Optima TB: A tool to help optimally allocate tuberculosis spending.

Approximately 85% of tuberculosis (TB) related deaths occur in low- and middle-income countries where health resources are scarce. Effective priority setting is required to maximise the impact of limited budgets. The Optima TB tool has been developed to support analytical capacity and inform evidence-based priority setting processes for TB health benefits package design. This paper outlines the Optima TB framework and how it was applied in Belarus, an upper-middle income country in Eastern Europe with a relatively high burden of TB. Optima TB is a population-based disease transmission model, with programmatic cost functions and an optimisation algorithm. Modelled populations include age-differentiated general populations and higher-risk populations such as people living with HIV. Populations and prospective interventions are defined in consultation with local stakeholders. In partnership with the latter, demographic, epidemiological, programmatic, as well as cost and spending data for these populations and interventions are then collated. An optimisation analysis of TB spending was conducted in Belarus, using program objectives and constraints defined in collaboration with local stakeholders, which included experts, decision makers, funders and organisations involved in service delivery, support and technical assistance. These analyses show that it is possible to improve health impact by redistributing current TB spending in Belarus. Specifically, shifting funding from inpatient- to outpatient-focused care models, and from mass screening to active case finding strategies, could reduce TB prevalence and mortality by up to 45% and 50%, respectively, by 2035. In addition, an optimised allocation of TB spending could lead to a reduction in drug-resistant TB infections by 40% over this period. This would support progress towards national TB targets without additional financial resources. The case study in Belarus demonstrates how reallocations of spending across existing and new interventions could have a substantial impact on TB outcomes. This highlights the potential for Optima TB and similar modelling tools to support evidence-based priority setting.

Analysing the link between public transport use and airborne transmission: mobility and contagion in the London underground.

BACKGROUND: The transmission of infectious diseases is dependent on the amount and nature of contacts between infectious and healthy individuals. Confined and crowded environments that people visit in their day-to-day life (such as town squares, business districts, transport hubs, etc) can act as hot-spots for spreading disease. In this study we explore the link between the use of public transport and the spread of airborne infections in urban environments. METHODS: We study a large number of journeys on the London Underground, which is known to be particularly crowded at certain times. We use publically available Oyster card data (the electronic ticket used for public transport in Greater London), to infer passengers' routes on the underground network. In order to estimate the spread of a generic airborne disease in each station, we use and extend an analytical microscopic model that was initially designed to study people moving in a corridor. RESULTS: Comparing our results with influenza-like illnesses (ILI) data collected by Public Health England (PHE) in London boroughs, shows a correlation between the use of public transport and the spread of ILI. Specifically, we show that passengers departing from boroughs with higher ILI rates have higher number of contacts when travelling on the underground. Moreover, by comparing our results with other demographic key factors, we are able to discuss the role that the Underground plays in the spread of airborne infections in the English capital. CONCLUSIONS: Our study suggests a link between public transport use and infectious diseases transmission and encourages further research into that area. Results could be used to inform the development of non-pharmacological interventions that can act on preventing instead of curing infections and are, potentially, more cost-effective.

Economic evaluation of novel Mycobacterium tuberculosis specific antigen-based skin tests for detection of TB infection: A modelling study.

Evidence on the economic impact of novel skin tests for tuberculosis infection (TBST) is scarce and limited by study quality. We used estimates on the cost-effectiveness of the use of TBST compared to current tuberculosis infection (TBI) tests to assess whether TBST are affordable and feasible to implement under different country contexts. A Markov model parametrised to Brazil, South Africa and the UK was developed to compare the cost-effectiveness of three TBI testing strategies: (1) Diaskintest (DST), (2) TST test, and (3) IGRA QFT test. Univariate and probabilistic sensitivity analyses over unit costs and main parameters were performed. Our modelling results show that Diaskintest saves $5.60 and gains 0.024 QALYs per patient and $8.40, and 0.01 QALYs per patient in Brazil, compared to TST and IGRA respectively. In South Africa, Diaskintest is also cost-saving at $4.39, with 0.015 QALYs per patient gained, compared to TST, and $64.41, and 0.007 QALYs per patient, compared to IGRA. In the UK, Diaskintest saves $73.33, and gaines 0.0351 QALYs per patient, compared to TST. However, Diaskintest, compared to IGRA, showed an incremental cost of $521.45 (95% CI (500.94-545.07)) per QALY, below the willingness-to-pay threshold of $20.223 per QALY. Diaskintest potentially saves costs and results in greater health gains than the TST and IGRA tests in Brazil and South Africa. In the UK Diaskintest would gain health but also be more costly. Our results have potential external validity because TBST remained cost-effective despite extensive sensitivity analyses.

