The effect of new Mycobacterium tuberculosis infection on the sensitivity of prognostic TB signatures.

BACKGROUND: Tests that identify individuals at greatest risk of TB will allow more efficient targeting of preventive therapy. The WHO target product profile for such tests defines optimal sensitivity of 90% and minimum sensitivity of 75% for predicting incident TB. The CORTIS (Correlate of Risk Targeted Intervention Study) evaluated a blood transcriptomic signature (RISK11) for predicting incident TB in a high transmission setting. RISK11 is able to predict TB disease progression but optimal prognostic performance was limited to a 6-month horizon.METHODS: Using a mathematical model, we estimated how subsequent Mycobacterium tuberculosis (MTB) infection may have contributed to the decline in sensitivity of RISK11. We calculated the effect at different RISK11 thresholds (60% and 26%) and for different assumptions about the risk of MTB infection.RESULTS: Modelled sensitivity over 15 months, excluding new infection, was 28.7% (95% CI 12.3-74.1) compared to 25.0% (95% CI 12.7-45.9) observed in the trial. Modelled sensitivity exceeded the minimum criteria (>75%) over a 9-month horizon at the 60% threshold and over 12 months at the 26% threshold.CONCLUSIONS: The effect of new infection on prognostic signature performance is likely to be small. Signatures such as RISK11 may be most useful in individuals, such as household contacts, where probable time of infection is known.

Estimation of subnational tuberculosis burden: generation and application of a new tool in Indonesia.

SETTING: In many high tuberculosis (TB) burden countries, there is substantial geographical heterogeneity in TB burden. In addition, decisions on TB funding and policy are highly decentralised. Subnational estimates of burden, however, are usually unavailable for planning and target setting.OBJECTIVE and DESIGN: We developed a statistical model termed SUBsET to estimate the distribution of the national TB incidence through a weighted score using selected variables, and applied the model to the 514 districts in Indonesia, which have substantial policy and budgetary autonomy in TB. Estimated incidence was compared to reported facility and domicile-based notifications to estimate the case detection rate (CDR). Local stakeholders led model development and dissemination.RESULTS: The final SUBsET model included district population size, level of urbanisation, socio-economic indicators (living floor space and high school completion), human immunodeficiency virus prevalence and air pollution. We estimated district-level TB incidence to be between 201 and 2,485/100 000/year. The facility-based CDR varied between 0 and 190%, with high variation between neighbouring districts, suggesting strong cross-district health utilisation, which was confirmed by domicile-based CDR estimation. SUBsET results informed district-level TB action plans across Indonesia.CONCLUSION: The SUBsET model could be used to estimate the subnational burden in high-burden countries and inform TB policymaking at the relevant decentralised administrative level.

Improving the quality of modelling evidence used for tuberculosis policy evaluation.

Mathematical modelling is commonly used to evaluate policy options for tuberculosis (TB) control in high-burden countries. Although major policy and funding decisions are made based on these analyses, there is concern about the variability of results produced using modelled policy analyses. We discuss new guidance for country-level TB policy modelling. The guidance was developed by the TB Modelling and Analysis Consortium in collaboration with the World Health Organization Global TB Programme, with input from a range of TB stakeholders (funders, modelling groups, country TB programme staff and subject matter experts). The guidance describes principles for country-level TB modelling, as well as good practices for operationalising the principles. The principles cover technical concerns such as model design, parameterisation and validation, as well as approaches for incorporating modelling into country-led policy making and budgeting. For modellers, this guidance suggests approaches to improve the quality and relevance of modelling undertaken to support country-level planning. For non-modellers, this guidance describes considerations for engaging modelling technical assistance, contributing to a modelling exercise and reviewing the results of modelled analyses. If routinely adopted, this guidance should improve the reliability, transparency and usefulness of modelling for country-level TB policy making. However, this guidance will not address all challenges facing modelling, and ongoing work is needed to improve the empirical evidence base for TB policy evaluation and develop stronger mechanisms for validating models. Increasing country ownership of the modelling process remains a challenge, requiring sustained engagement and capacity building.

Measuring the economic burden for TB patients in the End TB Strategy and Universal Health Coverage frameworks.

