Childhood vaccines and antibiotic use in low- and middle-income countries.

Vaccines may reduce the burden of antimicrobial resistance, in part by preventing infections for which treatment often includes the use of antibiotics1-4. However, the effects of vaccination on antibiotic consumption remain poorly understood-especially in low- and middle-income countries (LMICs), where the burden of antimicrobial resistance is greatest5. Here we show that vaccines that have recently been implemented in the World Health Organization's Expanded Programme on Immunization reduce antibiotic consumption substantially among children under five years of age in LMICs. By analysing data from large-scale studies of households, we estimate that pneumococcal conjugate vaccines and live attenuated rotavirus vaccines confer 19.7% (95% confidence interval, 3.4-43.4%) and 11.4% (4.0-18.6%) protection against antibiotic-treated episodes of acute respiratory infection and diarrhoea, respectively, in age groups that experience the greatest disease burden attributable to the vaccine-targeted pathogens6,7. Under current coverage levels, pneumococcal and rotavirus vaccines prevent 23.8 million and 13.6 million episodes of antibiotic-treated illness, respectively, among children under five years of age in LMICs each year. Direct protection resulting from the achievement of universal coverage targets for these vaccines could prevent an additional 40.0 million episodes of antibiotic-treated illness. This evidence supports the prioritization of vaccines within the global strategy to combat antimicrobial resistance8.

Multimodeling approach to evaluating the efficacy of layering pharmaceutical and nonpharmaceutical interventions for influenza pandemics.

When an influenza pandemic emerges, temporary school closures and antiviral treatment may slow virus spread, reduce the overall disease burden, and provide time for vaccine development, distribution, and administration while keeping a larger portion of the general population infection free. The impact of such measures will depend on the transmissibility and severity of the virus and the timing and extent of their implementation. To provide robust assessments of layered pandemic intervention strategies, the Centers for Disease Control and Prevention (CDC) funded a network of academic groups to build a framework for the development and comparison of multiple pandemic influenza models. Research teams from Columbia University, Imperial College London/Princeton University, Northeastern University, the University of Texas at Austin/Yale University, and the University of Virginia independently modeled three prescribed sets of pandemic influenza scenarios developed collaboratively by the CDC and network members. Results provided by the groups were aggregated into a mean-based ensemble. The ensemble and most component models agreed on the ranking of the most and least effective intervention strategies by impact but not on the magnitude of those impacts. In the scenarios evaluated, vaccination alone, due to the time needed for development, approval, and deployment, would not be expected to substantially reduce the numbers of illnesses, hospitalizations, and deaths that would occur. Only strategies that included early implementation of school closure were found to substantially mitigate early spread and allow time for vaccines to be developed and administered, especially under a highly transmissible pandemic scenario.

fastlin: an ultra-fast program for Mycobacterium tuberculosis complex lineage typing.

SUMMARY: Fastlin is a bioinformatics tool designed for rapid Mycobacterium tuberculosis complex (MTBC) lineage typing. It utilizes an ultra-fast alignment-free approach to detect previously identified barcode single nucleotide polymorphisms associated with specific MTBC lineages. In a comprehensive benchmarking against existing tools, fastlin demonstrated high accuracy and significantly faster running times. AVAILABILITY AND IMPLEMENTATION: fastlin is freely available at https://github.com/rderelle/fastlin and can easily be installed using Conda.

Using real-time data to guide decision-making during an influenza pandemic: A modelling analysis.

Influenza pandemics typically occur in multiple waves of infection, often associated with initial emergence of a novel virus, followed (in temperate regions) by a resurgence accompanying the onset of the annual influenza season. Here, we examined whether data collected from an initial pandemic wave could be informative, for the need to implement non-pharmaceutical measures in any resurgent wave. Drawing from the 2009 H1N1 pandemic in 10 states in the USA, we calibrated simple mathematical models of influenza transmission dynamics to data for laboratory confirmed hospitalisations during the initial 'spring' wave. We then projected pandemic outcomes (cumulative hospitalisations) during the fall wave, and compared these projections with data. Model results showed reasonable agreement for all states that reported a substantial number of cases in the spring wave. Using this model we propose a probabilistic decision framework that can be used to determine the need for preemptive measures such as postponing school openings, in advance of a fall wave. This work illustrates how model-based evidence synthesis, in real-time during an early pandemic wave, could be used to inform timely decisions for pandemic response.

The potential impact of vaccination on tuberculosis burden in India: A modelling analysis.

