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

Tabby2: a user-friendly web tool for forecasting state-level TB outcomes in the United States

BACKGROUND

In the United States, the tuberculosis (TB) disease burden and associated factors vary substantially across states. While public health agencies must choose how to deploy resources to combat TB and latent tuberculosis infection (LTBI), state-level modeling analyses to inform policy decisions have not been widely available.

METHODS

We developed a mathematical model of TB epidemiology linked to a web-based user interface - Tabby2. The model is calibrated to epidemiological and demographic data for the United States, each U.S. state, and the District of Columbia. Users can simulate pre-defined scenarios describing approaches to TB prevention and treatment or create their own intervention scenarios. Location-specific results for epidemiological outcomes, service utilization, costs, and cost-effectiveness are reported as downloadable tables and customizable visualizations. To demonstrate the tool's functionality, we projected trends in TB outcomes without additional intervention for all 50 states and the District of Columbia. We further undertook a case study of expanded treatment of LTBI among non-U.S.-born individuals in Massachusetts, covering 10% of the target population annually over 2025-2029.

RESULTS

Between 2022 and 2050, TB incidence rates were projected to decline in all states and the District of Columbia. Incidence projections for the year 2050 ranged from 0.03 to 3.8 cases (median 0.95) per 100,000 persons. By 2050, we project that majority (> 50%) of TB will be diagnosed among non-U.S.-born persons in 46 states and the District of Columbia; per state percentages range from 17.4% to 96.7% (median 83.0%). In Massachusetts, expanded testing and treatment for LTBI in this population was projected to reduce cumulative TB cases between 2025 and 2050 by 6.3% and TB-related deaths by 8.4%, relative to base case projections. This intervention had an incremental cost-effectiveness ratio of $180,951 (2020 USD) per quality-adjusted life year gained from the societal perspective.

CONCLUSIONS

Tabby2 allows users to estimate the costs, impact, and cost-effectiveness of different TB prevention approaches for multiple geographic areas in the United States. Expanded testing and treatment for LTBI could accelerate declines in TB incidence in the United States, as demonstrated in the Massachusetts case study.

Ending tuberculosis: the cost of missing the World Health Organization target in a low-incidence country

BACKGROUND

Ending tuberculosis (TB) is a global priority and targets for doing so are outlined in the World Health Organization (WHO) End TB Strategy. For low-incidence countries, eliminating TB requires high levels of wealth, low levels of income inequality and effective TB programmes and services that can meet the needs of people who have not benefited from these and are still at risk of TB. In Ireland, numerous reports have noted a need for more funding for TB prevention and control.

AIM

The aim of this research was to estimate the cost of not meeting the WHO End TB target of a 90% reduction in TB incidence in Ireland between 2015 and 2035.

METHODS

The cost of projected TB cases between 2022 and 2035 is estimated based on trends in surveillance data for the period 2015 to 2019 and outcomes reported in the literature.

RESULTS

Between 2022 and 2035, it is projected that a failure to meet the WHO End TB Strategy target will result in an additional 989 cases of TB, 577.3 disability-adjusted life years and 35 deaths with TB in Ireland. The cost of this is estimated to be €70.779 million.

CONCLUSION

Given the estimated cost, Ireland's current prospects of eliminating TB and the tendency towards programmatic funding internationally, greater investment in TB prevention and control in Ireland is justifiable. A national elimination strategy with actions at the levels of the social determinants of health, the health system and the TB programme should be funded.

Impact of interventions for tuberculosis prevention and care in South Africa - a systematic review of mathematical modelling studies

BACKGROUND

Substantial additional efforts are needed to prevent, find and successfully treat tuberculosis (TB) in South Africa (SA). In thepast decade, an increasing body of mathematical modelling research has investigated the population-level impact of TB prevention and careinterventions. To date, this evidence has not been assessed in the SA context.

OBJECTIVE

To systematically review mathematical modelling studies that estimated the impact of interventions towards the World HealthOrganization's End TB Strategy targets for TB incidence, TB deaths and catastrophic costs due to TB in SA.

METHODS

We searched the PubMed, Web of Science and Scopus databases for studies that used transmission-dynamic models of TB in SAand reported on at least one of the End TB Strategy targets at population level. We described study populations, type of interventions andtheir target groups, and estimates of impact and other key findings. For studies of country-level interventions, we estimated average annualpercentage declines (AAPDs) in TB incidence and mortality attributable to the intervention.

RESULTS

We identified 29 studies that met our inclusion criteria, of which 7 modelled TB preventive interventions (vaccination,antiretroviral treatment (ART) for HIV, TB preventive treatment (TPT)), 12 considered interventions along the care cascade for TB(screening/case finding, reducing initial loss to follow-up, diagnostic and treatment interventions), and 10 modelled combinationsof preventive and care-cascade interventions. Only one study focused on reducing catastrophic costs due to TB. The highest impactof a single intervention was estimated in studies of TB vaccination, TPT among people living with HIV, and scale-up of ART. Forpreventive interventions, AAPDs for TB incidence varied between 0.06% and 7.07%, and for care-cascade interventions between 0.05%and 3.27%.

