Tuberculosis (TB) Aftermath: study protocol for a hybrid type I effectiveness-implementation non-inferiority randomized trial in India comparing two active case finding (ACF) strategies among individuals treated for TB and their household contacts.

BACKGROUND: Approximately 7% of all reported tuberculosis (TB) cases each year are recurrent, occurring among people who have had TB in the recent or distant past. TB recurrence is particularly common in India, which has the largest TB burden worldwide. Although patients recently treated for TB are at high risk of developing TB again, evidence around effective active case finding (ACF) strategies in this population is scarce. We will conduct a hybrid type I effectiveness-implementation non-inferiority randomized trial to compare the effectiveness, cost-effectiveness, and feasibility of two ACF strategies among individuals who have completed TB treatment and their household contacts (HHCs). METHODS: We will enroll 1076 adults (≥ 18 years) who have completed TB treatment at a public TB unit (TU) in Pune, India, along with their HHCs (averaging two per patient, n = 2152). Participants will undergo symptom-based ACF by existing healthcare workers (HCWs) at 6-month intervals and will be randomized to either home-based ACF (HACF) or telephonic ACF (TACF). Symptomatic participants will undergo microbiologic testing through the program. Asymptomatic HHCs will be referred for TB preventive treatment (TPT) per national guidelines. The primary outcome is rate per 100 person-years of people diagnosed with new or recurrent TB by study arm, within 12 months following treatment completion. The secondary outcome is proportion of HHCs < 6 years, by study arm, initiated on TPT after ruling out TB disease. Study staff will collect socio-demographic and clinical data to identify risk factors for TB recurrence and will measure post-TB lung impairment. In both arms, an 18-month "mop-up" visit will be conducted to ascertain outcomes. We will use the RE-AIM framework to characterize implementation processes and explore acceptability through in-depth interviews with index patients, HHCs and HCWs (n = 100). Cost-effectiveness will be assessed by calculating the incremental cost per TB case detected within 12 months and projected for disability-adjusted life years averted based on modeled estimates of morbidity, mortality, and time with infectious TB. DISCUSSION: This novel trial will guide India's scale-up of post-treatment ACF and provide an evidence base for designing strategies to detect recurrent and new TB in other high burden settings. TRIAL REGISTRATION: NCT04333485 , registered April 3, 2020. CTRI/2020/05/025059 [Clinical Trials Registry of India], registered May 6 2020.

Scaling Up TB Screening and TB Preventive Treatment Globally: Key Actions and Healthcare Service Costs.

The 2018 United Nations High-Level Meeting on Tuberculosis (UNHLM) set targets for case detection and TB preventive treatment (TPT) by 2022. However, by the start of 2022, about 13.7 million TB patients still needed to be detected and treated, and 21.8 million household contacts needed to be given TPT globally. To inform future target setting, we examined how the 2018 UNHLM targets could have been achieved using WHO-recommended interventions for TB detection and TPT in 33 high-TB burden countries in the final year of the period covered by the UNHLM targets. We used OneHealth-TIME model outputs combined with the unit cost of interventions to derive the total costs of health services. Our model estimated that, in order to achieve UNHLM targets, >45 million people attending health facilities with symptoms would have needed to be evaluated for TB. An additional 23.1 million people with HIV, 19.4 million household TB contacts, and 303 million individuals from high-risk groups would have required systematic screening for TB. The estimated total costs amounted to ~USD 6.7 billion, of which ~15% was required for passive case finding, ~10% for screening people with HIV, ~4% for screening household contacts, ~65% for screening other risk groups, and ~6% for providing TPT to household contacts. Significant mobilization of additional domestic and international investments in TB healthcare services will be needed to reach such targets in the future.

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.

Pooled testing of sputum with Xpert MTB/RIF and Xpert Ultra during tuberculosis active case finding campaigns in Lao People's Democratic Republic.

