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      Learning from COVID-19 to reimagine tuberculosis diagnosis

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          Abstract

          The ongoing COVID-19 mass testing and vaccination campaigns are the result of unprecedented financial investments, rapid research and development, collaborative science, and innovation in delivery systems. It would be a shame to not leverage these systems for other priorities in global health. Before COVID-19, tuberculosis was the leading infectious killer of humans, affecting 10 million people in 2019 and causing 1·4 million deaths. Now, COVID-19 and tuberculosis pose a deadly, dual threat—a syndemic that feeds on social inequities and poverty. 1 The tuberculosis epidemic is worsening because of the COVID-19 pandemic, and most countries have seen big reductions in tuberculosis notifications. Given the massive setback to progress in reaching tuberculosis targets, it is crucial to leverage COVID-19 innovations and systems to improve tuberculosis care and control. Because over 1 million people have found it difficult to get tuberculosis diagnosed during the pandemic, we believe the most urgent priority is case finding. We see many opportunities to use the lessons of COVID-19 to reimagine tuberculosis diagnosis. First, we advocate for sustained use of digital tools for education about tuberculosis. The need for mass dissemination of COVID-19 information and disrupted health services brought on by lockdowns sparked wide uptake of smartphone apps and chatbots for interactive education, risk assessment, referrals, and contact tracing. 2 For example, HealthConnect, a set of interconnected information dissemination, case detection, and case-management services using WhatsApp enabled the Government of South Africa to reach more than 6 million people and health-care workers within the first 7 weeks of deployment. These tools are made freely available and have been customised for WHO and other countries, including Mozambique, Bangladesh, and Australia. Such digital solutions developed for COVID-19 need to be repurposed for tuberculosis. Since tuberculosis and COVID-19 will coexist, there is a huge opportunity for tuberculosis programmes and health-care providers to use these tools for information sharing and patient centred care. Second, we advocate making tuberculosis sample collection easier and simpler. Currently, diagnosis of tuberculosis remains highly reliant on sputum, which is difficult to collect, process, and transport, particularly from children, people living with HIV, and patients with early stage and extrapulmonary disease. COVID-19 demanded rapid and simpler testing options that led to innovation in new sample types and sample collection methods. For example, improved and affordable polyester swabs and new approaches to sampling using saliva, mouthwash, oral swabs, and absorbent strips in face masks have shown promise for COVID-19 sample collection and are now being tried for tuberculosis.3, 4 An easy to obtain sample that also could be used to detect other pathogens (such as SARS-CoV-2) would be revolutionary for tuberculosis. Third, we need to take tuberculosis diagnosis closer to homes. Currently, many tests are available only at the district level or higher, and this forces patients into complex, tedious pathways, with long diagnostic delays. By contrast, every country has improved access to COVID-19 testing. Decentralised testing with drive-through facilities, mobile testing sites, community health-care workers, pharmacies, schools, and workplaces have been effective and well adapted for self-sampling. Within the past 3 months, COVID-19 self-testing kits detecting SARS-CoV-2 antigen and even a single-use PCR test have obtained Emergency Use Authorisation, and other technologies, such as CRISPR, present new opportunities for rapid diagnostics. 5 The investments in infrastructure and innovation suggest a promising future for next generation point-of-care tuberculosis tests. Although urine lipoarabinomannan antigen detection tests are rapidly evolving (with more sensitive products), 6 tuberculosis diagnosis could be revolutionised by even simpler sampling options, such as oral swabs. Fourth, we call for adoption of artificial intelligence imaging systems for tuberculosis and other respiratory infections. COVID-19 has sparked several innovations in artificial intelligence. For example, systems for automated interpretation of chest x-ray images with computer-aided design software. These systems have been under development for tuberculosis for a decade and were quickly reconfigured for COVID-19 within the first months of the pandemic. 7 When combined with improved battery operated, ultra-portable, digital x-ray systems, this technology can be used throughout the health-care system and offer promise for high-throughput screening and integrated COVID-19 and tuberculosis testing. Cough analysers using artificial intelligence and digital stethoscopes with ambient noise cancelling are in early in development but COVID-19 has accelerated innovation that could be re-engineered for tuberculosis and other respiratory diseases. Fifth, we propose exploiting multi-disease molecular technologies. 8 To control COVID-19, countries have needed to scale-up their capacity to run molecular tests. In many settings, this capacity was enabled from pre-existing HIV and tuberculosis programmes that had centralised multi-disease molecular platforms (eg, HIV viral load assays) and could be expanded to meet demand. 9 Similarly, several countries have leveraged automated, cartridge-based molecular technologies (eg, GeneXpert and TrueNAT) for tuberculosis and COVID-19. This wide use of molecular technologies and bi-directional testing will be good for tuberculosis diagnosis and will reduce the reliance on suboptimal tools, such as smear microscopy. Finally, we believe that making data visible can benefit all disease care programmes. COVID-19 is a digitalised disease with real time data aggregation and analysis being used to visualise the pandemic and direct the public health response. By contrast, tuberculosis remains an analogue disease relying on paper-based systems and annual summary reporting. Investments in data systems, connected diagnostics, and use of crowd sourced data offers the opportunity to rethink tuberculosis surveillance and case notification. For example, location history and self-assessment data collected through the Government of India's Aarogya Setu app has been used to accurately forecast COVID-19 hotspots across the country. Vulnerability indices further offers the opportunity to deploy precise and tailored responses. Although these tools come with privacy and data security concerns that must be addressed, 10 the opportunities brought on by COVID-19 big data aggregators should be applied to tuberculosis. In conclusion, COVID-19 proves that tools and solutions can be found when there is investment and collaboration. We hope such efforts will also be made to find products and solutions for tuberculosis, an ancient disease that has caused millions of deaths. The time has come to reimagine tuberculosis care, and COVID-19 can be the blueprint. MR and SC are employees of Foundation of Innovative New Diagnostics (FIND). MP is an advisor to FIND. FIND is a not-for-profit foundation that supports the development, evaluation, and implementation of several diagnostics, including tuberculosis and COVID-19. FIND has product evaluation agreements with several private sector companies that design diagnostics for global health. These agreements strictly define FIND's independence and neutrality regarding the companies whose products get evaluated and describe roles and responsibilities. FIND is co-convener of the Access to COVID-19 Tools Accelerator, a partnership to accelerate access to the diagnostics tools needed in the COVID-19 response.

