses showed stool testing was cost-effective compared to only clinical diagnosis if TB prevalence at primary clinics was above 5.7%, with higher diagnostic accuracy of stool-based testing, or lower testing costs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eFor young children, stool testing at primary clinics, with or without referral to district hospitals, lowered costs in relation to lives saved compared to implementing at district hospitals alone or only clinical diagnosis using the treatment-decision algorithms.\u003c/p\u003e","manuscriptTitle":"A cost-effectiveness analysis of novel stool processing methods for diagnosis of tuberculosis in children under 5 years of age in Uganda","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-30 12:04:57","doi":"10.21203/rs.3.rs-6278387/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-27T17:09:54+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-25T01:07:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"51538422576267353337977981848328211895","date":"2025-06-05T17:08:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"91123432713503018332816890324841067774","date":"2025-06-05T15:38:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"38308644816225566081491762300488233821","date":"2025-06-02T10:38:20+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-28T18:44:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"270520326002363514237462077583510795241","date":"2025-04-25T14:56:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"310671811392137106337821200711185680732","date":"2025-04-23T15:10:09+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-31T23:57:47+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-28T11:22:20+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-03-27T07:54:56+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-26T17:07:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Health Services Research","date":"2025-03-26T17:06:32+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c61bcbfa-7ba4-41de-9239-4ddd351e83dc","owner":[],"postedDate":"April 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-10-20T16:07:46+00:00","versionOfRecord":{"articleIdentity":"rs-6278387","link":"https://doi.org/10.1186/s12913-025-13546-3","journal":{"identity":"bmc-health-services-research","isVorOnly":false,"title":"BMC Health Services Research"},"publishedOn":"2025-10-13 15:58:22","publishedOnDateReadable":"October 13th, 2025"},"versionCreatedAt":"2025-04-30 12:04:57","video":"","vorDoi":"10.1186/s12913-025-13546-3","vorDoiUrl":"https://doi.org/10.1186/s12913-025-13546-3","workflowStages":[]},"version":"v1","identity":"rs-6278387","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6278387","identity":"rs-6278387","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}