Public health benefits of shifting from hospital-focused to ambulatory TB care in Eastern Europe: Optimising TB investments in Belarus, the Republic of Moldova, and Romania.

High rates of drug-resistant tuberculosis (DR-TB) continue to threaten public health, especially in Eastern Europe. Costs for treating DR-TB are substantially higher than treating drug-susceptible TB, and higher yet if DR-TB services are delivered in hospital. The WHO recommends that multidrug-resistant (MDR) TB be treated using mainly ambulatory care, shown to have non-inferior health outcomes, however, there has been a delay to transition away from hospital-focused MDR-TB care in certain Eastern European countries. Allocative efficiency analyses were conducted for three countries in Eastern Europe, Belarus, the Republic of Moldova, and Romania, to minimise a combination of TB incidence, prevalence, and mortality by 2035. A primary focus of these studies was to determine the health benefits and financial savings that could be realised if DR-TB service delivery shifted from hospital-focused to ambulatory care. Here we provide a comprehensive assessment of findings from these studies to demonstrate the collective benefit of transitioning from hospital-focused to ambulatory TB care, and to address common regional considerations. We highlight that transitioning from hospital-focused to ambulatory TB care could reduce treatment costs by 20% in Romania, 24% in Moldova, and by as much as 40% in Belarus or almost 35 million US dollars across these three countries by 2035 without affecting quality of care. Improved TB outcomes could be achieved, however, without additional spending by reinvesting these savings in higher-impact TB diagnosis and more efficacious DR-TB treatment regimens. We found commonalities in the large portion of TB cases treated in hospital across these three regional countries, and similar obstacles to transitioning to ambulatory care. National governments in the Eastern European region should examine barriers delaying adoption of ambulatory DR-TB care and consider lost opportunities caused by delays in switching to more efficient treatment modes.

Evaluating the equity impact and cost-effectiveness of digital adherence technologies with differentiated care to support tuberculosis treatment adherence in Ethiopia: protocol and analysis plan for the health economics component of a cluster randomised trial.

BACKGROUND: Tuberculosis remains a leading infectious cause of death in resource-limited settings. Effective treatment is the cornerstone of tuberculosis control, reducing mortality, recurrence and transmission. Supporting treatment adherence through facility-based observations of medication taking can be costly to providers and patients. Digital adherence technologies (DATs) may facilitate treatment monitoring and differentiated care. The ASCENT-Ethiopia study is a three-arm cluster randomised trial assessing two DATs with differentiated care for supporting tuberculosis treatment adherence in Ethiopia. This study is part of the ASCENT consortium, assessing DATs in South Africa, the Philippines, Ukraine, Tanzania and Ethiopia. The aim of this study is to determine the costs, cost-effectiveness and equity impact of implementing DATs in Ethiopia. METHODS AND DESIGN: A total of 78 health facilities have been randomised (1:1:1) into one of two intervention arms or a standard-of-care arm. Approximately 50 participants from each health facility will be enrolled on the trial. Participants in facilities randomised to the intervention arms are offered a DAT linked to the ASCENT adherence platform for daily adherence monitoring and differentiated response for those who have missed doses. Participants at standard-of-care facilities receive routine care. Treatment outcomes and resource utilisation will be measured for each participant. The primary effectiveness outcome is a composite index of unfavourable end-of-treatment outcomes (lost to follow-up, death or treatment failure) or treatment recurrence within 6 months of end-of-treatment. For the cost-effectiveness analysis, end-of-treatment outcomes will be used to estimate disability-adjusted life years (DALYs) averted. Provider and patient cost data will be collected from a subsample of 5 health facilities per study arm, 10 participants per facility (n = 150). We will conduct a societal cost-effectiveness analysis using Bayesian hierarchical models that account for the individual-level correlation between costs and outcomes as well as intra-cluster correlation. An equity impact analysis will be conducted to summarise equity efficiency trade-offs. DISCUSSION: Trial enrolment is ongoing. This paper follows the published trial protocol and describes the protocol and analysis plan for the health economics work package of the ASCENT-Ethiopia trial. This analysis will generate economic evidence to inform the implementation of DATs in Ethiopia and globally. TRIAL REGISTRATION: Pan African Clinical Trial Registry (PACTR) PACTR202008776694999. Registered on 11 August 2020,  https://pactr.samrc.ac.za/TrialDisplay.aspx?TrialID=12241 .

Shaping care home COVID-19 testing policy: a protocol for a pragmatic cluster randomised controlled trial of asymptomatic testing compared with standard care in care home staff (VIVALDI-CT).