Tuberculosis (TB) is a disease of poverty. Ensuring access to health care without the risk of financial hardship due to out-of-pocket health care expenditures (Universal Health Coverage [UHC]) is essential for providing accessible care to underprivileged populations, but this is not enough. The End TB Strategy promotes both patient-centred TB services and social protection measures, which aim to mitigate the economic hardship faced by TB patients and their households due to direct medical and non-medical expenditures, as well as to lost income. The strategy includes a target that no families should face catastrophic total costs due to TB. The indicator linked to this target aims to capture the total economic burden linked to TB care, and thus differs from the 'catastrophic expenditure on health' indicator, a key component of the UHC monitoring framework aligned with the Sustainable Development Goals. Countries, and particularly high TB burden countries, are expected to conduct nationally representative TB patient cost surveys to establish baseline measurements for the catastrophic costs indicator. Findings from these surveys should also help identify entry points for developing policies to ensure better financial and social protection for TB patients. In this paper, we define the key measurable concepts for TB patient cost surveys, notably the types of costs that are captured, and related affordability measures. We discuss methods for measuring these notions in the UHC framework and contrast them with how they are measured in TB patient cost surveys.

Global tuberculosis targets and milestones set for 2016-2035: definition and rationale.

BACKGROUND: Global tuberculosis (TB) targets were set as part of the World Health Organization's End TB Strategy (2016-2035) and the Sustainable Development Goals (2016-2030). OBJECTIVE: To define and explain the rationale for these targets. DESIGN: Scenarios for plausible reductions in TB deaths and cases were developed using empirical evidence from best-performing countries and modelling of the scale-up of under-used interventions and hypothetical TB vaccines. Results were discussed at consultations in 2012 and 2013. A final proposal was presented to the World Health Assembly in 2014 and unanimously endorsed by all Member States. RESULTS: The 2030 targets are a 90% reduction in TB deaths and 80% reduction in TB incidence compared with 2015 levels. The 2035 targets are for reductions of 95% and 90%, respectively. A third target-that no TB-affected households experience catastrophic costs due to the disease by 2020-was also agreed. CONCLUSION: The global TB targets and milestones set for the period 2016-2035 are ambitious. Achieving them requires concerted action on several fronts, but two things are fundamental: 1) progress towards universal health coverage to ensure that everyone with TB can access high-quality treatment; and 2) substantial investment in research and development for new tools to prevent TB disease among the approximately 1.7 billion people infected.

Evidence-informed policy making at country level: lessons learned from the South African Tuberculosis Think Tank.

BACKGROUND: National Tuberculosis Programmes (NTPs) require specialist input to support the development of policy and practice informed by evidence, typically against tight deadlines. OBJECTIVE: To describe lessons learned from establishing a dedicated tuberculosis (TB) think tank to advise the South African NTP on TB policy. INTERVENTION AND EVALUATION METHODS: A national TB think tank was established to advise the NTP in support of evidence-informed policy. Support was provided for activities, including meetings, modelling and regular telephone calls, with a wider network of unpaid expert advisers under an executive committee and working groups. Intervention evaluation used desktop analysis of documentary evidence, interviews and direct observation. RESULTS: The TB Think Tank evolved over time to acquire three key roles: an 'institution', a 'policy dialogue forum' and an 'interface'. Although enthusiasm was high, motivating participation among the NTP and external experts proved challenging. Motivation of working groups was most successful when aligned to a specific need for NTP decision making. Despite challenges, the TB Think Tank contributed to South Africa's first ever TB and human immunodeficiency virus (HIV) investment case, and the decision to create South Africa's first ever ring-fenced grant for TB. The TB Think Tank also assisted the NTP in formulating strategy to accelerate progress towards reaching World Health Organization targets. DISCUSSION: With partners, the TB Think Tank achieved major successes in supporting evidence-informed decision making, and garnered increased funding for TB in South Africa. Identifying ways to increase the involvement of NTP staff and other experts, and keeping the scope of the Think Tank well defined, could facilitate greater impact. Think tank initiatives could be replicated in other settings to support evidence-informed policy making.

Modelling the social and structural determinants of tuberculosis: opportunities and challenges.