Background & objectives: Vaccination will play an important role in meeting the end tuberculosis (TB) goals. While certain vaccine candidates in advanced stages of clinical trials raise hope for the future availability of new tools, in the immediate term, there is also increasing interest in Bacille Calmette-Guérin revaccination among adults and adolescents as a potential strategy. Here, we sought to estimate the potential epidemiological impact of TB vaccination in India. Methods: We developed a deterministic, age-structured, compartmental model of TB in India. Data from the recent national prevalence survey was used to inform epidemiological burden while also incorporating a vulnerable population who may be prioritized for vaccination, the latter consistent with the burden of undernutrition. Using this framework, the potential impact on incidence and mortality of a vaccine with 50 per cent efficacy was estimated, if rolled out in 2023 to cover 50 per cent of the unvaccinated each year. Simulated impacts were compared for disease- vs. infection-preventing vaccines, as well as when prioritizing vulnerable groups (those with undernutrition) rather than the general population. A sensitivity analyses were also conducted with respect to the duration, and efficacy, of vaccine immunity. Results: When rolled out in the general population, an infection-preventing vaccine would avert 12 per cent (95% Bayesian credible intervals (Crl): 4.3-28%) of cumulative TB incidence between 2023 and 2030, while a disease-preventing vaccine would avert 29 per cent (95% Crl: 24-34%). Although the vulnerable population accounts for only around 16 per cent of India's population, prioritizing this group for vaccination would achieve almost half the impact of rollout in the general population, in the example of an infection-preventing vaccine. Sensitivity analysis also highlights the importance of the duration and efficacy of vaccine-induced immunity. Interpretation & conclusions: These results highlight how even a vaccine with moderate effectiveness (50%) could achieve substantial reductions in TB burden in India, especially when prioritized for the most vulnerable.

Scaling up target regimens for tuberculosis preventive treatment in Brazil and South Africa: An analysis of costs and cost-effectiveness.

BACKGROUND: Shorter, safer, and cheaper tuberculosis (TB) preventive treatment (TPT) regimens will enhance uptake and effectiveness. WHO developed target product profiles describing minimum requirements and optimal targets for key attributes of novel TPT regimens. We performed a cost-effectiveness analysis addressing the scale-up of regimens meeting these criteria in Brazil, a setting with relatively low transmission and low HIV and rifampicin-resistant TB (RR-TB) prevalence, and South Africa, a setting with higher transmission and higher HIV and RR-TB prevalence. METHODS AND FINDINGS: We used outputs from a model simulating scale-up of TPT regimens meeting minimal and optimal criteria. We assumed that drug costs for minimal and optimal regimens were identical to 6 months of daily isoniazid (6H). The minimal regimen lasted 3 months, with 70% completion and 80% efficacy; the optimal regimen lasted 1 month, with 90% completion and 100% efficacy. Target groups were people living with HIV (PLHIV) on antiretroviral treatment and household contacts (HHCs) of identified TB patients. The status quo was 6H at 2019 coverage levels for PLHIV and HHCs. We projected TB cases and deaths, TB-associated disability-adjusted life years (DALYs), and costs (in 2020 US dollars) associated with TB from a TB services perspective from 2020 to 2035, with 3% annual discounting. We estimated the expected costs and outcomes of scaling up 6H, the minimal TPT regimen, or the optimal TPT regimen to reach all eligible PLHIV and HHCs by 2023, compared to the status quo. Maintaining current 6H coverage in Brazil (0% of HHCs and 30% of PLHIV treated) would be associated with 1.1 (95% uncertainty range [UR] 1.1-1.2) million TB cases, 123,000 (115,000-132,000) deaths, and 2.5 (2.1-3.1) million DALYs and would cost $1.1 ($1.0-$1.3) billion during 2020-2035. Expanding the 6H, minimal, or optimal regimen to 100% coverage among eligible groups would reduce DALYs by 0.5% (95% UR 1.2% reduction, 0.4% increase), 2.5% (1.8%-3.0%), and 9.0% (6.5%-11.0%), respectively, with additional costs of $107 ($95-$117) million and $51 ($41-$60) million and savings of $36 ($14-$58) million, respectively. Compared to the status quo, costs per DALY averted were $7,608 and $808 for scaling up the 6H and minimal regimens, respectively, while the optimal regimen was dominant (cost savings, reduced DALYs). In South Africa, maintaining current 6H coverage (0% of HHCs and 69% of PLHIV treated) would be associated with 3.6 (95% UR 3.0-4.3) million TB cases, 843,000 (598,000-1,201,000) deaths, and 36.7 (19.5-58.0) million DALYs and would cost $2.5 ($1.8-$3.6) billion. Expanding coverage with the 6H, minimal, or optimal regimen would reduce DALYs by 6.9% (95% UR 4.3%-95%), 15.5% (11.8%-18.9%), and 38.0% (32.7%-43.0%), respectively, with additional costs of $79 (-$7, $151) million and $40 (-$52, $140) million and savings of $608 ($443-$832) million, respectively. Compared to the status quo, estimated costs per DALY averted were $31 and $7 for scaling up the 6H and minimal regimens, while the optimal regimen was dominant. Study limitations included the focus on 2 countries, and no explicit consideration of costs incurred before the decision to prescribe TPT. CONCLUSIONS: Our findings suggest that scale-up of TPT regimens meeting minimum or optimal requirements would likely have important impacts on TB-associated outcomes and would likely be cost-effective or cost saving.