INTRODUCTION: Active case finding (ACF) of individuals with tuberculosis (TB) is a key intervention to find the 30% of people missed every year. However, ACF requires screening large numbers of individuals who have a low probability of positive results, typically <5%, which makes using the recommended molecular tests expensive. METHODS: We conducted two ACF surveys (in 2020 and 2021) in high TB burden areas of Lao PDR. Participants were screened for TB symptoms and received a chest X-ray. Sputum samples of four consecutive individuals were pooled and tested with Xpert Mycobacterium tuberculosis (MTB)/rifampicin (RIF) (Xpert-MTB/RIF) (2020) or Xpert-Ultra (2021). The agreement of the individual and pooled samples was compared and the reasons for discrepant results and potential cartridge savings were assessed. RESULTS: Each survey included 436 participants, which were tested in 109 pools. In the Xpert-MTB/RIF survey, 25 (sensitivity 89%, 95% CI 72.8% to 96.3%) of 28 pools containing MTB-positive samples tested positive and 81 pools containing only MTB-negative samples tested negative (specificity 100%, 95% CI 95.5% to 100%). In the Xpert-Ultra survey, all 32 (sensitivity 100%, 95% CI 89.3% to 100%) pools containing MTB-positive samples tested positive and all 77 (specificity 100%, 95% CI 95.3% to 100%) containing only MTB-negative samples tested negative. Pooling with Xpert-MTB/RIF and Xpert-Ultra saved 52% and 46% (227/436 and 199/436, respectively) of cartridge costs alone. CONCLUSION: Testing single and pooled specimens had a high level of agreement, with complete concordance when using Xpert-Ultra. Pooling samples could generate significant cartridge savings during ACF campaigns.

Integrated Tuberculosis and COVID-19 Activities in Karachi and Tuberculosis Case Notifications.

As the COVID-19 pandemic surged, lockdowns led to the cancellation of essential health services. As part of our Zero TB activities in Karachi, we adapted our approach to integrate activities for TB and COVID-19 to decrease the impact on diagnosis and linkage to care for TB treatment. We implemented the following: (1) integrated COVID-19 screening and testing within existing TB program activities, along with the use of an artificial intelligence (AI) software reader on digital chest X-rays; (2) home delivery of medication; (3) use of telehealth and mental health counseling; (4) provision of PPE; (5) burnout monitoring of health workers; and (6) patient safety and disinfectant protocol. We used programmatic data for six districts of Karachi from January 2018 to March 2021 to explore the time trends in case notifications, the impact of the COVID-19 pandemic, and service adaptations in the city. The case notifications in all six districts in Karachi were over 80% of the trend-adjusted expected notifications with three districts having over 90% of the expected case notifications. Overall, Karachi reached 90% of the expected case notifications during the COVID-19 pandemic. The collaborative efforts by the provincial TB program and private sector partners facilitated this reduced loss in case notifications.

An outbreak of COVID-19 associated with a fitness centre in Saskatchewan: Lessons for prevention.

Background: An outbreak of the coronavirus disease 2019 (COVID-19) occurred in Saskatchewan from September 12 to October 20, 2020. The index event, attendance at a local gym, seeded six additional clusters/outbreaks in multiple settings. These included a high school, a hospital, three workplaces (A, B and C) and several households. The overall cluster comprised 63 cases, 27 gym members and an additional 36 second, third and fourth generation cases. Methods: All outbreak-related, laboratory-confirmed cases of COVID-19 were included in the analysis. Local public health authorities interviewed all cases and contacts and conducted environmental investigations of the fitness facility. We used descriptive epidemiological methods to understand transmission dynamics of the gym-associated cluster using case investigation, contact investigation and laboratory data, including whole genome sequencing. Results: Sequencing data confirmed the unique lineage of cluster-related cases (n=32 sequenced; severe acute respiratory syndrome coronavirus 2 [SARS-CoV-2] lineage B.1.1.72). In addition to gym attendance, infectious cases attended high school and were involved in other activities. Despite ongoing transmission in the fitness facility, no secondary cases were identified in the high school where four student belonging to the cluster attended class during their infectious period. Conclusion: We describe an outbreak of COVID-19 where the index case(s) attended a fitness facility, and further spread occurred for 38 days despite active-case finding and isolation of positive cases over this period. Due to gym attendance over time, short-term closing and cleaning may not interrupt chains of transmission. Targeted, preventive public health action in fitness facilities may be warranted. Control measures worked to limit in-school acquisition.

Active Case Finding for Tuberculosis in India: A Syntheses of Activities and Outcomes Reported by the National Tuberculosis Elimination Programme.