CONCLUSION

We describe a body of mathematical modelling research with a focus on TB prevention and care in SA. We found higherestimates of impact reported in studies of preventive interventions, highlighting the need to invest in TB prevention in SA. However, studyheterogeneity and inconsistent baseline scenarios limit the ability to compare impact estimates between studies. Combinations, rather thansingle interventions, are likely needed to reach the End TB Strategy targets in SA.

Innovating tuberculosis prevention to achieve universal health coverage in the Philippines

To contribute to tuberculosis (TB) elimination, TB preventive treatment (TPT) should integrate innovative approaches including tele-contact investigation (TCI), mathematical modelling, and participatory governance. Aligning with the World Health Organisation's primary health care framework, supply is provided by the provincial health system, demand is cultivated by the community, while governance is represented by the governor, who oversees the health leadership structure, local policies, and allocation of resources. A healthy dynamic between these three components is required to achieve universal health coverage (UHC). Because of their potential to integrate health systems and engage communities, primary health care principles underpin an effective approach to TB prevention. First, the provincial health system should connect with the community through TCI to transform the status quo of passive service delivery. Second, community participation should strengthen the linkage between the health system and governance, which ensures that community action plans are aligned with provincial TPT targets. Third, governance should leverage mathematical modelling to allocate resources to those with greatest need. Central to this is a reliable TB information system that should validate a robust mathematical model to measure cost-effectiveness of the intervention. Collectively, this holistic approach to TB prevention could provide a proof-of-concept that investing in primary health care is the key to UHC.

Population benefits of addressing programmatic and social determinants of gender disparities in tuberculosis in Viet Nam: A modelling study

High prevalence of infectious tuberculosis among men suggests potential population-wide benefits from addressing programmatic and social determinants of gender disparities. Utilising a sex-stratified compartmental transmission model calibrated to tuberculosis burden estimates for Viet Nam, we modelled interventions to increase active case finding, to reduce tobacco smoking, and to reduce alcohol consumption by 2025 in line with national and global targets. For each intervention, we examined scenarios differentially targeting men and women and evaluated impact on tuberculosis morbidity and mortality in men, women, and children in 2035. Active case finding interventions targeting men projected greater reductions in tuberculosis incidence in men, women, and children (16.2%, uncertainty interval, UI, 11.4-23.0%, 11.8%, UI 8.0-18.6%, and 21.5%, UI 16.9-28.5%, respectively) than those targeting women (5.2%, UI 3.8-7.1%, 5.4%, UI 3.9-7.3%, and 8.6%, UI 6.9-10.7%, respectively). Projected reductions in tuberculosis incidence for interventions to reduce male tobacco smoking and alcohol consumption were greatest for men (17.4%, UI 11.8-24.7%, and 11.0%, UI 5.4-19.4%, respectively), but still substantial for women (6.9%, UI 3.8-12.5%, and 4.4%, UI 1.9-10.6%, respectively) and children (12.7%, UI 8.4-19.0%, and 8.0%, UI 3.9-15.0%, respectively). Comparable interventions targeting women projected limited impact, with declines of 0.3% (UI 0.2%-0.3%) and 0.1% (UI 0.0%-0.1%), respectively. Addressing programmatic and social determinants of men's tuberculosis burden has population-wide benefits. Future interventions to increase active case finding, to reduce tobacco smoking, and to reduce harmful alcohol consumption, whilst not ignoring women, should focus on men to most effectively reduce tuberculosis morbidity and mortality in men, women, and children.

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.

Tuberculosis (TB) remains a leading global cause of death and morbidity, and 85% of deaths occur in countries where resources for TB care and control are limited. Many countries cannot finance all TB interventions or technologies, which means difficult decisions on what to prioritise and publically finance. Modelling tools can help decision-makers set priorities based on evidence, in a systematic and transparent way. This study presents Optima TB, a tool that estimates which allocations of spending across interventions will most likely maximise specified objectives—such as minimising TB deaths, prevalence and incidence. In partnership with local decision-makers and stakeholders, Optima TB was applied in Belarus. Recommendations from the model findings include focussing investment on outpatient rather than inpatient care and actively finding people with TB (e.g. through contact tracing) rather than mass testing of the population. The recommended reallocations of spending could reduce TB prevalence and deaths by up to 45% and 50%, respectively, by 2035 for the same amount of spending. Key stakeholders were engaged throughout the analysis and findings and uncertainty around the results were clearly communicated with decision-makers. The timeliness of the results helped inform national dialogue on TB care reform, among other key policy discussions.