Prioritising attributes for tuberculosis preventive treatment regimens: a modelling analysis.

BACKGROUND: Recent years have seen important improvements in available preventive treatment regimens for tuberculosis (TB), and research is ongoing to develop these further. To assist with the formulation of target product profiles for future regimens, we examined which regimen properties would be most influential in the epidemiological impact of preventive treatment. METHODS: Following expert consultation, we identified 5 regimen properties relevant to the incidence-reducing impact of a future preventive treatment regimen: regimen duration, efficacy, ease-of-adherence (treatment completion rates in programmatic conditions), forgiveness to non-completion and the barrier to developing rifampicin resistance during treatment. For each regimen property, we elicited expert input for minimally acceptable and optimal (ideal-but-feasible) performance scenarios for future regimens. Using mathematical modelling, we then examined how each regimen property would influence the TB incidence reduction arising from full uptake of future regimens according to current WHO guidelines, in four countries: South Africa, Kenya, India and Brazil. RESULTS: Of all regimen properties, efficacy is the single most important predictor of epidemiological impact, while ease-of-adherence plays an important secondary role. These results are qualitatively consistent across country settings; sensitivity analyses show that these results are also qualitatively robust to a range of model assumptions, including the mechanism of action of future preventive regimens. CONCLUSIONS: As preventive treatment regimens against TB continue to improve, understanding the key drivers of epidemiological impact can assist in guiding further development. By meeting these key targets, future preventive treatment regimens could play a critical role in global efforts to end TB.

Quantifying the potential value of antigen-detection rapid diagnostic tests for COVID-19: a modelling analysis.

BACKGROUND: Testing plays a critical role in treatment and prevention responses to the COVID-19 pandemic. Compared to nucleic acid tests (NATs), antigen-detection rapid diagnostic tests (Ag-RDTs) can be more accessible, but typically have lower sensitivity and specificity. By quantifying these trade-offs, we aimed to inform decisions about when an Ag-RDT would offer greater public health value than reliance on NAT. METHODS: Following an expert consultation, we selected two use cases for analysis: rapid identification of people with COVID-19 amongst patients admitted with respiratory symptoms in a 'hospital' setting and early identification and isolation of people with mildly symptomatic COVID-19 in a 'community' setting. Using decision analysis, we evaluated the health system cost and health impact (deaths averted and infectious days isolated) of an Ag-RDT-led strategy, compared to a strategy based on NAT and clinical judgement. We adopted a broad range of values for 'contextual' parameters relevant to a range of settings, including the availability of NAT and the performance of clinical judgement. We performed a multivariate sensitivity analysis to all of these parameters. RESULTS: In a hospital setting, an Ag-RDT-led strategy would avert more deaths than a NAT-based strategy, and at lower cost per death averted, when the sensitivity of clinical judgement is less than 90%, and when NAT results are available in time to inform clinical decision-making for less than 85% of patients. The use of an Ag-RDT is robustly supported in community settings, where it would avert more transmission at lower cost than relying on NAT alone, under a wide range of assumptions. CONCLUSIONS: Despite their imperfect sensitivity and specificity, Ag-RDTs have the potential to be simultaneously more impactful, and have a lower cost per death and infectious person-days averted, than current approaches to COVID-19 diagnostic testing.

Achieving a "step change" in the tuberculosis epidemic through comprehensive community-wide intervention: a model-based analysis.

