Active case-finding of tuberculosis compared with symptom-driven standard of care: a modelling analysis.

BACKGROUND: In settings with large case detection gaps, active case-finding (ACF) may play a critical role in the uberculosis (TB) response. However, ACF is resource intensive, and its effectiveness depends on whether people detected with TB through ACF might otherwise spontaneously resolve or be diagnosed through routine care. We analysed the potential effectiveness of ACF for TB relative to the counterfactual scenario of routine care alone. METHODS: We constructed a Markov simulation model of TB natural history, diagnosis, symptoms, ACF and treatment, using a hypothetical reference setting using data from South East Asian countries. We calibrated the model to empirical data using Bayesian methods, and simulated potential 5-year outcomes with an 'aspirational' ACF intervention (reflecting maximum possible effectiveness) compared with the standard-of-care outcomes. RESULTS: Under the standard of care, 51% (95% credible interval, CrI: 31%, 75%) of people with prevalent TB at baseline were estimated to be diagnosed and linked to care over 5 years. With aspirational ACF, this increased to 88% (95% CrI: 84%, 94%). Most of this difference represented people who were diagnosed and treated through ACF but experienced spontaneous resolution under standard-of-care. Aspirational ACF was projected to reduce the average duration of TB disease by 12 months (95% CrI: 6%, 18%) and TB-associated disability-adjusted life-years by 71% (95% CrI: 67%, 76%). CONCLUSION: These data illustrate the importance of considering outcomes in a counterfactual standard of care scenario, as well as trade-offs between overdiagnosis and averted morbidity through earlier diagnosis-not just for TB, but for any disease in which population-based screening is recommended.

Cost-effectiveness of Low-complexity Screening Tests in Community-based Case-finding for Tuberculosis.

INTRODUCTION: In high-burden settings, low-complexity screening tests for tuberculosis (TB) could expand the reach of community-based case-finding efforts. The potential costs and cost-effectiveness of approaches incorporating these tests are poorly understood. METHODS: We developed a microsimulation model assessing 3 approaches to community-based case-finding in hypothetical populations (India-, South Africa-, The Philippines-, Uganda-, and Vietnam-like settings) with TB prevalence 4 times that of national estimates: (1) screening with a point-of-care C-reactive protein (CRP) test, (2) screening with a more sensitive "Hypothetical Screening test" (95% sensitive for Xpert Ultra-positive TB, 70% specificity; equipment/labor costs similar to Xpert Ultra, but using a $2 cartridge) followed by sputum Xpert Ultra if positive, or (3) testing all individuals with sputum Xpert Ultra. Costs are expressed in 2023 US dollars and include treatment costs. RESULTS: Universal Xpert Ultra was estimated to cost a mean $4.0 million (95% uncertainty range: $3.5 to $4.6 million) and avert 3200 (2600 to 3900) TB-related disability-adjusted life years (DALYs) per 100 000 people screened ($670 [The Philippines] to $2000 [Vietnam] per DALY averted). CRP was projected to cost $550 (The Philippines) to $1500 (Vietnam) per DALY averted but with 44% fewer DALYs averted. The Hypothetical Screening test showed minimal benefit compared to universal Xpert Ultra, but if specificity were improved to 95% and per-test cost to $4.5 (all-inclusive), this strategy could cost $390 (The Philippines) to $940 (Vietnam) per DALY averted. CONCLUSIONS: Screening tests can meaningfully improve the cost-effectiveness of community-based case-finding for TB but only if they are sensitive, specific, and inexpensive.

Determining cost and placement decisions for moderate complexity NAATs for tuberculosis drug susceptibility testing.

BACKGROUND: Access to drug resistant testing for tuberculosis (TB) remains a challenge in high burden countries. Recently, the World Health Organization approved the use of several moderate complexity automated nucleic acid amplification tests (MC-NAAT) that have performance profiles suitable for placement in a range of TB laboratory tiers to improve drug susceptibility tests (DST) coverage. METHODS: We conducted cost analysis of two MC-NAATs with different testing throughput: Lower Throughput (LT, < 24 tests per run) and Higher Throughput (HT, upto 90+ tests per run) for placement in a hypothetical laboratory in a resource limited setting. We used per-test cost as the main indicator to assess 1) drivers of cost by resource types and 2) optimized levels of annual testing volumes for the respective MC-NAATs. RESULTS: The base-case per test cost of $18.52 (range: $13.79 - $40.70) for LT test and $15.37 (range: $9.61 - $37.40) for HT test. Per test cost estimates were most sensitive to the number of testing days per week, followed by equipment costs and TB-specific workloads. In general, HT NAATs were cheaper at all testing volume levels, but at lower testing volumes (less than 2,000 per year) LT tests can be cheaper if the durability of the testing system is markedly better and/or procured equipment costs are lower than that of HT NAAT. CONCLUSION: Assuming equivalent performance and infrastructural needs, placement strategies for MC-NAATs need to be prioritized by laboratory system's operational factors, testing demands, and costs.

Pragmatic cluster-randomized trial of home-based preventive treatment for TB in Ethiopia and South Africa (CHIP-TB).