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          Most cited references9

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          Saliva or Nasopharyngeal Swab Specimens for Detection of SARS-CoV-2

          To the Editor: Rapid and accurate diagnostic tests are essential for controlling the ongoing Covid-19 pandemic. Although the current standard involves testing of nasopharyngeal swab specimens by quantitative reverse-transcriptase polymerase chain reaction (RT-qPCR) to detect SARS-CoV-2, saliva specimens may be an alternative diagnostic sample. 1-4 Rigorous evaluation is needed to determine how saliva specimens compare with nasopharyngeal swab specimens with respect to sensitivity in detection of SARS-CoV-2 during the course of infection. A total of 70 inpatients with Covid-19 provided written informed consent to participate in our study (see the Methods section in Supplementary Appendix 1, available with the full text of this letter at NEJM.org). After Covid-19 was confirmed with a positive nasopharyngeal swab specimen at hospital admission, we obtained additional samples from the patients during hospitalization. We tested saliva specimens collected by the patients themselves and nasopharyngeal swabs collected from the patients at the same time point by health care workers. Using primer sequences from the Centers for Disease Control and Prevention, we detected more SARS-CoV-2 RNA copies in the saliva specimens (mean log copies per milliliter, 5.58; 95% confidence interval [CI], 5.09 to 6.07) than in the nasopharyngeal swab specimens (mean log copies per milliliter, 4.93; 95% CI, 4.53 to 5.33) (Figure 1A, and Fig. S1 in Supplementary Appendix 1). In addition, a higher percentage of saliva samples than nasopharyngeal swab samples were positive up to 10 days after the Covid-19 diagnosis (Figure 1B). At 1 to 5 days after diagnosis, 81% (95% CI, 71 to 96) of the saliva samples were positive, as compared with 71% (95% CI, 67 to 94) of the nasopharyngeal swab specimens. These findings suggest that saliva specimens and nasopharyngeal swab specimens have at least similar sensitivity in the detection of SARS-CoV-2 during the course of hospitalization. Because the results of testing of nasopharyngeal swab specimens to detect SARS-CoV-2 may vary with repeated sampling in individual patients, 5 we evaluated viral detection in matched samples over time. The level of SARS-CoV-2 RNA decreased after symptom onset in both saliva specimens (estimated slope, −0.11; 95% credible interval, −0.15 to −0.06) (Figure 1C) and nasopharyngeal swab specimens (estimated slope, −0.09; 95% credible interval, −0.13 to −0.05) (Figure 1D). In three instances, a negative nasopharyngeal swab specimen was followed by a positive swab at the next collection of a specimen (Figure 1D); this phenomenon occurred only once with the saliva specimens (Figure 1C). During the clinical course, we observed less variation in levels of SARS-CoV-2 RNA in the saliva specimens (standard deviation, 0.98 virus RNA copies per milliliter; 95% credible interval, 0.08 to 1.98) than in the nasopharyngeal swab specimens (standard deviation, 2.01 virus RNA copies per milliliter; 95% credible interval, 1.29 to 2.70) (see Supplementary Appendix 1). Recent studies have shown that SARS-CoV-2 can be detected in the saliva of asymptomatic persons and outpatients. 1-3 We therefore screened 495 asymptomatic health care workers who provided written informed consent to participate in our prospective study, and we used RT-qPCR to test both saliva and nasopharyngeal samples obtained from these persons. We detected SARS-CoV-2 RNA in saliva specimens obtained from 13 persons who did not report any symptoms at or before the time of sample collection. Of these 13 health care workers, 9 had collected matched nasopharyngeal swab specimens by themselves on the same day, and 7 of these specimens tested negative (Fig. S2). The diagnosis in the 13 health care workers with positive saliva specimens was later confirmed in diagnostic testing of additional nasopharyngeal samples by a CLIA (Clinical Laboratory Improvement Amendments of 1988)–certified laboratory. Variation in nasopharyngeal sampling may be an explanation for false negative results, so monitoring an internal control for proper sample collection may provide an alternative evaluation technique. In specimens collected from inpatients by health care workers, we found greater variation in human RNase P cycle threshold (Ct) values in nasopharyngeal swab specimens (standard deviation, 2.89 Ct; 95% CI, 26.53 to 27.69) than in saliva specimens (standard deviation, 2.49 Ct; 95% CI, 23.35 to 24.35). When health care workers collected their own specimens, we also found greater variation in RNase P Ct values in nasopharyngeal swab specimens (standard deviation, 2.26 Ct; 95% CI, 28.39 to 28.56) than in saliva specimens (standard deviation , 1.65 Ct; 95% CI, 24.14 to 24.26) (Fig. S3). Collection of saliva samples by patients themselves negates the need for direct interaction between health care workers and patients. This interaction is a source of major testing bottlenecks and presents a risk of nosocomial infection. Collection of saliva samples by patients themselves also alleviates demands for supplies of swabs and personal protective equipment. Given the growing need for testing, our findings provide support for the potential of saliva specimens in the diagnosis of SARS-CoV-2 infection.
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            Digital technologies in the public-health response to COVID-19