BACKGROUND: Global progress towards reducing tuberculosis (TB) incidence and mortality has consistently lagged behind the World Health Organization targets leading to a perception that large reductions in TB burden cannot be achieved. However, several recent and historical trials suggest that intervention efforts that are comprehensive and intensive can have a substantial epidemiological impact. We aimed to quantify the potential epidemiological impact of an intensive but realistic, community-wide campaign utilizing existing tools and designed to achieve a "step change" in the TB burden. METHODS: We developed a compartmental model that resembled TB transmission and epidemiology of a mid-sized city in India, the country with the greatest absolute TB burden worldwide. We modeled the impact of a one-time, community-wide screening campaign, with treatment for TB disease and preventive therapy for latent TB infection (LTBI). This one-time intervention was followed by the strengthening of the tuberculosis-related health system, potentially facilitated by leveraging the one-time campaign. We estimated the tuberculosis cases and deaths that could be averted over 10 years using this comprehensive approach and assessed the contributions of individual components of the intervention. RESULTS: A campaign that successfully screened 70% of the adult population for active and latent tuberculosis and subsequently reduced diagnostic and treatment delays and unsuccessful treatment outcomes by 50% was projected to avert 7800 (95% range 5450-10,200) cases and 1710 (1290-2180) tuberculosis-related deaths per 1 million population over 10 years. Of the total averted deaths, 33.5% (28.2-38.3) were attributable to the inclusion of preventive therapy and 52.9% (48.4-56.9) to health system strengthening. CONCLUSIONS: A one-time, community-wide mass campaign, comprehensively designed to detect, treat, and prevent tuberculosis with currently existing tools can have a meaningful and long-lasting epidemiological impact. Successful treatment of LTBI is critical to achieving this result. Health system strengthening is essential to any effort to transform the TB response.

Antigen-based Rapid Diagnostic Testing or Alternatives for Diagnosis of Symptomatic COVID-19: A Simulation-based Net Benefit Analysis.

BACKGROUND: SARS-CoV-2 antigen-detection rapid diagnostic tests can diagnose COVID-19 rapidly and at low cost, but lower sensitivity compared with reverse-transcriptase polymerase chain reaction (PCR) has limited clinical adoption. METHODS: We compared antigen testing, PCR testing, and clinical judgment alone for diagnosing symptomatic COVID-19 in an outpatient setting (10% COVID-19 prevalence among the patients tested, 3-day PCR turnaround) and a hospital setting (40% prevalence, 24-hour PCR turnaround). We simulated transmission from cases and contacts, and relationships between time, viral burden, transmission, and case detection. We compared diagnostic approaches using a measure of net benefit that incorporated both clinical and public health benefits and harms of the intervention. RESULTS: In the outpatient setting, we estimated that using antigen testing instead of PCR to test 200 individuals could be equivalent to preventing all symptomatic transmission from one person with COVID-19 (one "transmission-equivalent"). In a hospital, net benefit analysis favored PCR and testing 25 patients with PCR instead of antigen testing achieved one transmission-equivalent of benefit. In both settings, antigen testing was preferable to PCR if PCR turnaround time exceeded 2 days. Both tests provided greater net benefit than management based on clinical judgment alone unless intervention carried minimal harm and was provided equally regardless of diagnostic approach. CONCLUSIONS: For diagnosis of symptomatic COVID-19, we estimated that the speed of diagnosis with antigen testing is likely to outweigh its lower accuracy compared with PCR, wherever PCR turnaround time is 2 days or longer. This advantage may be even greater if antigen tests are also less expensive.

Implications of localized charge for human influenza A H1N1 hemagglutinin evolution: Insights from deep mutational scans.

Seasonal influenza A viruses of humans evolve rapidly due to strong selection pressures from host immune responses, principally on the hemagglutinin (HA) viral surface protein. Based on mouse transmission experiments, a proposed mechanism for immune evasion consists of increased avidity to host cellular receptors, mediated by electrostatic charge interactions with negatively charged cell surfaces. In support of this, the HA charge of the globally circulating H3N2 has increased over time since its pandemic. However, the same trend was not seen in H1N1 HA sequences. This is counter-intuitive, since immune escape due to increased avidity (due itself to an increase in charge) was determined experimentally. Here, we explore whether patterns of local charge of H1N1 HA can explain this discrepancy and thus further associate electrostatic charge with immune escape and viral evolutionary dynamics. Measures of site-wise functional selection and expected charge computed from deep mutational scan data on an early H1N1 HA yield a striking division of residues into three groups, separated by charge. We then explored evolutionary dynamics of these groups from 1918 to 2008. In particular, one group increases in net charge over time and consists of sites that are evolving the fastest, that are closest to the receptor binding site (RBS), and that are exposed to solvent (i.e., on the surface). By contrast, another group decreases in net charge and consists of sites that are further away from the RBS and evolving slower, but also exposed to solvent. The last group consists of those sites in the HA core, with no change in net charge and that evolve very slowly. Thus, there is a group of residues that follows the same trend as seen for the entire H3N2 HA. It is possible that the H1N1 HA is under other biophysical constraints that result in compensatory decreases in charge elsewhere on the protein. Our results implicate localized charge in HA interactions with host cells, and highlight how deep mutational scan data can inform evolutionary hypotheses.

