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Tuberculosis is a disease of poverty that claims the lives of over a million people annually. (1) Globally, tuberculosis is concentrated in low- to middle-income countries. The five countries--Brazil, the [...]

Objective: To investigate the prevalence of non‐communicable diseases among household contacts of people with tuberculosis. Methods: We conducted a systematic review and individual participant data meta‐analysis. We searched Medline, Embase and the Global Index Medicus from inception to 16 May 2023. We included studies that assessed for at least one non‐communicable disease among household contacts of people with clinical tuberculosis. We estimated the non‐communicable disease prevalence through mixed effects logistic regression for studies providing individual participant data, and compared it with estimates from aggregated data meta‐analyses. Furthermore, we compared age and sex‐standardised non‐communicable disease prevalence with national‐level estimates standardised for age and sex. Results: We identified 39 eligible studies, of which 14 provided individual participant data (29,194 contacts). Of the remaining 25 studies, 18 studies reported aggregated data suitable for aggregated data meta‐analysis. In individual participant data analysis, the pooled prevalence of diabetes in studies that undertook biochemical testing was 8.8% (95% confidence interval [CI], 5.1%–14.9%, four studies). Age‐and sex‐standardised prevalence was higher in two studies (10.4% vs. 6.9% and 11.5% vs. 8.4%) than the corresponding national estimates and similar in two studies. Prevalence of diabetes mellitus based on self‐report or medical records was 3.4% (95% CI 2.6%–4.6%, 14 studies). Prevalence did not significantly differ compared to estimates from aggregated data meta‐analysis. There were limited data for other non‐communicable diseases. Conclusion: The prevalence of diabetes mellitus among household contacts was high while that of known diabetes was substantially lower, suggesting the underdiagnosis. tuberculosis household contact investigation offers opportunities to deliver multifaceted interventions to identify tuberculosis infection and disease, screen for non‐communicable diseases and address shared risk factors. [ABSTRACT FROM AUTHOR]

Background: In 2022, fewer than half of persons with tuberculosis (TB) had access to molecular diagnostic tests for TB due to their high costs. Studies have found that the use of artificial intelligence (AI) software for chest X-ray (CXR) interpretation and sputum specimen pooling can each reduce the cost of testing. We modeled the combination of both strategies to estimate potential savings in consumables that could be used to expand access to molecular diagnostics. Methods: We obtained Xpert testing and positivity data segmented into deciles by AI probability scores for TB from the community- and healthcare facility-based active case finding conducted in Bangladesh, Nigeria, Viet Nam, and Zambia. AI scores in the model were based on CAD4TB version 7 (Zambia) and qXR (all other countries). We modeled four ordinal screening and testing approaches involving AI-aided CXR interpretation to indicate individual and pooled testing. Setting a false negative rate of 5%, for each approach we calculated additional and cumulative savings over the baseline of universal Xpert testing, as well as the theoretical expansion in diagnostic coverage. Results: In each country, the optimal screening and testing approach was to use AI to rule out testing in deciles with low AI scores and to guide pooled vs individual testing in persons with moderate and high AI scores, respectively. This approach yielded cumulative savings in Xpert tests over baseline ranging from 50.8% in Zambia to 57.5% in Nigeria and 61.5% in Bangladesh and Viet Nam. Using these savings, diagnostic coverage theoretically could be expanded by 34% to 160% across the different approaches and countries. Conclusions: Using AI software data generated during CXR interpretation to inform a differentiated pooled testing strategy may optimize TB diagnostic test use, and could extend molecular tests to more people who need them. The optimal AI thresholds and pooled testing strategy varied across countries, which suggests that bespoke screening and testing approaches may be needed for differing populations and settings. [ABSTRACT FROM AUTHOR]