Thinking clearly about social aspects of infectious disease transmission

Social and cultural forces shape almost every aspect of infectious disease transmission in human populations, as well as our ability to measure, understand, and respond to epidemics. For directly transmitted infections, pathogen transmission relies on human-to-human contact, with kinship, household, and societal structures shaping contact patterns that in turn determine epidemic dynamics. Social, economic, and cultural forces also shape patterns of exposure, health-seeking behaviour, infection outcomes, the likelihood of diagnosis and reporting of cases, and the uptake of interventions. Although these social aspects of epidemiology are hard to quantify and have limited the generalizability of modelling frameworks in a policy context, new sources of data on relevant aspects of human behaviour are increasingly available. Researchers have begun to embrace data from mobile devices and other technologies as useful proxies for behavioural drivers of disease transmission, but there is much work to be done to measure and validate these approaches, particularly for policy-making. Here we discuss how integrating local knowledge in the design of model frameworks and the interpretation of new data streams offers the possibility of policy-relevant models for public health decision-making as well as the development of robust, generalizable theories about human behaviour in relation to infectious diseases.

Tuberculosis in the Western Pacific Region: Estimating the burden of disease and return on investment 2020-2030 in four countries

Background

We aimed to estimate the disease burden of Tuberculosis (TB) and return on investment of TB care in selected high-burden countries of the Western Pacific Region (WPR) until 2030.

Methods

We projected the TB epidemic in Viet Nam and Lao People's Democratic Republic (PDR) 2020–2030 using a mathematical model under various scenarios: counterfactual (no TB care); baseline (TB care continues at current levels); and 12 different diagnosis and treatment interventions. We retrieved previous modeling results for China and the Philippines. We pooled the new and existing information on incidence and deaths in the four countries, covering >80% of the TB burden in WPR. We estimated the return on investment of TB care and interventions in Viet Nam and Lao PDR using a Solow model.

Findings

In the baseline scenario, TB incidence in the four countries decreased from 97•0/100,000/year (2019) to 90•1/100,000/year (2030), and TB deaths from 83,300/year (2019) to 71,100/year (2030). Active case finding (ACF) strategies (screening people not seeking care for respiratory symptoms) were the most effective single interventions. Return on investment (2020–2030) for TB care in Viet Nam and Lao PDR ranged US$4-US$49/dollar spent; additional interventions brought up to US$2•7/dollar spent.

Interpretation

In the modeled countries, TB incidence will only modestly decrease without additional interventions. Interventions that include ACF can reduce TB burden but achieving the End TB incidence and mortality targets will be difficult without new transformational tools (e.g. vaccine, new diagnostic tools, shorter treatment). However, TB care, even at its current level, can bring a multiple-fold return on investment.

Funding

World Health Organization Western Pacific Regional Office; Swiss National Science Foundation Grant 163878.

Building resource constraints and feasibility considerations in mathematical models for infectious disease: A systematic literature review

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Mathematical model capabilities to explore complex systems now enable priority-setting to consider local resource constraints.

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Common objectives of model-based analyses incorporating constraints are to assess real-world feasibility or allocate resources efficiently.

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Constraints may be incorporated via (i) model-based estimation; (ii) linkage of mathematical and health system models; or (iii) optimisation.

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Models can then project constrained intervention effects and costs and resource requirement s for delivering interventions at full scale.

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'Health system constraints' should be systematically defined for routine operationalisation in model-based priority-setting.

Priority setting for infectious disease control is increasingly concerned with physical input constraints and other real-world restrictions on implementation and on the decision process. These health system constraints determine the ‘feasibility’ of interventions and hence impact. However, considering them within mathematical models places additional demands on model structure and relies on data availability. This review aims to provide an overview of published methods for considering constraints in mathematical models of infectious disease.

We systematically searched the literature to identify studies employing dynamic transmission models to assess interventions in any infectious disease and geographical area that included non-financial constraints to implementation. Information was extracted on the types of constraints considered and how these were identified and characterised, as well as on the model structures and techniques for incorporating the constraints.

A total of 36 studies were retained for analysis. While most dynamic transmission models identified were deterministic compartmental models, stochastic models and agent-based simulations were also successfully used for assessing the effects of non-financial constraints on priority setting. Studies aimed to assess reductions in intervention coverage (and programme costs) as a result of constraints preventing successful roll-out and scale-up, and/or to calculate costs and resources needed to relax these constraints and achieve desired coverage levels. We identified three approaches for incorporating constraints within the analyses: (i) estimation within the disease transmission model; (ii) linking disease transmission and health system models; (iii) optimising under constraints (other than the budget).

The review highlighted the viability of expanding model-based priority setting to consider health system constraints. We show strengths and limitations in current approaches to identify and quantify locally-relevant constraints, ranging from simple assumptions to structured elicitation and operational models. Overall, there is a clear need for transparency in the way feasibility is defined as a decision criteria for its systematic operationalisation within models.