Understanding the incremental value of novel diagnostic tests for tuberculosis.

Tuberculosis is a major source of global mortality caused by infection, partly because of a tremendous ongoing burden of undiagnosed disease. Improved diagnostic technology may play an increasingly crucial part in global efforts to end tuberculosis, but the ability of diagnostic tests to curb tuberculosis transmission is dependent on multiple factors, including the time taken by a patient to seek health care, the patient's symptoms, and the patterns of transmission before diagnosis. Novel diagnostic assays for tuberculosis have conventionally been evaluated on the basis of characteristics such as sensitivity and specificity, using assumptions that probably overestimate the impact of diagnostic tests on transmission. We argue for a shift in focus to the evaluation of such tests' incremental value, defining outcomes that reflect each test's purpose (for example, transmissions averted) and comparing systems with the test against those without, in terms of those outcomes. Incremental value can also be measured in units of outcome per incremental unit of resource (for example, money or human capacity). Using a novel, simplified model of tuberculosis transmission that addresses some of the limitations of earlier tuberculosis diagnostic models, we demonstrate that the incremental value of any novel test depends not just on its accuracy, but also on elements such as patient behaviour, tuberculosis natural history and health systems. By integrating these factors into a single unified framework, we advance an approach to the evaluation of new diagnostic tests for tuberculosis that considers the incremental value at the population level and demonstrates how additional data could inform more-effective implementation of tuberculosis diagnostic tests under various conditions.

Plausibility of a third wave of COVID-19 in India: A mathematical modelling based analysis.

BACKGROUND & OBJECTIVES: In the context of India's ongoing resurgence of COVID-19 (second wave since mid-February 2021, following the subsiding of the first wave in September 2020), there has been increasing speculation on the possibility of a future third wave of infection, posing a burden on the healthcare system. Using simple mathematical models of the transmission dynamics of SARS-CoV-2, this study examined the conditions under which a serious third wave could occur. METHODS: Using a deterministic, compartmental model of SARS-CoV-2 transmission, four potential mechanisms for a third wave were examined: (i) waning immunity restores previously exposed individuals to a susceptible state, (ii) emergence of a new viral variant that is capable of escaping immunity to previously circulating strains, (iii) emergence of a new viral variant that is more transmissible than the previously circulating strains, and (iv) release of current lockdowns affording fresh opportunities for transmission. RESULTS: Immune-mediated mechanisms (waning immunity, or viral evolution for immune escape) are unlikely to drive a severe third wave if acting on their own, unless such mechanisms lead to a complete loss of protection among those previously exposed. Likewise, a new, more transmissible variant would have to exceed a high threshold (R0>4.5) to cause a third wave on its own. However, plausible mechanisms for a third wave include: (i) a new variant that is more transmissible and at the same time capable of escaping prior immunity, and (ii) lockdowns that are highly effective in limiting transmission and subsequently released. In both cases, any third wave seems unlikely to be as severe as the second wave. Rapid scale-up of vaccination efforts could play an important role in mitigating these and future waves of the disease. INTERPRETATION & CONCLUSIONS: This study demonstrates plausible mechanisms by which a substantial third wave could occur, while also illustrating that it is unlikely for any such resurgence to be as large as the second wave. Model projections are, however, subject to several uncertainties, and it remains important to scale up vaccination coverage to mitigate against any eventuality. Preparedness planning for any potential future wave will benefit by drawing upon the projected numbers based on the present modelling exercise.

Trade-offs between cost and accuracy in active case finding for tuberculosis: A dynamic modelling analysis.

BACKGROUND: Active case finding (ACF) may be valuable in tuberculosis (TB) control, but questions remain about its optimum implementation in different settings. For example, smear microscopy misses up to half of TB cases, yet is cheap and detects the most infectious TB cases. What, then, is the incremental value of using more sensitive and specific, yet more costly, tests such as Xpert MTB/RIF in ACF in a high-burden setting? METHODS AND FINDINGS: We constructed a dynamic transmission model of TB, calibrated to be consistent with an urban slum population in India. We applied this model to compare the potential cost and impact of 2 hypothetical approaches following initial symptom screening: (i) 'moderate accuracy' testing employing a microscopy-like test (i.e., lower cost but also lower accuracy) for bacteriological confirmation and (ii) 'high accuracy' testing employing an Xpert-like test (higher cost but also higher accuracy, while also detecting rifampicin resistance). Results suggest that ACF using a moderate-accuracy test could in fact cost more overall than using a high-accuracy test. Under an illustrative budget of US$20 million in a slum population of 2 million, high-accuracy testing would avert 1.14 (95% credible interval 0.75-1.99, with p = 0.28) cases relative to each case averted by moderate-accuracy testing. Test specificity is a key driver: High-accuracy testing would be significantly more impactful at the 5% significance level, as long as the high-accuracy test has specificity at least 3 percentage points greater than the moderate-accuracy test. Additional factors promoting the impact of high-accuracy testing are that (i) its ability to detect rifampicin resistance can lead to long-term cost savings in second-line treatment and (ii) its higher sensitivity contributes to the overall cases averted by ACF. Amongst the limitations of this study, our cost model has a narrow focus on the commodity costs of testing and treatment; our estimates should not be taken as indicative of the overall cost of ACF. There remains uncertainty about the true specificity of tests such as smear and Xpert-like tests in ACF, relating to the accuracy of the reference standard under such conditions. CONCLUSIONS: Our results suggest that cheaper diagnostics do not necessarily translate to less costly ACF, as any savings from the test cost can be strongly outweighed by factors including false-positive TB treatment, reduced sensitivity, and foregone savings in second-line treatment. In resource-limited settings, it is therefore important to take all of these factors into account when designing cost-effective strategies for ACF.