INTRODUCTION: Care home residents have experienced significant morbidity, mortality and disruption following outbreaks of SARS-CoV-2. Regular SARS-CoV-2 testing of care home staff was introduced to reduce transmission of infection, but it is unclear whether this remains beneficial. This trial aims to investigate whether use of regular asymptomatic staff testing, alongside funding to reimburse sick pay for those who test positive and meet costs of employing agency staff, is a feasible and effective strategy to reduce COVID-19 impact in care homes. METHODS AND ANALYSIS: The VIVALDI-Clinical Trial is a multicentre, open-label, cluster randomised controlled, phase III/IV superiority trial in up to 280 residential and/or nursing homes in England providing care to adults aged >65 years. All regular and agency staff will be enrolled, excepting those who opt out. Homes will be randomised to the intervention arm (twice weekly asymptomatic staff testing for SARS-CoV-2) or the control arm (current national testing guidance). Staff who test positive for SARS-CoV-2 will self-isolate and receive sick pay. Care providers will be reimbursed for costs associated with employing temporary staff to backfill for absence arising directly from the trial.The trial will be delivered by a multidisciplinary research team through a series of five work packages.The primary outcome is the incidence of COVID-19-related hospital admissions in residents. Secondary outcomes include the number and duration of outbreaks and home closures. Health economic and modelling analyses will investigate the cost-effectiveness and cost consequences of the testing intervention. A process evaluation using qualitative interviews will be conducted to understand intervention roll out and identify areas for optimisation to inform future intervention scale-up, should the testing approach prove effective and cost-effective. Stakeholder engagement will be undertaken to enable the sector to plan for results and their implications and to coproduce recommendations on the use of testing for policy-makers. ETHICS AND DISSEMINATION: The study has been approved by the London-Bromley Research Ethics Committee (reference number 22/LO/0846) and the Health Research Authority (22/CAG/0165). The results of the trial will be disseminated regardless of the direction of effect. The publication of the results will comply with a trial-specific publication policy and will include submission to open access journals. A lay summary of the results will also be produced to disseminate the results to participants. TRIAL REGISTRATION NUMBER: ISRCTN13296529.

Transmission of gram-negative antibiotic-resistant bacteria following differing exposure to antibiotic-resistance reservoirs in a rural community: a modelling study for bloodstream infections.

Exposure to community reservoirs of gram-negative antibiotic-resistant bacteria (GN-ARB) genes poses substantial health risks to individuals, complicating potential infections. Transmission networks and population dynamics remain unclear, particularly in resource-poor communities. We use a dynamic compartment model to assess GN-ARB transmission quantitatively, including the susceptible, colonised, infected, and removed populations at the community-hospital interface. We used two side streams to distinguish between individuals at high- and low-risk exposure to community ARB reservoirs. The model was calibrated using data from a cross-sectional cohort study (N = 357) in Chile and supplemented by existing literature. Most individuals acquired ARB from the community reservoirs (98%) rather than the hospital. High exposure to GN-ARB reservoirs was associated with 17% and 16% greater prevalence for GN-ARB carriage in the hospital and community settings, respectively. The higher exposure has led to 16% more infections and attributed mortality. Our results highlight the need for early-stage identification and testing capability of bloodstream infections caused by GN-ARB through a faster response at the community level, where most GN-ARB are likely to be acquired. Increasing treatment rates for individuals colonised or infected by GN-ARB and controlling the exposure to antibiotic consumption and GN-ARB reservoirs, is crucial to curve GN-ABR transmission.

National tuberculosis spending efficiency and its associated factors in 121 low-income and middle-income countries, 2010-19: a data envelopment and stochastic frontier analysis.

BACKGROUND: Maximising the efficiency of national tuberculosis programmes is key to improving service coverage, outcomes, and progress towards End TB targets. We aimed to determine the overall efficiency of tuberculosis spending and investigate associated factors in 121 low-income and middle-income countries between 2010 and 2019. METHODS: In this data envelopment and stochastic frontier analysis, we used data from the WHO Global TB report series on tuberculosis spending as the input and treatment coverage as the output to estimate tuberculosis spending efficiency. We investigated associations between 25 independent variables and overall efficiency. FINDINGS: We estimated global tuberculosis spending efficiency to be between 73·8% (95% CI 71·2-76·3) and 87·7% (84·9-90·6) in 2019, depending on the analytical method used. This estimate suggests that existing global tuberculosis treatment coverage could be increased by between 12·3% (95% CI 9·4-15·1) and 26·2% (23·7-28·8) for the same amount of spending. Efficiency has improved over the study period, mainly since 2015, but a substantial difference of 70·7-72·1 percentage points between the most and least efficient countries still exists. We found a consistent significant association between efficiency and current health expenditure as a share of gross domestic product, out-of-pocket spending on health, and some Sustainable Development Goal (SDG) indicators such as universal health coverage. INTERPRETATION: To improve efficiency, treatment coverage will need to be increased, particularly in the least efficient contexts where this might require additional spending. However, progress towards global End TB targets is slow even in the most efficient countries. Variables associated with TB spending efficiency suggest efficiency is complimented by commitments to improving health-care access that is free at the point of use and wider progress towards the SDGs. These findings support calls for additional investment in tuberculosis care. FUNDING: None.