Strategic investment in tuberculosis control in the Republic of Bulgaria

As Bulgaria transitions away from Global Fund grant, robust estimates of the comparative impact of the various response strategies under consideration are needed to ensure sustained effectiveness of the tuberculosis (TB) programme. We tailored an established mathematical model for TB control to the epidemic in Bulgaria to project the likely outcomes of seven intervention scenarios. Under existing programmatic conditions projected forward, the country's targets for achieving TB elimination in the coming decades will not be achieved. No interventions under consideration were predicted to accelerate the baseline projected reduction in epidemiological indicators significantly. Discontinuation of the 'Open Doors' program and activities of non-governmental organisations would result in a marked exacerbation of the epidemic (increasing incidence in 2035 by 6-8% relative to baseline conditions projected forward). Changing to a short course regimen for multidrug-resistant TB (MDR-TB) would substantially decrease MDR-TB mortality (by 21.6% in 2035 relative to baseline conditions projected forward). Changing to ambulatory care for eligible patients would not affect TB burden but would be markedly cost-saving. In conclusion, Bulgaria faces important challenges in transitioning to a primarily domestically-financed TB programme. The country should consider maintaining currently effective programs and shifting towards ambulatory care to ensure program sustainability.

Heterogeneous infectiousness in mathematical models of tuberculosis: A systematic review

TB mathematical models employ various assumptions and approaches in dealing with the heterogeneous infectiousness of persons with active TB. We reviewed existing approaches and considered the relationship between them and existing epidemiological evidence. We searched the following electronic bibliographic databases from inception to 9 October 2018: MEDLINE, EMBASE, Biosis, Global Health and Scopus. Two investigators extracted data using a standardised data extraction tool. We included in the review any transmission dynamic model of M. tuberculosis transmission explicitly simulating heterogeneous infectiousness of person with active TB. We extracted information including: study objective, model structure, number of active TB compartments, factors used to stratify the active TB compartment, relative infectiousness of each active TB compartment and any intervention evaluated in the model. Our search returned 1899 unique references, of which the full text of 454 records were assessed for eligibility, and 99 studies met the inclusion criteria. Of these, 89 used compartmental models implemented with ordinary differential equations, while the most common approach to stratification of the active TB compartment was to incorporate two levels of infectiousness. However, various clinical characteristics were used to stratify the active TB compartments, and models differed as to whether they permitted transition between these states. Thirty-four models stratified the infectious compartment according to sputum smear status or pulmonary involvement, while 18 models stratified based on health care-related factors. Variation in infectiousness associated with drug-resistant M. tuberculosis was the rationale for stratifying active TB in 33 models, with these models consistently assuming that drug-resistant active TB cases were less infectious. Given the evidence of extensive heterogeneity in infectiousness of individuals with active TB, an argument exists for incorporating heterogeneous infectiousness, although this should be considered in light of the objectives of the study and the research question. PROSPERO Registration: CRD42019111936.

Simple Inclusion of Complex Diagnostic Algorithms in Infectious Disease Models for Economic Evaluation

INTRODUCTION

Cost-effectiveness models for infectious disease interventions often require transmission models that capture the indirect benefits from averted subsequent infections. Compartmental models based on ordinary differential equations are commonly used in this context. Decision trees are frequently used in cost-effectiveness modeling and are well suited to describing diagnostic algorithms. However, complex decision trees are laborious to specify as compartmental models and cumbersome to adapt, limiting the detail of algorithms typically included in transmission models.

METHODS

We consider an approximation replacing a decision tree with a single holding state for systems where the time scale of the diagnostic algorithm is shorter than time scales associated with disease progression or transmission. We describe recursive algorithms for calculating the outcomes and mean costs and delays associated with decision trees, as well as design strategies for computational implementation. We assess the performance of the approximation in a simple model of transmission/diagnosis and its role in simplifying a model of tuberculosis diagnostics.

RESULTS

When diagnostic delays were short relative to recovery rates, our approximation provided a good account of infection dynamics and the cumulative costs of diagnosis and treatment. Proportional errors were below 5% so long as the longest delay in our 2-step algorithm was under 20% of the recovery time scale. Specifying new diagnostic algorithms in our tuberculosis model was reduced from several tens to just a few lines of code.

DISCUSSION

For conditions characterized by a diagnostic process that is neither instantaneous nor protracted (relative to transmission dynamics), this novel approach retains the advantages of decision trees while embedding them in more complex models of disease transmission. Concise specification and code reuse increase transparency and reduce potential for error.