Recently, the number of artificial intelligence powered computer-aided detection (CAD) products that detect tuberculosis (TB)-related abnormalities from chest X-rays (CXR) available on the market has increased. Although CXR is a relatively effective and inexpensive method for TB screening and triaging, a shortage of skilled radiologists in many high TB-burden countries limits its use. CAD technology offers a solution to this problem. Before adopting a CAD product, TB programmes need to consider not only the diagnostic accuracy but also implementation-relevant features including operational characteristics, deployment mechanism, input and machine compatibility, output format, options for integration into the legacy system, costs, data sharing and privacy aspects, and certification. A landscaping analysis was conducted to collect this information among CAD developers known to have or soon to have a TB product. The responses were reviewed and finalized with the developers, and are published on an open-access website: www.ai4hlth.org. CAD products are constantly being improved and the site will continuously be updated to account for updates and new products. This unique online resource aims to inform the TB community about available CAD tools, their features and set-up procedures, to enable TB programmes to identify the most suitable product to incorporate in interventions. [ABSTRACT FROM AUTHOR]

In the original publication of this article [1], an author's name needs to be revised from Jacob Creswel to Jacob Creswell. [ABSTRACT FROM AUTHOR]

Current strategies to promptly, effectively, and equitably screen people with tuberculosis (TB) and link them to diagnosis and care are insufficient; new approaches are required to find the millions of people around the world with TB who are missed each year. Interventions also need to be designed considering how people interact with health care facilities and where appropriate should be suitable for use in the community. Here, the historical, new, and reemerging technologies that are being utilised for TB diagnosis globally are discussed, whilst highlighting that how we use and evaluate tests is just as important as the tests themselves. [ABSTRACT FROM AUTHOR]

Deep learning (DL) neural networks have only recently been employed to interpret chest radiography (CXR) to screen and triage people for pulmonary tuberculosis (TB). No published studies have compared multiple DL systems and populations. We conducted a retrospective evaluation of three DL systems (CAD4TB, Lunit INSIGHT, and qXR) for detecting TB-associated abnormalities in chest radiographs from outpatients in Nepal and Cameroon. All 1196 individuals received a Xpert MTB/RIF assay and a CXR read by two groups of radiologists and the DL systems. Xpert was used as the reference standard. The area under the curve of the three systems was similar: Lunit (0.94, 95% CI: 0.93–0.96), qXR (0.94, 95% CI: 0.92–0.97) and CAD4TB (0.92, 95% CI: 0.90–0.95). When matching the sensitivity of the radiologists, the specificities of the DL systems were significantly higher except for one. Using DL systems to read CXRs could reduce the number of Xpert MTB/RIF tests needed by 66% while maintaining sensitivity at 95% or better. Using a universal cutoff score resulted different performance in each site, highlighting the need to select scores based on the population screened. These DL systems should be considered by TB programs where human resources are constrained, and automated technology is available. [ABSTRACT FROM AUTHOR]

Background: In Asia, over 50% of patients with symptoms of tuberculosis (TB) access health care from private providers. These patients are usually not notified to the National TB Control Programs, which contributes to low notification rates in many countries.Methods: From January 1, 2011 to December 31, 2012, Karachi's Indus Hospital - a private sector partner to the National TB Programme - engaged 80 private family clinics in its catchment area in active case finding using health worker incentives to increase notification of TB disease. The costs incurred were estimated from the perspective of patients, health facility and the program providing TB services. A Markov decision tree model was developed to calculate the cost-effectiveness of the active case finding as compared to case detection through the routine passive TB centers. Pakistan has a large private health sector, which can be mobilized for TB screening using an incentivized active case finding strategy. Currently, TB screening is largely performed in specialist public TB centers through passive case finding. Active and passive case finding strategies are assumed to operate independently from each other.Results: The incentive-based active case finding program costed USD 223 per patient treated. In contrast, the center based non-incentive arm was 23.4% cheaper, costing USD 171 per patient treated. Cost-effectiveness analysis showed that the incentive-based active case finding program was more effective and less expensive per DALY averted when compared to the baseline passive case finding as it averts an additional 0.01966 DALYs and saved 15.74 US$ per patient treated.Conclusion: Both screening strategies appear to be cost-effective in an urban Pakistan context. Incentive driven active case findings of TB in the private sector costs less and averts more DALYs per health seeker than passive case finding, when both alternatives are compared to a common baseline situation of no screening. [ABSTRACT FROM AUTHOR]