How artificial intelligence may help the Covid-19 pandemic: Pitfalls and lessons for the future.

The clinical severity, rapid transmission and human losses due to coronavirus disease 2019 (Covid-19) have led the World Health Organization to declare it a pandemic. Traditional epidemiological tools are being significantly complemented by recent innovations especially using artificial intelligence (AI) and machine learning. AI-based model systems could improve pattern recognition of disease spread in populations and predictions of outbreaks in different geographical locations. A variable and a minimal amount of data are available for the signs and symptoms of Covid-19, allowing a composite of maximum likelihood algorithms to be employed to enhance the accuracy of disease diagnosis and to identify potential drugs. AI-based forecasting and predictions are expected to complement traditional approaches by helping public health officials to select better response and preparedness measures against Covid-19 cases. AI-based approaches have helped address the key issues but a significant impact on the global healthcare industry is yet to be achieved. The capability of AI to address the challenges may make it a key player in the operation of healthcare systems in future. Here, we present an overview of the prospective applications of the AI model systems in healthcare settings during the ongoing Covid-19 pandemic.

Responses to COVID-19 in South Asian Association for Regional Cooperation (SAARC) countries in 2020, a data analysis during a world of crises.

Coronavirus disease (COVID-19) caused by SARS-CoV-2 was notified from Wuhan city, Hubei province, China in the mid of December 2019. The disease is showing dynamic change in the pattern of confirmed cases and death toll in these low and middle-income countries (LMICs). In this study, exponential growth (EG) method was used to calculate the real-time reproductive number (Rt) for initial and later stage of epidemic in South Asian Association for Regional Cooperation (SAARC) member countries (April 2020 - December 2020). Time dependent (TD) method was used to calculate the weekly real -time reproduction number (Rt). We also presented the observations on COVID-19 epidemiology in relation with the health expenditure, poverty, BCG vaccination, literacy population density and Rt for understanding the current scenario, trends, and expected outcome of the disease in SAARC countries. A significant positive correlation was noticed between COVID-19 deaths and health expenditure (% GDP) (r = 0.58, P < 0.05). The other factors such as population density/sq km, literacy %, adult population %, and poverty % were not significantly correlated with number of COVID-19 cases and deaths. Among SAARC countries, the highest Rt was observed in India (Rt = 2.10; 95% CI 2.04-2.17) followed by Bangladesh (Rt = 1.62; 95% CI 1.59-1.64) in initial state of epidemic. A continuous monitoring is necessitated in all countries looking at the medical facilities, available infrastructure and healthcare manpower, constraints which may appear with increased number of critically ill patients if the situation persists longer.

Implementing a chest X-ray artificial intelligence tool to enhance tuberculosis screening in India: Lessons learned.

Artificial Intelligence (AI) based chest X-ray (CXR) screening for tuberculosis (TB) is becoming increasingly popular. Still, deploying such AI tools can be challenging due to multiple real-life barriers like software installation, workflow integration, network connectivity constraints, limited human resources available to interpret findings, etc. To understand these challenges, PATH implemented a TB REACH active case-finding program in a resource-limited setting of Nagpur in India, where an AI software device (qXR) intended for TB screening using CXR images was used. Eight private CXR laboratories that fulfilled prerequisites for AI software installation were engaged for this program. Key lessons about operational feasibility and accessibility, along with the strategies adopted to overcome these challenges, were learned during this program. This program also helped to screen 10,481 presumptive TB individuals using informal providers based on clinical history. Among them, 2,303 individuals were flagged as presumptive for TB by a radiologist or by AI based on their CXR interpretation. Approximately 15.8% increase in overall TB yield could be attributed to the presence of AI alone because these additional cases were not deemed presumptive for TB by radiologists, but AI was able to identify them. Successful implementation of AI tools like qXR in resource-limited settings in India will require solving real-life implementation challenges for seamless deployment and workflow integration.

Digital Storytelling and Community Engagement to Find Missing TB Cases in Rural Nuh, India.