India launched a national community-based active TB case finding (ACF) campaign in 2017 as part of the strategic plan of the National Tuberculosis Elimination Programme (NTEP). This review evaluated the outcomes for the components of the ACF campaign against the NTEP's minimum indicators and elicited the challenges faced in implementation. We supplemented data from completed pretested data proformas returned by ACF programme managers from nine states and two union territories (for 2017-2019) and five implementing partner agencies (2013-2020), with summary national data on the state-wise ACF outcomes for 2018-2020 published in annual reports by the NTEP. The data revealed variations in the strategies used to map and screen vulnerable populations and the diagnostic algorithms used across the states and union territories. National data were unavailable to assess whether the NTEP indicators for the minimum proportions identified with presumptive TB among those screened (5%), those with presumptive TB undergoing diagnostic tests (>95%), the minimum sputum smear positivity rate (2% to 3%), those with negative sputum smears tested with chest X-rays or CBNAAT (>95%) and those diagnosed through ACF initiated on anti-TB treatment (>95%) were fulfilled. Only 30% (10/33) of the states in 2018, 23% (7/31) in 2019 and 21% (7/34) in 2020 met the NTEP expectation that 5% of those tested through ACF would be diagnosed with TB (all forms). The number needed to screen to diagnose one person with TB (NNS) was not included among the NTEP's programme indicators. This rough indicator of the efficiency of ACF varied considerably across the states and union territories. The median NNS in 2018 was 2080 (interquartile range or IQR 517-4068). In 2019, the NNS was 2468 (IQR 1050-7924), and in 2020, the NNS was 906 (IQR 108-6550). The data consistently revealed that the states that tested a greater proportion of those screened during ACF and used chest X-rays or CBNAAT (or both) to diagnose TB had a higher diagnostic yield with a lower NNS. Many implementation challenges, related to health systems, healthcare provision and difficulties experienced by patients, were elicited. We suggest a series of strategic interventions addressing the implementation challenges and the six gaps identified in ACF outcomes and the expected indicators that could potentially improve the efficacy and effectiveness of community-based ACF in India.

Health system resilience: Ensuring TB services during COVID-19 pandemic in Kerala, India.

COVID-19 pandemic has affected TB case detection and continuity of care globally. Kerala, the southern Indian state has experienced a reduction in TB notification during second and third quarter of 2020. Through (1) causal analysis (2) meticulous planning and establishment of systems (3) locally customised guidelines (4) better management of resources (5) integration with other programs and (6) good partnership with private sector, Kerala was able to catch up the TB notification and ensure that TB services remain intact even during the COVID-19 pandemic. Approach to catch up TB diagnosis included (1) Field based active case finding among the vulnerable individuals, (2) bilateral screening for TB and COVID-19, (3) enhancement of biosafety in laboratories, (4) strengthening of specimen collection and transportation systems, (5) targeted advocacy and communication to find out missed cases and (6) effective partnership with the private sector. Current experiences also show that TB case finding could be improved and delay in diagnosis could be averted by integrating TB case finding into the screening and testing systems established for COVID-19. The experiences of ensuring TB services during pandemic in Kerala also affirms the importance of maintaining an integrated and strong TB control component in the public health sector and vesting ownership of the TB control programme with the primary health care team. Community-based and community-led responses that take diagnosis, care, and support to the doors of those affected have much potential in delivering TB services in the subsequent years of pandemic.

Predicted Impact of Vaccination and Active Case Finding Measures to Control Epidemic of Coronavirus Disease 2019 in a Migrant-Populated Area in Thailand.