Potential population level impact on tuberculosis incidence of using an mRNA expression signature correlate-of-risk test to target tuberculosis preventive therapy

Achieving the WHO End-Tuberculosis (TB) targets requires approaches to prevent progression to TB among individuals with Mycobacterium tuberculosis (M.tb) infection. Effective preventive therapy (PT) exists, but current tests have low specificity for identifying who, among those infected, is at risk of developing TB. Using mathematical models, we assessed the potential population-level impact on TB incidence of using a new more specific mRNA expression signature (COR) to target PT among HIV-uninfected adults in South Africa. We compared the results to the use of the existing interferon-γ release assay (IGRA). With annual screening coverage of 30% COR-targeted PT could reduce TB incidence in 2035 by 20% (95% CI 15-27). With the same coverage, IGRA-targeted PT could reduce TB incidence by 39% (31-48) but would require greater use of PT resulting in a higher number needed to treat per TB case averted (COR: 49 (29-77); IGRA: 84 (59-123)). The relative differences between COR and IGRA were not sensitive to screening coverage. COR-targeted PT could contribute to reducing total TB burden in high incidence countries like South Africa by allowing more efficient targeting of treatment. To maximise impact, COR-like tests may be best utilised in the highest burden regions, or sub-populations, within these countries.

Estimating the Impact of Tuberculosis Case Detection in Constrained Health Systems: An Example of Case-Finding in South Africa

Mathematical models are increasingly being used to compare strategies for tuberculosis (TB) control and inform policy decisions. Models often do not consider financial and other constraints on implementation and may overestimate the impact that can be achieved. We developed a pragmatic approach for incorporating resource constraints into mathematical models of TB. Using a TB transmission model calibrated for South Africa, we estimated the epidemiologic impact and resource requirements (financial, human resource (HR), and diagnostic) of 9 case-finding interventions. We compared the model-estimated resources with scenarios of future resource availability and estimated the impact of interventions under these constraints. Without constraints, symptom screening in public health clinics and among persons receiving care for human immunodeficiency virus infection was predicted to lead to larger reductions in TB incidence (9.5% (2.5th–97.5th percentile range (PR), 8.6–12.2) and 14.5% (2.5th–97.5th PR, 12.2–16.3), respectively) than improved adherence to diagnostic guidelines (2.7%; 2.5th–97.5th PR, 1.6–4.1). However, symptom screening required large increases in resources, exceeding future HR capacity. Even under our most optimistic HR scenario, the reduction in TB incidence from clinic symptom screening was 0.2%–0.9%—less than that of improved adherence to diagnostic guidelines. Ignoring resource constraints may result in incorrect conclusions about an intervention’s impact and may lead to suboptimal policy decisions. Models used for decision-making should consider resource constraints.

Application of provincial data in mathematical modelling to inform sub-national tuberculosis program decision-making in South Africa

South Africa has the highest tuberculosis (TB) disease incidence rate in the world, and TB is the leading infectious cause of death. Decisions on, and funding for, TB prevention and care policies are decentralised to the provincial governments and therefore, tools to inform policy need to operate at this level. We describe the use of a mathematical model planning tool at provincial level in a high HIV and TB burden country, to estimate the impact on TB burden of achieving the 90-(90)-90 targets of the Stop TB Partnership Global Plan to End TB. "TIME Impact" is a freely available, user-friendly TB modelling tool. In collaboration with provincial TB programme staff, and the South African National TB Programme, models for three (of nine) provinces were calibrated to TB notifications, incidence, and screening data. Reported levels of TB programme activities were used as baseline inputs into the models, which were used to estimate the impact of scale-up of interventions focusing on screening, linkage to care and treatment success. All baseline models predicted a trend of decreasing TB incidence and mortality, consistent with recent data from South Africa. The projected impacts of the interventions differed by province and were greatly influenced by assumed current coverage levels. The absence of provincial TB burden estimates and uncertainty in current activity coverage levels were key data gaps. A user-friendly modelling tool allows TB burden and intervention impact projection at the sub-national level. Key sub-national data gaps should be addressed to improve the quality of sub-national model predictions.

A Bayesian Approach to Understanding Sex Differences in Tuberculosis Disease Burden

Globally, men have a higher epidemiologic burden of tuberculosis (incidence, prevalence, mortality) than women do, possibly due to differences in disease incidence, treatment initiation, self-cure, and/or untreated-tuberculosis mortality rates. Using a simple, sex-stratified compartmental model, we employed a Bayesian approach to explore which factors most likely explain men's higher burden. We applied the model to smear-positive pulmonary tuberculosis in Vietnam (2006-2007) and Malawi (2013-2014). Posterior estimates were consistent with sex-specific prevalence and notifications in both countries. Results supported higher incidence in men and showed that both sexes faced longer durations of untreated disease than estimated by self-reports. Prior untreated disease durations were revised upward 8- to 24-fold, to 2.2 (95% credible interval: 1.7, 2.9) years for men in Vietnam and 2.8 (1.8, 4.1) years for men in Malawi, approximately a year longer than for women in each country. Results imply that substantial sex differences in tuberculosis burden are almost solely attributable to men's disadvantages in disease incidence and untreated disease duration. The latter, for which self-reports provide a poor proxy, implies inadequate coverage of case-finding strategies. These results highlight an urgent need for better understanding of gender-related barriers faced by men and support the systematic targeting of men for screening.