Nuh, Haryana, is one of India's least developed districts. To improve TB case notifications, ZMQ carried out an active case-finding (ACF) intervention conducted by community health workers (MIRAs) using a digital TB storytelling platform to create TB awareness in the community. The combined storytelling and ACF intervention were conducted house-to-house or in community group settings. Steps included (A) the development of digital TB awareness-raising stories using a participatory approach called Story Labs; (B) the implementation of the intervention; and (C) process, outcome, and impact evaluation of these activities. Six digital stories were created and used during ACF in which 19,345 people were screened and 255 people were diagnosed with TB. Of 731 participants surveyed, the stories were well received and resulted in an increase in TB knowledge. ACF activities resulted in a 56% increase in bacteriologically confirmed TB and an 8% decrease in all forms of TB compared to baseline. All form notifications may have been impacted by COVID-19 lockdowns. Digital TB storytelling can improve TB awareness and knowledge, particularly for low-literacy populations. The use of these tools may benefit ACF campaigns and improve TB case finding.

Adaptations to the first wave of the COVID-19 pandemic by private sector tuberculosis care providers in India.

Background: India's dominant private healthcare sector is the destination for 60-85% of initial tuberculosis care-seeking. The COVID-19 pandemic in India drastically affected TB case notifications in the first half of 2020. In this survey, we assessed the impact of the first wave of COVID-19 in India on private providers, and changes they adopted in their practice due to the pandemic. Methods: The Joint Effort for Elimination of TB (JEET) is a nationwide Global Fund project implemented across 406 districts in 23 states to extend quality TB services to patients seeking care in private sector. We conducted a rapid survey of 11% (2,750) of active providers engaged under JEET's intense Patient Provider Support Agency (PPSA) model across 15 Indian states in Q1 (February-March) of 2021. Providers were contacted in person or telephonically, and consenting participants were interviewed using a web-based survey tool. Responses from participants were elicited on their practice before COVID-19, during the 2020 lockdowns (March-April 2020) and currently (Q1 2021). Data were adjusted for survey design and non-response, and results were summarised using descriptive statistics and logistic regression. Results: Of the 2,750 providers sampled, 2,011 consented and were surveyed (73 % response). Nearly 50 % were between 30 and 45 years of age, and 51 % were from Uttar Pradesh, Maharashtra and Gujarat. Seventy percent of providers reported reduced daily out-patient numbers in Q1 2021 compared to pre-COVID times. During the lockdown, 898 (40 %) of providers said their facilities were closed, while 323 (11 %) offered limited services including teleconsultation. In Q1 2021, 88 % of provider facilities were fully open, with 10 % providing adjusted services, and 4 % using teleconsultation. Only 2 % remained completely closed. Majority of the providers (92 %) reported not experiencing any delays in TB testing in Q1 2021 compared to pre-COVID times. Only 6 % reported raising costs at their clinic, mostly to cover personal protective equipment (PPE) and other infection control measures, although 60-90 % implemented various infection control measures. Thirty-three percent of TB providers were ordering COVID-19 testing, in addition to TB testing.To adapt, 82% of survey providers implemented social distancing and increased timing between appointments and 83% started conducting temperature checks, with variation by state and provider type, while 89% adopted additional sanitation measures in their facilities. Furthermore, 62% of providers started using PPE, and 13% made physical changes (air filters, isolation of patient areas) to their clinic to prevent infection. Seventy percent of providers stated that infection control measures could decrease TB transmission. Conclusion: Although COVID-19 restrictions resulted in significant declines in patient turn-out at private facilities, our analysis showed that most providers were open and costs for TB care remained mostly the same in Q1 2021. As result of the COVID-19 pandemic, several positive strategies have been adapted by the private sector TB care providers. Since the subsequent COVID-19 waves were more severe or widespread, additional work is needed to assess the impact of the pandemic on the private health sector.

Engaging Private Health Care Providers to Identify Individuals with TB in Nepal.

In Nepal, 47% of individuals who fell ill with TB were not reported to the National TB Program in 2018. Approximately 60% of persons with TB initially seek care in the private sector. From November 2018 to January 2020, we implemented an active case finding intervention in the Parsa and Dhanusha districts targeting private provider facilities. To evaluate the impact of the intervention, we reported on crude intervention results. We further compared case notification during the implementation to baseline and control population (Bara and Siraha) notifications. We screened 203,332 individuals; 11,266 (5.5%) were identified as presumptive for TB and 8077 (71.7%) were tested for TB. Approximately 8% had a TB diagnosis, of whom 383 (56.2%) were bacteriologically confirmed (Bac+). In total, 653 (95.7%) individuals were initiated on treatment at DOTS facilities. For the intervention districts, there was a 17%increase for bacteriologically positive TB and 10% for all forms TB compared to baseline. In comparison, the change in notifications in the control population were 4% for bacteriologically positive, and -2% all forms. Through engagement of private sector facilities, our intervention was able to increase the number of individuals identified with TB by over 10% in the Parsa and Dhanusha districts.