BACKGROUND: Thailand experienced the first wave of Coronavirus Disease 2019 (COVID-19) during March-May 2020 and has been facing the second wave since December 2020. The area facing the greatest impact was Samut Sakhon, a main migrant-receiving province in the country. The Department of Disease Control (DDC) of the Thai Ministry of Public Health (MOPH) considered initiating a vaccination strategy in combination with active case finding (ACF) in the epidemic area. The DDC commissioned a research team to predict the impact of various vaccination and ACF policy scenarios in terms of case reduction and deaths averted, which is the objective of this study. METHODS: The design of this study was a secondary analysis of quantitative data. Most of the data were obtained from the DDC, MOPH. Deterministic system dynamics and compartmental models were exercised. A basic reproductive number (R0) was estimated at 3 from the beginning. Vaccine efficacy against disease transmission was assumed to be 50%. A total of 10,000 people were estimated as an initial population size. RESULTS: The findings showed that the greater the vaccination coverage, the smaller the size of incident and cumulative cases. Compared with a no-vaccination and no-ACF scenario, the 90%-vaccination coverage combined with 90%-ACF coverage contributed to a reduction of cumulative cases by 33%. The case reduction benefit would be greater when R0 was smaller (~53% and ~51% when R0 equated 2 and 1.5, respectively). CONCLUSION: This study reaffirmed the idea that a combination of vaccination and ACF measures contributed to favourable results in reducing the number of COVID-19 cases and deaths, relative to the implementation of only a single measure. The greater the vaccination and ACF coverage, the greater the volume of cases saved. Though we demonstrated the benefit of vaccination strategies in this setting, actual implementation should consider many more policy angles, such as social acceptability, cost-effectiveness and operational feasibility. Further studies that address these topics based on empirical evidence are of great value.

Modelling the effectiveness of intervention strategies to control COVID-19 outbreaks and estimating healthcare demand in Germany.

OBJECTIVES: An outbreak of the novel coronavirus in December 2019 caused a worldwide pandemic. This disease also impacts European countries, including Germany. Without effective medicines or vaccines, non-pharmaceutical interventions are the best strategy to reduce the number of cases. STUDY DESIGN: A deterministic model was simulated to evaluate the number of infectious and healthcare demand. METHOD: Using an age-structured SEIR model for the COVID-19 transmission, we project the COVID-19-associated demand for hospital and ICU beds within Germany. We estimated the effectiveness of different control measures, including active case-finding and quarantining of asymptomatic persons, self-isolation of people who had contact with an infectious person, and physical distancing, as well as a combination of these control measures. RESULTS: We found that contact tracing could reduce the peak of ICU beds as well as mass testing. The time delay between diagnosis and self-isolation influences the control measures. Physical distancing to limit the contact rate would delay the peak of the outbreak, which results in the demand for ICU beds being below the capacity during the early outbreak. CONCLUSIONS: Our study analyzed several scenarios in order to provide policymakers that face the pandemic of COVID-19 with insights into the different measures available. We highlight that the individuals who have had contact with a virus-positive person must be quarantined as soon as possible to reduce contact with possible infectious cases and to reduce transmission. Keeping physical distance and having fewer contacts should be implemented to prevent overwhelming ICU demand.

Case finding strategies under National Tuberculosis Elimination Programme (NTEP).

Case finding, an important parameter in fight against Tuberculosis (TB) has always remained a challenge despite advances in diagnostic modalities, access to health care and administrative commitment. We are still far from reaching the goals so set as per End TB Strategy and National Strategic Plan 2017-2025, and case finding is of paramount importance for achieving the said targets. This article, after identifying the obstacles faced in case finding, explores the various case finding strategies in the perspective of diagnostics, feasibility, resource utilization and current recommendations. Need for prioritization of case finding in different settings with involvement and active participation of one and all has been discussed. Role of health education in an individual, general public and health care worker in the context of case finding has been highlighted. Research areas to strengthen case finding have been enumerated. The review concludes by bringing out the need for heightened efforts for case finding in TB as the resources are significantly diverted as the world is facing the corona virus disease 2019 (COVID-19) pandemic.

[Application of healthcare big data in active case finding of COVID-19 in Yinzhou district of Ningbo].

During the prevention and control of the COVID-19 epidemic, identifying and controlling the source of infection has become one of the most important prevention and control measures to curb the epidemic in the absence of vaccines and specific therapeutic drugs. While actively taking traditional and comprehensive "early detection" measures, Yinzhou district implemented inter-departmental data sharing through the joint prevention and control mechanism. Relying on a healthcare big data platform that integrates the data from medical, disease control and non-health sectors, Yinzhou district innovatively explored the big data-driven COVID-19 case finding pattern with online suspected case screening and offline verification and disposal. Such effort has laid a solid foundation and gathered experience to conduct the dynamic and continuous surveillance and early warning for infectious disease outbreaks more effectively and efficiently in the future. This article introduces the exploration of this pattern in Yinzhou district and discusses the role of big data-driven disease surveillance in the prevention and control of infectious diseases.