Empirical estimation of resource constraints for use in model-based economic evaluation: an example of TB services in South Africa

BACKGROUND

Evidence on the relative costs and effects of interventions that do not consider 'real-world' constraints on implementation may be misleading. However, in many low- and middle-income countries, time and data scarcity mean that incorporating health system constraints in priority setting can be challenging.

METHODS

We developed a 'proof of concept' method to empirically estimate health system constraints for inclusion in model-based economic evaluations, using intensified case-finding strategies (ICF) for tuberculosis (TB) in South Africa as an example. As part of a strategic planning process, we quantified the resources (fiscal and human) needed to scale up different ICF strategies (cough triage and WHO symptom screening). We identified and characterised three constraints through discussions with local stakeholders: (1) financial constraint: potential maximum increase in public TB financing available for new TB interventions; (2) human resource constraint: maximum current and future capacity among public sector nurses that could be dedicated to TB services; and (3) diagnostic supplies constraint: maximum ratio of Xpert MTB/RIF tests to TB notifications. We assessed the impact of these constraints on the costs of different ICF strategies.

RESULTS

It would not be possible to reach the target coverage of ICF (as defined by policy makers) without addressing financial, human resource and diagnostic supplies constraints. The costs of addressing human resource constraints is substantial, increasing total TB programme costs during the period 2016-2035 by between 7% and 37% compared to assuming the expansion of ICF is unconstrained, depending on the ICF strategy chosen.

CONCLUSIONS

Failure to include the costs of relaxing constraints may provide misleading estimates of costs, and therefore cost-effectiveness. In turn, these could impact the local relevance and credibility of analyses, thereby increasing the risk of sub-optimal investments.

Investigating the impact of TB case-detection strategies and the consequences of false positive diagnosis through mathematical modelling

Background

Increasing case notifications is one of the top programmatic priorities of National TB Control Programmes (NTPs). To find more cases, NTPs often need to consider expanding TB case-detection activities to populations with increasingly low prevalence of disease. Together with low-specificity diagnostic algorithms, these strategies can lead to an increasingly high number of false positive diagnoses, which has important adverse consequences.

Methods

We apply TIME, a widely-used country-level model, to quantify the expected impact of different case-finding strategies under two scenarios. In the first scenario, we compare the impact of implementing two different diagnostic algorithms (higher sensitivity only versus higher sensitivity and specificity) to reach programmatic screening targets. In the second scenario, we examine the impact of expanding coverage to a population with a lower prevalence of disease. Finally, we explore the implications of modelling without taking into consideration the screening of healthy individuals. Outcomes considered were changes in notifications, the ratio of additional false positive to true positive diagnoses, the positive predictive value (PPV), and incidence.

Results

In scenario 1, algorithm A of prolonged cough and GeneXpert yielded fewer additional notifications compared to algorithm B of any symptom and smear microscopy (n = 4.0 K vs 13.8 K), relative to baseline between 2017 and 2025. However, algorithm A resulted in an increase in PPV, averting 2.4 K false positive notifications thus resulting in a more efficient impact on incidence. Scenario 2 demonstrated an absolute decrease of 11% in the PPV as intensified case finding activities expanded into low-prevalence populations without improving diagnostic accuracy, yielding an additional 23 K false positive diagnoses for an additional 1.3 K true positive diagnoses between 2017 and 2025. Modelling the second scenario without taking into account screening amongst healthy individuals overestimated the impact on cases averted by a factor of 6.

Conclusion

Our findings show that total notifications can be a misleading indicator for TB programme performance, and should be interpreted carefully. When evaluating potential case-finding strategies, NTPs should consider the specificity of diagnostic algorithms and the risk of increasing false-positive diagnoses. Similarly, modelling the impact of case-finding strategies without taking into account potential adverse consequences can overestimate impact and lead to poor strategic decision-making.

Electronic supplementary material

The online version of this article (10.1186/s12879-018-3239-x) contains supplementary material, which is available to authorized users.

epidemix-An interactive multi-model application for teaching and visualizing infectious disease transmission

Mathematical models of disease transmission are used to improve our understanding of patterns of infection and to identify factors influencing them. During recent public and animal health crises, such as pandemic influenza, Ebola, Zika, foot-and-mouth disease, models have made important contributions in addressing policy questions, especially through the assessment of the trajectory and scale of outbreaks, and the evaluation of control interventions. However, their mathematical formulation means that they may appear as a "black box" to those without the appropriate mathematical background. This may lead to a negative perception of their utility for guiding policy, and generate expectations, which are not in line with what these models can deliver. It is therefore important for policymakers, as well as public health and animal health professionals and researchers who collaborate with modelers and use results generated by these models for policy development or research purpose, to understand the key concepts and assumptions underlying these models. The software application epidemix (http://shinyapps.rvc.ac.uk) presented here aims to make mathematical models of disease transmission accessible to a wider audience of users. By developing a visual interface for a suite of eight models, users can develop an understanding of the impact of various modelling assumptions - especially mixing patterns - on the trajectory of an epidemic and the impact of control interventions, without having to directly deal with the complexity of mathematical equations and programming languages. Models are compartmental or individual-based, deterministic or stochastic, and assume homogeneous or heterogeneous-mixing patterns (with the probability of transmission depending on the underlying structure of contact networks, or the spatial distribution of hosts). This application is intended to be used by scientists teaching mathematical modelling short courses to non-specialists - including policy makers, public and animal health professionals and students - and wishing to develop hands-on practicals illustrating key concepts of disease dynamics and control.