Economic Evaluation of Community Tuberculosis Active Case-Finding Approaches in Cambodia: A Quasi-Experimental Study.

This study aimed to estimate the costs and incremental cost-effectiveness of two community-based tuberculosis (TB) active case-finding (ACF) strategies in Cambodia. We also assessed the number needed to screen and test to find one TB case. Program and national TB notification data from a quasi-experimental study of a cohort of people with TB in 12 intervention operational districts (ODs) and 12 control ODs between November 2018 and December 2019 were analyzed. Two ACF interventions (ACF seed-and-recruit (ACF SAR) model and one-off roving (one-off) ACF) were implemented concurrently. The matched control sites included PCF only. We estimated costs using the program and published data in Cambodia. The primary outcome was disability-adjusted life years (DALY) averted over 14 months. We considered the gross domestic product per capita of Cambodia in 2018 as the cost-effectiveness threshold. ACF SAR needed to test 7.7 people with presumptive TB to identify one all-forms TB, while one-off ACF needed to test 22.4. The costs to diagnose one all-forms TB were USD 458 (ACF SAR) and USD 191 (one-off ACF). The incremental cost per DALY averted was USD 257 for ACF SAR and USD 204 for one-off ACF. Community-based ACF interventions that targeted key populations for TB in Cambodia were highly cost-effective.

Factors Affecting Motivation among Key Populations to Engage with Tuberculosis Screening and Testing Services in Northwest Tanzania: A Mixed-Methods Analysis.

In northwest Tanzania, many artisanal small-scale miners (ASMs) and female sex workers (FSWs) live in informal communities surrounding mines where tuberculosis (TB) is highly prevalent. An active case finding (ACF) intervention to increase TB case notification was undertaken in two districts. Alongside this, a study was implemented to understand engagement with the intervention through: (1) quantitative questionnaires to 128 ASMs and FSWs, who either engaged or did not engage in the ACF intervention, to assess their views on TB; (2) qualitative interviews with 41 ASMs and FSWs, 36 community health workers (CHWs) and 30 community stakeholders. The mean perceived severity of TB score was higher in the engaged than in the non-engaged group (p = 0.01). Thematic analysis showed that health-seeking behaviour was similar across both groups but that individuals in the non-engaged group were more reluctant to give sputum samples, often because they did not understand the purpose. CHWs feared contracting TB on the job, and many noted that mining areas were difficult to access without transportation. Community stakeholders provided various recommendations to increase engagement. This study highlights reasons for engagement with a large-scale ACF intervention targeting key populations and presents insights from implementers and stakeholders on the implementation of the intervention.

Islands of Tuberculosis Elimination: An Evaluation of Community-Based Active Case Finding in North Sumatra, Indonesia.

Community-based active case finding (ACF) is needed to reach key/vulnerable populations with limited access to tuberculosis (TB) care. Published reports of ACF interventions in Indonesia are scarce. We conducted an evaluation of a multicomponent community-based ACF intervention as it scaled from one district to nine in Nias and mainland North Sumatra. Community and health system support measures including laboratory strengthening, political advocacy, sputum transport, and community awareness were instituted. ACF was conducted in three phases: pilot (18 months, 1 district), intervention (12 months, 4 districts) and scale-up (9 months, 9 districts). The pilot phase identified 215 individuals with bacteriologically positive (B+) TB, representing 42% of B+ TB notifications. The intervention phase yielded 509, representing 54% of B+ notifications and the scale-up phase identified 1345 individuals with B+ TB (56% of notifications). We observed large increases in B+ notifications on Nias, but no overall change on the mainland despite district variation. Overall, community health workers screened 377,304 individuals of whom 1547 tested positive, and 95% were initiated on treatment. Our evaluation shows that multicomponent community-based ACF can reduce the number of people missed by TB programs. Community-based organizations are best placed for accessing and engaging hard to reach populations and providing integrated support which can have a large positive effect on TB notifications.

Mobilising community networks for early identification of tuberculosis and treatment initiation in Cambodia: an evaluation of a seed-and-recruit model.