Predicting the impact of patient and private provider behavior on diagnostic delay for pulmonary tuberculosis patients in India: A simulation modeling study.

BACKGROUND: Tuberculosis (TB) incidence in India continues to be high due, in large part, to long delays experienced by patients before successful diagnosis and treatment initiation, especially in the private sector. This diagnostic delay is driven by patients' inclination to switch between different types of providers and providers' inclination to delay ordering of accurate diagnostic tests relevant to TB. Our objective is to quantify the impact of changes in these behavioral characteristics of providers and patients on diagnostic delay experienced by pulmonary TB patients. METHODS AND FINDINGS: We developed a discrete event simulation model of patients' diagnostic pathways that captures key behavioral characteristics of providers (time to order a test) and patients (time to switch to another provider). We used an expectation-maximization algorithm to estimate the parameters underlying these behavioral characteristics, with quantitative data encoded from detailed interviews of 76 and 64 pulmonary TB patients in the 2 Indian cities of Mumbai and Patna, respectively, which were conducted between April and August 2014. We employed the estimated model to simulate different counterfactual scenarios of diagnostic pathways under altered behavioral characteristics of providers and patients to predict their potential impact on the diagnostic delay. Private healthcare providers including chemists were the first point of contact for the majority of TB patients in Mumbai (70%) and Patna (94%). In Mumbai, 45% of TB patients first approached less-than-fully-qualified providers (LTFQs), who take 28.71 days on average for diagnosis. About 61% of these patients switched to other providers without a diagnosis. Our model estimates that immediate testing for TB by LTFQs at the first visit (at the current level of diagnostic accuracy) could reduce the average diagnostic delay from 35.53 days (95% CI: 34.60, 36.46) to 18.72 days (95% CI: 18.01, 19.43). In Patna, 61% of TB patients first approached fully qualified providers (FQs), who take 9.74 days on average for diagnosis. Similarly, immediate testing by FQs at the first visit (at the current level of diagnostic accuracy) could reduce the average diagnostic delay from 23.39 days (95% CI: 22.77, 24.02) to 11.16 days (95% CI: 10.52, 11.81). Improving the diagnostic accuracy of providers per se, without reducing the time to testing, was not predicted to lead to any reduction in diagnostic delay. Our study was limited because of its restricted geographic scope, small sample size, and possible recall bias, which are typically associated with studies of patient pathways using patient interviews. CONCLUSIONS: In this study, we found that encouraging private providers to order definitive TB diagnostic tests earlier during patient consultation may have substantial impact on reducing diagnostic delay in these urban Indian settings. These results should be combined with disease transmission models to predict the impact of changes in provider behavior on TB incidence.

The potential impact of urine-LAM diagnostics on tuberculosis incidence and mortality: A modelling analysis.