Impact of malaria interventions on child mortality in endemic African settings: comparison and alignment between LiST and Spectrum-Malaria model

Background

In malaria-endemic countries, malaria prevention and treatment are critical for child health. In the context of intervention scale-up and rapid changes in endemicity, projections of intervention impact and optimized program scale-up strategies need to take into account the consequent dynamics of transmission and immunity.

Methods

The new Spectrum-Malaria program planning tool was used to project health impacts of Insecticide-Treated mosquito Nets (ITNs) and effective management of uncomplicated malaria cases (CMU), among other interventions, on malaria infection prevalence, case incidence and mortality in children 0–4 years, 5–14 years of age and adults. Spectrum-Malaria uses statistical models fitted to simulations of the dynamic effects of increasing intervention coverage on these burdens as a function of baseline malaria endemicity, seasonality in transmission and malaria intervention coverage levels (estimated for years 2000 to 2015 by the World Health Organization and Malaria Atlas Project). Spectrum-Malaria projections of proportional reductions in under-five malaria mortality were compared with those of the Lives Saved Tool (LiST) for the Democratic Republic of the Congo and Zambia, for given (standardized) scenarios of ITN and/or CMU scale-up over 2016–2030.

Results

Proportional mortality reductions over the first two years following scale-up of ITNs from near-zero baselines to moderately higher coverages align well between LiST and Spectrum-Malaria —as expected since both models were fitted to cluster-randomized ITN trials in moderate-to-high-endemic settings with 2-year durations. For further scale-up from moderately high ITN coverage to near-universal coverage (as currently relevant for strategic planning for many countries), Spectrum-Malaria predicts smaller additional ITN impacts than LiST, reflecting progressive saturation. For CMU, especially in the longer term (over 2022–2030) and for lower-endemic settings (like Zambia), Spectrum-Malaria projects larger proportional impacts, reflecting onward dynamic effects not fully captured by LiST.

Conclusions

Spectrum-Malaria complements LiST by extending the scope of malaria interventions, program packages and health outcomes that can be evaluated for policy making and strategic planning within and beyond the perspective of child survival.

Electronic supplementary material

The online version of this article (10.1186/s12889-017-4739-0) contains supplementary material, which is available to authorized users.

Catastrophic costs potentially averted by tuberculosis control in India and South Africa: a modelling study

BACKGROUND

The economic burden on households affected by tuberculosis through costs to patients can be catastrophic. WHO's End TB Strategy recognises and aims to eliminate these potentially devastating economic effects. We assessed whether aggressive expansion of tuberculosis services might reduce catastrophic costs.

METHODS

We estimated the reduction in tuberculosis-related catastrophic costs with an aggressive expansion of tuberculosis services in India and South Africa from 2016 to 2035, in line with the End TB Strategy. Using modelled incidence and mortality for tuberculosis and patient-incurred cost estimates, we investigated three intervention scenarios: improved treatment of drug-sensitive tuberculosis; improved treatment of multidrug-resistant tuberculosis; and expansion of access to tuberculosis care through intensified case finding (South Africa only). We defined tuberculosis-related catastrophic costs as the sum of direct medical, direct non-medical, and indirect costs to patients exceeding 20% of total annual household income. Intervention effects were quantified as changes in the number of households incurring catastrophic costs and were assessed by quintiles of household income.

FINDINGS

In India and South Africa, improvements in treatment for drug-sensitive and multidrug-resistant tuberculosis could reduce the number of households incurring tuberculosis-related catastrophic costs by 6-19%. The benefits would be greatest for the poorest households. In South Africa, expanded access to care could decrease household tuberculosis-related catastrophic costs by 5-20%, but gains would be seen largely after 5-10 years.

INTERPRETATION

Aggressive expansion of tuberculosis services in India and South Africa could lessen, although not eliminate, the catastrophic financial burden on affected households.

FUNDING

Bill & Melinda Gates Foundation.

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.

Modular programming for tuberculosis control, the "AuTuMN" platform

BACKGROUND

Tuberculosis (TB) is now the world's leading infectious killer and major programmatic advances will be needed if we are to meet the ambitious new End TB Targets. Although mathematical models are powerful tools for TB control, such models must be flexible enough to capture the complexity and heterogeneity of the global TB epidemic. This includes simulating a disease that affects age groups and other risk groups differently, has varying levels of infectiousness depending upon the organ involved and varying outcomes from treatment depending on the drug resistance pattern of the infecting strain.