BACKGROUND AND OBJECTIVES: The effects of active case finding (ACF) models that mobilise community networks for early identification and treatment of tuberculosis (TB) remain unknown. We investigated and compared the effect of community-based ACF using a seed-and-recruit model with one-off roving ACF and passive case finding (PCF) on the time to treatment initiation and identification of bacteriologically confirmed TB. METHODS: In this retrospective cohort study conducted in 12 operational districts in Cambodia, we assessed relationships between ACF models and: 1) the time to treatment initiation using Cox proportional hazards regression; and 2) the identification of bacteriologically confirmed TB using modified Poisson regression with robust sandwich variance. RESULTS: We included 728 adults with TB, of whom 36% were identified via the community-based ACF using a seed-and-recruit model. We found community-based ACF using a seed-and-recruit model was associated with shorter delay to treatment initiation compared to one-off roving ACF (hazard ratio 0.81, 95% CI 0.68-0.96). Compared to one-off roving ACF and PCF, community-based ACF using a seed-and-recruit model was 45% (prevalence ratio (PR) 1.45, 95% CI 1.19-1.78) and 39% (PR 1.39, 95% CI 0.99-1.94) more likely to find and detect bacteriologically confirmed TB, respectively. CONCLUSION: Mobilising community networks to find TB cases was associated with early initiation of TB treatment in Cambodia. This approach was more likely to find bacteriologically confirmed TB cases, contributing to the reduction of risk of transmission within the community.

User experience and patient satisfaction with tuberculosis care in low- and middle-income countries: A systematic review.

BACKGROUND: Patient-centered care is at the forefront of the End TB strategy, yet little is known about user (patient's) experience and patient satisfaction with TB services. Our study aims to systematically review quantitative studies evaluating user experience and TB patient satisfaction within the health care system. METHODS: Five medical databases were systematically searched between January 1st, 2009 and December 31st, 2018. English studies assessing user experience and patient satisfaction within the healthcare system from a TB patient's perspective in low and middle-income countries, were included. RESULTS: Thirty-five studies from 16 low and middle-income countries evaluated three major themes; facilities and patient centeredness (n = 23), patient-provider relationship (n = 22) and overall satisfaction (n = 19). Overall study quality was low as they used varying tools to measure user experience and patient satisfaction. CONCLUSION: Our study shows large variability in measurement of user experiences and patient satisfaction. Studies reported that patients were mostly satisfied with TB care services, and those that were dissatisfied were substantially more likely to be lost to follow-up. The high satisfaction rates could have been due to lack of education on good quality patient care or fear of losing access to health care. A standardized patient centered tool could be designed to help assess user experience and patient satisfaction to allow comparisons among health systems and countries.

Deep learning, computer-aided radiography reading for tuberculosis: a diagnostic accuracy study from a tertiary hospital in India.

In general, chest radiographs (CXR) have high sensitivity and moderate specificity for active pulmonary tuberculosis (PTB) screening when interpreted by human readers. However, they are challenging to scale due to hardware costs and the dearth of professionals available to interpret CXR in low-resource, high PTB burden settings. Recently, several computer-aided detection (CAD) programs have been developed to facilitate automated CXR interpretation. We conducted a retrospective case-control study to assess the diagnostic accuracy of a CAD software (qXR, Qure.ai, Mumbai, India) using microbiologically-confirmed PTB as the reference standard. To assess overall accuracy of qXR, receiver operating characteristic (ROC) analysis was used to determine the area under the curve (AUC), along with 95% confidence intervals (CI). Kappa coefficients, and associated 95% CI, were used to investigate inter-rater reliability of the radiologists for detection of specific chest abnormalities. In total, 317 cases and 612 controls were included in the analysis. The AUC for qXR for the detection of microbiologically-confirmed PTB was 0.81 (95% CI: 0.78, 0.84). Using the threshold that maximized sensitivity and specificity of qXR simultaneously, the software achieved a sensitivity and specificity of 71% (95% CI: 66%, 76%) and 80% (95% CI: 77%, 83%), respectively. The sensitivity and specificity of radiologists for the detection of microbiologically-confirmed PTB was 56% (95% CI: 50%, 62%) and 80% (95% CI: 77%, 83%), respectively. For detection of key PTB-related abnormalities 'pleural effusion' and 'cavity', qXR achieved an AUC of 0.94 (95% CI: 0.92, 0.96) and 0.84 (95% CI: 0.82, 0.87), respectively. For the other abnormalities, the AUC ranged from 0.75 (95% CI: 0.70, 0.80) to 0.94 (95% CI: 0.91, 0.96). The controls had a high prevalence of other lung diseases which can cause radiological manifestations similar to PTB (e.g., 26% had pneumonia, 15% had lung malignancy, etc.). In a tertiary hospital in India, qXR demonstrated moderate sensitivity and specificity for the detection of PTB. There is likely a larger role for CAD software as a triage test for PTB at the primary care level in settings where access to radiologists in limited. Larger prospective studies that can better assess heterogeneity in important subgroups are needed.