BACKGROUND: Lateral flow urine lipoarabinomannan (LAM) tests could offer important new opportunities for the early detection of tuberculosis (TB). The currently licensed LAM test, Alere Determine TB LAM Ag ('LF-LAM'), performs best in the sickest people living with HIV (PLHIV). However, the technology continues to improve, with newer LAM tests, such as Fujifilm SILVAMP TB LAM ('SILVAMP-LAM') showing improved sensitivity, including amongst HIV-negative patients. It is important to anticipate the epidemiological impact that current and future LAM tests may have on TB incidence and mortality. METHODS AND FINDINGS: Concentrating on South Africa, we examined the impact that widening LAM test eligibility would have on TB incidence and mortality. We developed a mathematical model of TB transmission to project the impact of LAM tests, distinguishing 'current' tests (with sensitivity consistent with LF-LAM), from hypothetical 'future' tests (having sensitivity consistent with SILVAMP-LAM). We modelled the impact of both tests, assuming full adoption of the 2019 WHO guidelines for the use of these tests amongst those receiving HIV care. We also simulated the hypothetical deployment of future LAM tests for all people presenting to care with TB symptoms, not restricted to PLHIV. Our model projects that 2,700,000 (95% credible interval [CrI] 2,000,000-3,600,000) and 420,000 (95% CrI 350,000-520,000) cumulative TB incident cases and deaths, respectively, would occur between 2020 and 2035 if the status quo is maintained. Relative to this comparator, current and future LAM tests would respectively avert 54 (95% CrI 33-86) and 90 (95% CrI 55-145) TB deaths amongst inpatients between 2020 and 2035, i.e., reductions of 5% (95% CrI 4%-6%) and 9% (95% CrI 7%-11%) in inpatient TB mortality. This impact in absolute deaths averted doubles if testing is expanded to include outpatients, yet remains <1% of country-level TB deaths. Similar patterns apply to incidence results. However, deploying a future LAM test for all people presenting to care with TB symptoms would avert 470,000 (95% CrI 220,000-870,000) incident TB cases (18% reduction, 95% CrI 9%-29%) and 120,000 (95% CrI 69,000-210,000) deaths (30% reduction, 95% CrI 18%-44%) between 2020 and 2035. Notably, this increase in impact arises largely from diagnosis of TB amongst those with HIV who are not yet in HIV care, and who would thus be ineligible for a LAM test under current guidelines. Qualitatively similar results apply under an alternative comparator assuming expanded use of GeneXpert MTB/RIF ('Xpert') for TB diagnosis. Sensitivity analysis demonstrates qualitatively similar results in a setting like Kenya, which also has a generalised HIV epidemic, but a lower burden of HIV/TB coinfection. Amongst limitations of this analysis, we do not address the cost or cost-effectiveness of future tests. Our model neglects drug resistance and focuses on the country-level epidemic, thus ignoring subnational variations in HIV and TB burden. CONCLUSIONS: These results suggest that LAM tests could have an important effect in averting TB deaths amongst PLHIV with advanced disease. However, achieving population-level impact on the TB epidemic, even in high-HIV-burden settings, will require future LAM tests to have sufficient performance to be deployed more broadly than in HIV care.

The potential impact of preventive therapy against tuberculosis in the WHO South-East Asian Region: a modelling approach.

BACKGROUND: The prevention of tuberculosis (TB) is key for accelerating current, slow declines in TB burden. The 2018 World Health Organization (WHO) guidelines on eligibility for preventive therapy to treat latent TB infection (LTBI) include people living with human immunodeficiency virus (PLHIV), household contacts of TB patients including children, and those with clinical conditions including silicosis, dialysis, transplantation, etc. and other country-specific groups. We aimed to estimate the potential impact of full implementation of these guidelines in the WHO South-East Asian (SEA) Region, which bears the largest burden of TB and LTBI amongst the WHO regions. METHODS: We developed mathematical models of TB transmission dynamics, calibrated individually to each of the 11 countries in the region. We modelled preventive therapy in the absence of other TB interventions. As an alternative comparator, reflecting ongoing developments in TB control in the region, we also simulated improvements in the treatment cascade for active TB, including private sector engagement and intensified case-finding. Relative to both scenarios, for each country in the region, we projected TB cases and deaths averted between 2020 and 2030, by full uptake of preventive therapy, defined as comprehensive coverage amongst eligible populations as per WHO guidelines, and assuming outcomes consistent with clinical trials. We also performed sensitivity analysis to illustrate impact under less-than-optimal conditions. RESULTS: At the regional level, full uptake of preventive therapy amongst identified risk groups would reduce annual incidence rates in 2030 by 8.30% (95% CrI 6.48-10.83) relative to 2015, in the absence of any additional interventions. If implemented against a backdrop of improved TB treatment cascades, preventive therapy would achieve an incremental 6.93 percentage points (95% CrI 5.81-8.51) of reduction in annual incidence rates, compared to 2015. At the regional level, the numbers of individuals with latent TB infection that need to be treated to avert 1 TB case is 64 (95% CrI 55-74). Sensitivity analysis illustrates that results for impact are roughly proportional to 'effective coverage' (the product of actual coverage and effectiveness of the regimen). CONCLUSIONS: Full implementation of WHO guidelines is important for ending TB in the SEA Region. Although future strategies will need to be expanded to the population level, to achieve large declines in TB incidence, the uptake of current tools can offer a valuable step in this direction.