Tackling TB in migrants arriving at Europe's southern border.

Over a quarter of the individuals diagnosed with tuberculosis [TB] in the European Union region are born outside of the area and the proportion has been increasing steadily. Italy is a low TB incidence country with over 50% of TB cases in the foreign-born population primarily due to the high numbers of migrants entering the country via land or sea. As a case study to evaluate the value of screening in newly arrived migrants, the EDETECT-TB project in Italy implemented and evaluated active TB screening in the migrant population at first reception centres to ensure early diagnosis to avoid further spread. Based on a cost-effectiveness analysis from a program provider perspective, a decision tree model allowed the assessment of the value for money of case finding by estimating the cost per case of active TB detected compared with the status quo of no screening. The analysis confirmed that early case detection is a cost-effective intervention in areas with migrants arriving from high TB risk settings. Targeted post-arrival early screening of high TB risk vulnerable new entrants to Italy has a potential role in reducing the spread of TB among migrants.

Modelling SARS-COV2 Spread in London: Approaches to Lift the Lockdown.

OBJECTIVE: To use mathematical models to predict the epidemiological impact of lifting the lockdown in London, UK, and alternative strategies to help inform policy in the UK. METHODS: A mathematical model for the transmission of SARS-CoV2 in London. The model was parametrised using data on notified cases, deaths, contacts, and mobility to analyse the epidemic in the UK capital. We investigated the impact of multiple non pharmaceutical interventions (NPIs) and combinations of these measures on future incidence of COVID-19. RESULTS: Immediate action at the early stages of an epidemic in the affected districts would have tackled spread. While an extended lockdown is highly effective, other measures such as shielding older populations, universal testing and facemasks can all potentially contribute to a reduction of infections and deaths. However, based on current evidence it seems unlikely they will be as effective as continued lockdown. In order to achieve elimination and lift lockdown within 5 months, the best strategy seems to be a combination of weekly universal testing, contact tracing and use of facemasks, with concurrent lockdown. This approach could potentially reduce deaths by 48% compared with continued lockdown alone. CONCLUSIONS: A combination of NPIs such as universal testing, contact tracing and mask use while under lockdown would be associated with least deaths and infections. This approach would require high uptake and sustained local effort but it is potentially feasible as may lead to elimination in a relatively short time scale.

Modelling the dynamics of EBV transmission to inform a vaccine target product profile and future vaccination strategy.

Epstein-Barr virus (EBV) is one of the most common human viruses and the cause of pathologies such as infectious mononucleosis (IM) and certain cancers. No vaccine against EBV infection currently exists, but such vaccines are in development. Knowledge of how EBV is transmitted at the population level is critical to the development of target product profiles (TPPs) for such vaccines and future vaccination strategies. We present the first mathematical model of EBV transmission, parameterised using data from England, and use it to compare hypothetical prophylactic vaccines with different characteristics and the impact of vaccinating different age groups. We found that vaccine duration had more impact than vaccine efficacy on modelled EBV and IM prevalence. The age group vaccinated also had an important effect: vaccinating at a younger age led to a greater reduction in seroprevalence but an increase in IM cases associated with delayed infection. Vaccination had impact on cancer incidence only in the long run, because in England most EBV-related cancers arise in later life. Durability of protection should be a key factor to prioritise in EBV vaccine development and included in vaccine TPPs. These findings are timely and important for vaccine developers and policy-makers alike.

Analytical modelling of the spread of disease in confined and crowded spaces.

Since 1927 and until recently, most models describing the spread of disease have been of compartmental type, based on the assumption that populations are homogeneous and well-mixed. Recent models have utilised agent-based models and complex networks to explicitly study heterogeneous interaction patterns, but this leads to an increasing computational complexity. Compartmental models are appealing because of their simplicity, but their parameters, especially the transmission rate, are complex and depend on a number of factors, which makes it hard to predict how a change of a single environmental, demographic, or epidemiological factor will affect the population. Therefore, in this contribution we propose a middle ground, utilising crowd-behaviour research to improve compartmental models in crowded situations. We show how both the rate of infection as well as the walking speed depend on the local crowd density around an infected individual. The combined effect is that the rate of infection at a population scale has an analytically tractable non-linear dependency on crowd density. We model the spread of a hypothetical disease in a corridor and compare our new model with a typical compartmental model, which highlights the regime in which current models may not produce credible results.