BCG vaccine: a hope to control COVID-19 pandemic amid crisis.

COVID-19 caused by the virus SARS-CoV-2 has gripped essentially all countries in the world, and has infected millions and killed hundreds of thousands of people. Several innovative approaches are in development to restrain the spread of SARS-CoV-2. In particular, BCG, a vaccine against tuberculosis (TB), is being considered as an alternative therapeutic modality. BCG vaccine is known to induce both humoral and adaptive immunities, thereby activating both nonspecific and cross-reactive immune responses in the host, which combined could effectively resist other pathogens including SARS-CoV-2. Notably, some studies have revealed that SARS-CoV-2 infectivity, case positivity, and mortality rate have been higher in countries that have not adopted BCG vaccination than in countries that have done so. This review presents an overview of the concepts underlying BCG vaccination and its nonspecific immuological effects and protection, resulting in 'trained immunity' and potential utility for resisting COVID-19.

N-TB: A mobile-based application to simplify nutritional assessment, counseling and care of patients with tuberculosis in India.

Undernutrition is the most prevalent comorbidity in patients with tuberculosis (TB) in India. Undernutrition is often severe and results in higher risk of death, drug toxicity during treatment, poor functional status at end of treatment and a higher risk of relapse after successful treatment. A World Health Organization guideline has recommended nutritional assessment, counseling, and care as integral parts of TB care. The Revised National Tuberculosis Control Programme has recognized undernutrition as a significant comorbidity, released a guidance document for improving nutritional care and support, and launched a scheme for direct bank transfer of monthly cash benefit to TB patients. However, there are gaps at the provider level on nutritional assessment, due to challenges in calculation and interpretation of body mass index (BMI). A mobile based application has been developed for use in the programme, which makes estimation of BMI possible, classifies the severity of undernutrition, suggests triage and clinical actions based on the BMI, indicates desirable body weight corresponding to a BMI of 21 kg/m2, and the daily caloric and protein intake for underweight patients with active TB. The app also provides tips for dietary counseling for TB patients, information on the major food groups, emphasizes an adequate and balanced diet from locally available foods for nutritional recovery of TB patients.

Prevalence of diabetes mellitus amongst hospitalized tuberculosis patients at an Indian tertiary care center: A descriptive analysis.

BACKGROUND: India has a high prevalence of tuberculosis (TB) as well as diabetes mellitus (DM). DM is a chronic disease caused by deficiency of insulin production by the pancreas. The risk of TB amongst DM patients is three times higher than those without. The estimated national prevalence of DM is 7.3%. Despite the growing burden of DM, there are limited studies describing the prevalence of TB-DM in India. OBJECTIVE: Our study estimated the prevalence of DM amongst adult hospitalized TB patients at Kasturba Hospital, Manipal and determined factors associated with the likelihood of DM-TB co-prevalence. METHODS: We conducted a retrospective cohort study at Kasturba Hospital, Manipal Academy of Higher Education. All hospitalized adult patients diagnosed with pulmonary TB (PTB) and extrapulmonary TB (EPTB) between June 1st 2015 and June 30th 2016 were eligible for inclusion. Pediatric and pregnant TB patients were excluded from our study. Data were extracted from medical charts. Descriptive and multivariate analyses were performed in R. Multivariate analysis adjusted for age, gender, type of TB, history of TB, and nutrition (body mass index (BMI)) status. RESULTS: A total of 728 patients met the eligibility criteria, 517 (71%) were male, 210 (29%) female, 406 (56%) had PTB and 322 (44%) had EPTB. Amongst those with a nutritional status, 36 (30%) patients were underweight (BMI <18.4 kg/m2), 73 (40%) had a normal BMI (18.5kg/m2-24.9 kg/m2), 15 (8%) were overweight (BMI 25.0 kg/m2-29.9 kg/m2) and 9 (5%) were obese (BMI >30.0 kg/m2). A total of 720 (98.9%) of TB patients had at least one blood sugar test result. The overall prevalence of DM (n = 184) amongst TB patients was 25.3% (95% CI 22.2%, 28.6%). When stratified, it was 35.0% (30.4%, 39.9%) and 13.0% (9.7%, 17.3%) amongst PTB and EPTB patients respectively. TB patients aged 41-60 years had 3.51 times higher odds (aOR 3.51 (2.08, 6.07)) of having DM than patients 40 years or younger. Patients aged 60 years or older had 2.49 times higher odds (aOR 2.49 (1.28, 4.85)) of having DM than younger patients (<40 years). Females had lower odds (aOR 0.80 (0.46, 1.37)) of developing DM than male TB patients and patients with a history of TB had lower odds (aOR 0.73 (0.39, 1.32)) than newly diagnosed TB patients. Additionally, EPTB patients had significantly lower odds (aOR 0.26 (0.15, 0.43)) compared to PTB patients. Underweight patients also had significantly lower odds (aOR 0.25 (0.14, 0.42)) of having DM than normal weight patients. CONCLUSION: Our study found a higher prevalence of TB-DM than the national average. TB-DM co-prevalence was significantly associated with age, type of TB and undernutrition. As India's DM prevalence is expected to rise, TB-DM will become an increasingly important part of the TB epidemic requiring specialized study and care.