BACKGROUND: Each year, 1 million children develop TB resulting in over 200,000 child deaths. TB preventive treatment (TPT) is highly effective in preventing TB but remains poorly implemented for household child contacts. Home-based child contact management and TPT services may improve access to care. In this study, we aim to evaluate the effectiveness and cost-effectiveness of home-based contact management with TPT initiation in two TB high-burden African countries, Ethiopia and South Africa. METHODS: This pragmatic cluster randomized trial compares home-based versus facility-based care delivery models for contact management. Thirty-six clinics with decentralized TB services (18 in Ethiopia and 18 in South Africa) were randomized in a 1:1 ratio to conduct either home-based or facility-based contact management. The study will attempt to enroll all eligible close child contacts of infectious drug-sensitive TB index patients diagnosed and treated for TB by one of the study clinics. Child TB contact management, including contact tracing, child evaluation, and TPT initiation and follow-up, will take place in the child's home for the intervention arm and at the clinic for the control arm. The primary outcome is the cluster-level ratio of the number of household child contacts less than 15 years of age in Ethiopia and less than 5 years of age in South Africa initiated on TPT per index patient, comparing the intervention to the control arm. Secondary outcomes include child contact identification and the TB prevention continuum of care. Other implementation outcomes include acceptability, feasibility, fidelity, cost, and cost-effectiveness of the intervention. DISCUSSION: This implementation research trial will determine whether home-based contact management identifies and initiates more household child contacts on TPT than facility-based contact management. TRIAL REGISTRATION: NCT04369326 . Registered on April 30, 2020.

Economic evaluation of implementation science outcomes in low- and middle-income countries: a scoping review.

BACKGROUND: Historically, the focus of cost-effectiveness analyses has been on the costs to operate and deliver interventions after their initial design and launch. The costs related to design and implementation of interventions have often been omitted. Ignoring these costs leads to an underestimation of the true price of interventions and biases economic analyses toward favoring new interventions. This is especially true in low- and middle-income countries (LMICs), where implementation may require substantial up-front investment. This scoping review was conducted to explore the topics, depth, and availability of scientific literature on integrating implementation science into economic evaluations of health interventions in LMICs. METHODS: We searched Web of Science and PubMed for papers published between January 1, 2010, and December 31, 2021, that included components of both implementation science and economic evaluation. Studies from LMICs were prioritized for review, but papers from high-income countries were included if their methodology/findings were relevant to LMIC settings. RESULTS: Six thousand nine hundred eighty-six studies were screened, of which 55 were included in full-text review and 23 selected for inclusion and data extraction. Most papers were theoretical, though some focused on a single disease or disease subset, including: mental health (n = 5), HIV (n = 3), tuberculosis (n = 3), and diabetes (n = 2). Manuscripts included a mix of methodology papers, empirical studies, and other (e.g., narrative) reviews. Authorship of the included literature was skewed toward high-income settings, with 22 of the 23 papers featuring first and senior authors from high-income countries. Of nine empirical studies included, no consistent implementation cost outcomes were measured, and only four could be mapped to an existing costing or implementation framework. There was also substantial heterogeneity across studies in how implementation costs were defined, and the methods used to collect them. CONCLUSION: A sparse but growing literature explores the intersection of implementation science and economic evaluation. Key needs include more research in LMICs, greater consensus on the definition of implementation costs, standardized methods to collect such costs, and identifying outcomes of greatest relevance. Addressing these gaps will result in stronger links between implementation science and economic evaluation and will create more robust and accurate estimates of intervention costs. TRIAL REGISTRATION: The protocol for this manuscript was published on the Open Science Framework. It is available at: https://osf.io/ms5fa/ (DOI: 10.17605/OSF.IO/32EPJ).

Trans-illumination intestine projection imaging of intestinal motility in mice.

Functional intestinal imaging holds importance for the diagnosis and evaluation of treatment of gastrointestinal diseases. Currently, preclinical imaging of intestinal motility in animal models is performed either invasively with excised intestines or noninvasively under anesthesia, and cannot reveal intestinal dynamics in the awake condition. Capitalizing on near-infrared optics and a high-absorbing contrast agent, we report the Trans-illumination Intestine Projection (TIP) imaging system for free-moving mice. After a complete system evaluation, we performed in vivo studies, and obtained peristalsis and segmentation motor patterns of free-moving mice. We show the in vivo typical segmentation motor pattern, that was previously shown in ex vivo studies to be controlled by intestinal pacemaker cells. We also show the effects of anesthesia on motor patterns, highlighting the possibility to study the role of the extrinsic nervous system in controlling motor patterns, which requires unanesthetized live animals. Combining with light-field technologies, we further demonstrated 3D imaging of intestine in vivo (3D-TIP). Importantly, the added depth information allows us to extract intestines located away from the abdominal wall, and to quantify intestinal motor patterns along different directions. The TIP system should open up avenues for functional imaging of the GI tract in conscious animals in natural physiological states.

Classification of runners' performance levels with concurrent prediction of biomechanical parameters using data from inertial measurement units.

Identification of runner's performance level is critical to coaching, performance enhancement and injury prevention. Machine learning techniques have been developed to measure biomechanical parameters with body-worn inertial measurement unit (IMU) sensors. However, a robust method to classify runners is still unavailable. In this paper, we developed two models to classify running performance and predict biomechanical parameters of 30 subjects. We named the models RunNet-CNN and RunNet-MLP based on their architectures: convolutional neural network (CNN) and multilayer perceptron (MLP), respectively. In addition, we examined two validation approaches, subject-wise (leave-one-subject-out) and record-wise. RunNet-MLP classified runner's performance levels with an overall accuracy of 97.1%. Our results also showed that RunNet-CNN outperformed RunNet-MLP and gradient boosting decision tree in predicting biomechanical parameters. RunNet-CNN showed good agreement (R2 > 0.9) with the ground-truth reference on biomechanical parameters. The prediction accuracy for the record-wise method was better than the subject-wise method regardless of biomechanical parameters or models. Our findings showed the viability of using IMUs to produce reliable prediction of runners' performance levels and biomechanical parameters.