INTRODUCTION: Despite the close link between tuberculosis (TB) and poverty, most mathematical models of TB have not addressed underlying social and structural determinants. OBJECTIVE: To review studies employing mathematical modelling to evaluate the epidemiological impact of the structural determinants of TB. METHODS: We systematically searched PubMed and personal libraries to identify eligible articles. We extracted data on the modelling techniques employed, research question, types of structural determinants modelled and setting. RESULTS: From 232 records identified, we included eight articles published between 2008 and 2015; six employed population-based dynamic TB transmission models and two non-dynamic analytic models. Seven studies focused on proximal TB determinants (four on nutritional status, one on wealth, one on indoor air pollution, and one examined overcrowding, socio-economic and nutritional status), and one focused on macro-economic influences. CONCLUSIONS: Few modelling studies have attempted to evaluate structural determinants of TB, resulting in key knowledge gaps. Despite the challenges of modelling such a complex system, models must broaden their scope to remain useful for policy making. Given the intersectoral nature of the interrelations between structural determinants and TB outcomes, this work will require multidisciplinary collaborations. A useful starting point would be to focus on developing relatively simple models that can strengthen our knowledge regarding the potential effect of the structural determinants on TB outcomes.

Is IPT more effective in high-burden settings? Modelling the effect of tuberculosis incidence on IPT impact.

SETTING: Isoniazid preventive therapy (IPT) is effective for preventing active tuberculosis (TB), although its mechanism of action is poorly understood and the optimal disease burden for IPT use has not been defined. OBJECTIVE: To describe the relationship between TB incidence and IPT effectiveness. METHODS: We constructed a model of TB transmission dynamics to investigate IPT effectiveness under various epidemiological settings. The model structure was intended to be highly adaptable to uncertainty in both input parameters and the mechanism of action of IPT. To determine the optimal setting for IPT use, we identified the lowest number needed to treat (NNT) with IPT to prevent one case of active TB. RESULTS: We found that the NNT as a function of TB incidence shows a 'U-shape', whereby IPT impact is greatest at an intermediate incidence and attenuated at both lower and higher incidence levels. This U-shape was observed over a broad range of parameter values; the optimal TB incidence was between 500 and 900 cases per 100 000 per year. CONCLUSIONS: TB burden is a critical factor to consider when making decisions about communitywide implementation of IPT. We believe that the total disease burden should not preclude programmatic application of IPT.

TIME Impact - a new user-friendly tuberculosis (TB) model to inform TB policy decisions.

Tuberculosis (TB) is the leading cause of death from infectious disease worldwide, predominantly affecting low- and middle-income countries (LMICs), where resources are limited. As such, countries need to be able to choose the most efficient interventions for their respective setting. Mathematical models can be valuable tools to inform rational policy decisions and improve resource allocation, but are often unavailable or inaccessible for LMICs, particularly in TB. We developed TIME Impact, a user-friendly TB model that enables local capacity building and strengthens country-specific policy discussions to inform support funding applications at the (sub-)national level (e.g. Ministry of Finance) or to international donors (e.g. the Global Fund to Fight AIDS, Tuberculosis and Malaria).TIME Impact is an epidemiological transmission model nested in TIME, a set of TB modelling tools available for free download within the widely-used Spectrum software. The TIME Impact model reflects key aspects of the natural history of TB, with additional structure for HIV/ART, drug resistance, treatment history and age. TIME Impact enables national TB programmes (NTPs) and other TB policymakers to better understand their own TB epidemic, plan their response, apply for funding and evaluate the implementation of the response.The explicit aim of TIME Impact's user-friendly interface is to enable training of local and international TB experts towards independent use. During application of TIME Impact, close involvement of the NTPs and other local partners also builds critical understanding of the modelling methods, assumptions and limitations inherent to modelling. This is essential to generate broad country-level ownership of the modelling data inputs and results. In turn, it stimulates discussions and a review of the current evidence and assumptions, strengthening the decision-making process in general.TIME Impact has been effectively applied in a variety of settings. In South Africa, it informed the first South African HIV and TB Investment Cases and successfully leveraged additional resources from the National Treasury at a time of austerity. In Ghana, a long-term TIME model-centred interaction with the NTP provided new insights into the local epidemiology and guided resource allocation decisions to improve impact.

Large-scale whole genome sequencing of M. tuberculosis provides insights into transmission in a high prevalence area.

To improve understanding of the factors influencing tuberculosis transmission and the role of pathogen variation, we sequenced all available specimens from patients diagnosed over 15 years in a whole district in Malawi. Mycobacterium tuberculosis lineages were assigned and transmission networks constructed, allowing ≤10 single nucleotide polymorphisms (SNPs) difference. We defined disease as due to recent infection if the network-determined source was within 5 years, and assessed transmissibility from forward transmissions resulting in disease. High-quality sequences were available for 1687 disease episodes (72% of all culture-positive episodes): 66% of patients linked to at least one other patient. The between-patient mutation rate was 0.26 SNPs/year (95% CI 0.21-0.31). We showed striking differences by lineage in the proportion of disease due to recent transmission and in transmissibility (highest for lineage-2 and lowest for lineage-1) that were not confounded by immigration, HIV status or drug resistance. Transmissions resulting in disease decreased markedly over time.