Explaining age disparities in tuberculosis burden in Taiwan: a modelling study.

BACKGROUND: Tuberculosis (TB) burden shows wide disparities across ages in Taiwan. In 2016, the age-specific notification rate in those older than 65 years old was about 100 times as much as in those younger than 15 years old (185.0 vs 1.6 per 100,000 population). Similar patterns are observed in other intermediate TB burden settings. However, driving mechanisms for such age disparities are not clear and may have importance for TB control efforts. METHODS: We hypothesised three mechanisms for the age disparity in TB burden: (i) older age groups bear a higher risk of TB progression due to immune senescence, (ii) elderly cases acquired TB infection during a past period of high transmission, which has since rapidly declined and thus contributes to little recent infections, and (iii) assortative mixing by age allows elders to maintain a higher risk of TB infection, while limiting spillover transmission to younger age groups. We developed a series of dynamic compartmental models to incorporate these mechanisms, individually and in combination. The models were calibrated to the TB notification rates in Taiwan over 1997-2016 and evaluated by goodness-of-fit to the age disparities and the temporal trend in the TB burden, as well as the deviance information criterion (DIC). According to the model performance, we compared contributions of the hypothesised mechanisms. RESULTS: The 'full' model including all the three hypothesised mechanisms best captured the age disparities and temporal trend of the TB notification rates. However, dropping individual mechanisms from the full model in turn, we found that excluding the mechanism of assortative mixing yielded the least change in goodness-of-fit. In terms of their influence on the TB dynamics, the major contribution of the 'immune senescence' and 'assortative mixing' mechanisms was to create disparate burden among age groups, while the 'declining transmission' mechanism served to capture the temporal trend of notification rates. CONCLUSIONS: In settings such as Taiwan, the current TB burden in the elderly may be impacted more by prevention of active disease following latent infection, than by case-finding for blocking transmission. Further studies on these mechanisms are needed to disentangle their impacts on the TB epidemic and develop corresponding control strategies.

Prudent public health intervention strategies to control the coronavirus disease 2019 transmission in India: A mathematical model-based approach.

Background & objectives: Coronavirus disease 2019 (COVID-19) has raised urgent questions about containment and mitigation, particularly in countries where the virus has not yet established human-to-human transmission. The objectives of this study were to find out if it was possible to prevent, or delay, the local outbreaks of COVID-19 through restrictions on travel from abroad and if the virus has already established in-country transmission, to what extent would its impact be mitigated through quarantine of symptomatic patients? Methods: These questions were addressed in the context of India, using simple mathematical models of infectious disease transmission. While there remained important uncertainties in the natural history of COVID-19, using hypothetical epidemic curves, some key findings were illustrated that appeared insensitive to model assumptions, as well as highlighting critical data gaps. Results: It was assumed that symptomatic quarantine would identify and quarantine 50 per cent of symptomatic individuals within three days of developing symptoms. In an optimistic scenario of the basic reproduction number (R0) being 1.5, and asymptomatic infections lacking any infectiousness, such measures would reduce the cumulative incidence by 62 per cent. In the pessimistic scenario of R0=4, and asymptomatic infections being half as infectious as symptomatic, this projected impact falls to two per cent. Interpretation & conclusions: Port-of-entry-based entry screening of travellers with suggestive clinical features and from COVID-19-affected countries, would achieve modest delays in the introduction of the virus into the community. Acting alone, however, such measures would be insufficient to delay the outbreak by weeks or longer. Once the virus establishes transmission within the community, quarantine of symptomatics may have a meaningful impact on disease burden. Model projections are subject to substantial uncertainty and can be further refined as more is understood about the natural history of infection of this novel virus. As a public health measure, health system and community preparedness would be critical to control any impending spread of COVID-19 in the country.

Guidance for Studies Evaluating the Accuracy of Tuberculosis Triage Tests.

Approximately 3.6 million cases of active tuberculosis (TB) go potentially undiagnosed annually, partly due to limited access to confirmatory diagnostic tests, such as molecular assays or mycobacterial culture, in community and primary healthcare settings. This article provides guidance for TB triage test evaluations. A TB triage test is designed for use in people with TB symptoms and/or significant risk factors for TB. Triage tests are simple and low-cost tests aiming to improve ease of access and implementation (compared with confirmatory tests) and decrease the proportion of patients requiring more expensive confirmatory testing. Evaluation of triage tests should occur in settings of intended use, such as community and primary healthcare centers. Important considerations for triage test evaluation include study design, population, sample type, test throughput, use of thresholds, reference standard (ideally culture), and specimen flow. The impact of a triage test will depend heavily on issues beyond accuracy, primarily centered on implementation.