            Digital technologies are being harnessed to support the public-health response to COVID-19 worldwide, including population surveillance, case identification, contact tracing and evaluation of interventions on the basis of mobility data and communication with the public. These rapid responses leverage billions of mobile phones, large online datasets, connected devices, relatively low-cost computing resources and advances in machine learning and natural language processing. This Review aims to capture the breadth of digital innovations for the public-health response to COVID-19 worldwide and their limitations, and barriers to their implementation, including legal, ethical and privacy barriers, as well as organizational and workforce barriers. The future of public health is likely to become increasingly digital, and we review the need for the alignment of international strategies for the regulation, evaluation and use of digital technologies to strengthen pandemic management, and future preparedness for COVID-19 and other infectious diseases.
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              Digital tools against COVID-19: taxonomy, ethical challenges, and navigation aid

              Summary Data collection and processing via digital public health technologies are being promoted worldwide by governments and private companies as strategic remedies for mitigating the COVID-19 pandemic and loosening lockdown measures. However, the ethical and legal boundaries of deploying digital tools for disease surveillance and control purposes are unclear, and a rapidly evolving debate has emerged globally around the promises and risks of mobilising digital tools for public health. To help scientists and policy makers to navigate technological and ethical uncertainty, we present a typology of the primary digital public health applications that are in use. These include proximity and contact tracing, symptom monitoring, quarantine control, and flow modelling. For each, we discuss context-specific risks, cross-sectional issues, and ethical concerns. Finally, recognising the need for practical guidance, we propose a navigation aid for policy makers and other decision makers for the ethical development and use of digital public health tools.
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                Author and article information

                Journal
                Lancet Microbe
                Lancet Microbe
                The Lancet. Microbe
                The Author(s). Published by Elsevier Ltd.
                2666-5247
                19 March 2021
                19 March 2021
                Affiliations
                [a ]Foundation of Innovative New Diagnostics, Geneva, Switzerland
                [b ]Department of Epidemiology and Biostatistics, School of Population and Global Health, McGill University, Montreal, Canada
                Article
                S2666-5247(21)00057-4
                10.1016/S2666-5247(21)00057-4
                7979141
                33778790
                28744e83-9dfb-4088-923b-1af3fc24e3ef
                © 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license

                Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

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