How can mathematical models advance tuberculosis control in high HIV prevalence settings?

Existing approaches to tuberculosis (TB) control have been no more than partially successful in areas with high human immunodeficiency virus (HIV) prevalence. In the context of increasingly constrained resources, mathematical modelling can augment understanding and support policy for implementing those strategies that are most likely to bring public health and economic benefits. In this paper, we present an overview of past and recent contributions of TB modelling in this key area, and suggest a way forward through a modelling research agenda that supports a more effective response to the TB-HIV epidemic, based on expert discussions at a meeting convened by the TB Modelling and Analysis Consortium. The research agenda identified high-priority areas for future modelling efforts, including 1) the difficult diagnosis and high mortality of TB-HIV; 2) the high risk of disease progression; 3) TB health systems in high HIV prevalence settings; 4) uncertainty in the natural progression of TB-HIV; and 5) combined interventions for TB-HIV. Efficient and rapid progress towards completion of this modelling agenda will require co-ordination between the modelling community and key stakeholders, including advocates, health policy makers, donors and national or regional finance officials. A continuing dialogue will ensure that new results are effectively communicated and new policy-relevant questions are addressed swiftly.

Human immunodeficiency virus associated tuberculosis more often due to recent infection than reactivation of latent infection.

BACKGROUND: It is unclear whether human immunodeficiency virus (HIV) increases the risk of tuberculosis (TB) mainly through reactivation or following recent Mycobacterium tuberculosis (re)infection. Within a DNA fingerprint-defined cluster of TB cases, reactivation cases are assumed to be the source of infection for subsequent secondary cases. As HIV-positive TB cases are less likely to be source cases, equal or higher clustering in HIV-positives would suggest that HIV mainly increases the risk of TB following recent infection. METHODS: A systematic review was conducted to identify all studies on TB clustering and HIV infection in HIV-endemic populations. Available individual patient data from eligible studies were pooled to analyse the association between clustering and HIV. RESULTS: Of seven eligible studies, six contributed individual patient data on 2116 patients. Clustering was as, or more, likely in the HIV-positive population, both overall (summary OR 1.26, 95%CI 1.0-1.5), and within age groups (OR 1.50, 95%CI 0.9-2.3; OR 1.00, 95%CI 0.8-1.3 and OR 2.57, 95%CI 1.4-5.7) for ages 15-25, 26-50 and >50 years, respectively. CONCLUSIONS: Our results suggest that HIV infection mainly increases the risk of TB following recent M. tuberculosis transmission, and that TB control measures in HIV-endemic settings should therefore focus on controlling M. tuberculosis transmission rather than treating individuals with latent M. tuberculosis infection.

Human immunodeficiency virus increases the risk of tuberculosis due to recent re-infection in individuals with latent infection.

BACKGROUND: Human immunodeficiency virus associated tuberculosis (TB) disease can follow reactivation of latent Mycobacterium tuberculosis infection or recent (re-)infection with M. tuberculosis. If contemporary TB cases share identical M. tuberculosis strains (i.e., are 'clustered'), the episode is likely to have followed recent (re-)infection, irrespective of evidence of previous latent infection. METHODS: Individuals experiencing a first TB episode between 1996 and 2008 in Karonga District, Northern Malawi, were included if information on M. tuberculosis infection status (from tuberculin tests) before 1990 and a DNA fingerprint from the TB episode were available. We explored differences in proportion clustered by prior M. tuberculosis infection status and HIV status, adjusting for age, sex, bacille Calmette-Guérin scar status and time since tuberculin testing. RESULTS: Of 79 HIV-negative TB cases, those with previous M. tuberculosis infection were much less likely to be clustered than cases without prior infection (29% vs. 77%, adjusted OR = 0.15, 95%CI 0.04-0.59). Among 119 HIV-positive TB cases, clustering was similar in both groups (88% vs. 84%, adjusted OR = 1.85, 95%CI 0.41-8.29). DISCUSSION: HIV infection appears to increase the risk of TB following recent re-infection in patients with latent M. tuberculosis infection. Our results add to the mounting evidence that HIV-associated TB mainly follows recent M. tuberculosis infection.