A bibliometric analysis of tuberculosis research, 2007-2016.

BACKGROUND: Tuberculosis (TB) research is a key component of the End TB Strategy. To track research output, we conducted a bibliometric analysis of TB research from the past decade. METHODS: The Web of Science database was searched for publications from January 2007 to December 2016 with "tuberculosis" in the title. References were analysed using the R bibliometrix package. A year-stratified 5% random subset was drawn to extract funding sources and identify research areas. FINDINGS: The annual growth rate of publications was 7.3%, and was highest (13.1%) among Brazil, Russia, India, China and South Africa (BRICS). The USA was the most productive country, with 18.4% of references, followed by India (9.7%), China (7.3%), England (6.5%), and South Africa (3.9%). In the subset analysis, the most common research area was 'fundamental research' (33.8%). Frequently acknowledged funders were US and EU-based, with China and India emerging as top funders. Collaborations appeared more frequently between high-income countries and low/medium income countries (LMICs), with fewer collaborations among LMICs. CONCLUSION: The past decade has seen a continued increase in TB publications. While USA continues to dominate research output and funding, BRICS countries have emerged as major research producers and funders. Collaborations among BRICS would enhance future TB research productivity.

Market penetration of Xpert MTB/RIF in high tuberculosis burden countries: A trend analysis from 2014 - 2016.

Background: Xpert® MTB/RIF, a rapid tuberculosis (TB) molecular test, was endorsed by the World Health Organization in 2010. Since then, 34.4 million cartridges have been procured under concessional pricing. Although the roll out of this diagnostic is promising, previous studies showed low market penetration. Methods: To assess 3-year trends of market penetration of Xpert MTB/RIF in the public sector, smear and Xpert MTB/RIF volumes for the year 2016 were evaluated and policies from 2014-2016 within 22 high-burden countries (HBCs) were studied. A structured questionnaire was sent to representatives of 22 HBCs. The questionnaires assessed the total smear and Xpert MTB/RIF volumes, number of modules and days of operation of GeneXpert machines in National TB Programs (NTPs). Data regarding the use of NTP GeneXpert machines for other diseases and GeneXpert procurement by other disease control programs were collected. Market penetration was estimated by the ratio of total sputum smear volume for initial diagnosis divided by the number of Xpert MTB/RIF tests procured in the public sector. Results: The survey response rate was 21/22 (95%). Smear/Xpert ratios decreased in 17/21 countries and increased in four countries, since 2014. The median ratio decreased from 32.6 (IQR: 44.6) in 2014 to 6.0 (IQR: 15.4) in 2016. In 2016, the median GeneXpert utilization was 20%, however seven countries (7/19; 37%) were running tests for other diseases on their NTP-procured GeneXpert systems in 2017, such as HIV, hepatitis-C virus (HCV), Chlamydia trachomatis, and Neisseria gonorrhoeae. Five (5/15; 33%) countries reported GeneXpert procurement by HIV or HCV programs in 2016 and/or 2017. Conclusions: Our results show a positive trend for Xpert MTB/RIF market penetration in 21 HBC public sectors. However, GeneXpert machines were under-utilized for TB, and inadequately exploited as a multi disease technology.