RESULTS

We adopted sound basic principles of software engineering to develop a modular software platform for simulation of TB control interventions ("AuTuMN"). These included object-oriented programming, logical linkage between modules and consistency of code syntax and variable naming. The underlying transmission dynamic model incorporates optional stratification by age, risk group, strain and organ involvement, while our approach to simulating time-variant programmatic parameters better captures the historical progression of the epidemic. An economic model is overlaid upon this epidemiological model which facilitates comparison between new and existing technologies. A "Model runner" module allows for predictions of future disease burden trajectories under alternative scenario situations, as well as uncertainty, automatic calibration, cost-effectiveness and optimisation. The model has now been used to guide TB control strategies across a range of settings and countries, with our modular approach enabling repeated application of the tool without the need for extensive modification for each application.

CONCLUSIONS

The modular construction of the platform minimises errors, enhances readability and collaboration between multiple programmers and enables rapid adaptation to answer questions in a broad range of contexts without the need for extensive re-programming. Such features are particularly important in simulating an epidemic as complex and diverse as TB.

Estimating prevalence trends in adult gonorrhoea and syphilis in low- and middle-income countries with the Spectrum-STI model: results for Zimbabwe and Morocco from 1995 to 2016

Objective

To develop a tool for estimating national trends in adult prevalence of sexually transmitted infections by low- and middle-income countries, using standardised, routinely collected programme indicator data.

Methods

The Spectrum-STI model fits time trends in the prevalence of active syphilis through logistic regression on prevalence data from antenatal clinic-based surveys, routine antenatal screening and general population surveys where available, weighting data by their national coverage and representativeness. Gonorrhoea prevalence was fitted as a moving average on population surveys (from the country, neighbouring countries and historic regional estimates), with trends informed additionally by urethral discharge case reports, where these were considered to have reasonably stable completeness. Prevalence data were adjusted for diagnostic test performance, high-risk populations not sampled, urban/rural and male/female prevalence ratios, using WHO's assumptions from latest global and regional-level estimations. Uncertainty intervals were obtained by bootstrap resampling.

Results

Estimated syphilis prevalence (in men and women) declined from 1.9% (95% CI 1.1% to 3.4%) in 2000 to 1.5% (1.3% to 1.8%) in 2016 in Zimbabwe, and from 1.5% (0.76% to 1.9%) to 0.55% (0.30% to 0.93%) in Morocco. At these time points, gonorrhoea estimates for women aged 15–49 years were 2.5% (95% CI 1.1% to 4.6%) and 3.8% (1.8% to 6.7%) in Zimbabwe; and 0.6% (0.3% to 1.1%) and 0.36% (0.1% to 1.0%) in Morocco, with male gonorrhoea prevalences 14% lower than female prevalence.

Conclusions

This epidemiological framework facilitates data review, validation and strategic analysis, prioritisation of data collection needs and surveillance strengthening by national experts. We estimated ongoing syphilis declines in both Zimbabwe and Morocco. For gonorrhoea, time trends were less certain, lacking recent population-based surveys.

Diagnosis of opportunistic infections: HIV co-infections - tuberculosis

PURPOSE OF REVIEW

Tuberculosis (TB) incidence has declined ∼1.5% annually since 2000, but continued to affect 10.4 million individuals in 2015, with 1/3 remaining undiagnosed or underreported. The diagnosis of TB among those co-infected with HIV is challenging as TB remains the leading cause of death in such individuals. Accurate and rapid diagnosis of active TB will avert mortality in both adults and children, reduce transmission, and assist in timeous decisions for antiretroviral therapy initiation. This review describes advances in diagnosing TB, especially among HIV co-infected individuals, highlights national program's uptake, and impact on patient care.

RECENT FINDINGS

The TB diagnostic landscape has been transformed over the last 5 years. Molecular diagnostics such as Xpert MTB/RIF, which simultaneously detects Mycobacterium tuberculosis (MTB) resistance to rifampicin, has revolutionized TB control programs. WHO endorsed the use of Xpert MTB/RIF in 2010 for use in HIV/TB co-infected patients, and later in 2013 for use as the initial diagnostic test for all adults and children with signs and symptoms of pulmonary TB. Line probe assays (LPAs) are recommended for the detection of rifampicin and isoniazid resistance in sputum smear-positive specimens and mycobacterial cultures. A second-line line probe assay has been recommended for the diagnosis of extensively drug-resistant (XDR)-TB Assays such as the urine lateral flow (LF)-lipoarabinomannan (LAM), can be used at the point of care (POC) and have a niche role to supplement the diagnosis of TB in seriously ill HIV-infected, hospitalized patients with low CD4 cell counts of less than 100 cells/μl. Polyvalent platforms such as the m2000 (Abbott Molecular) and GeneXpert (Cepheid) offer potential for integration of HIV and TB testing services. While the Research and Development (R&D) pipeline appears to be rich at first glance, there are actually few leads for true POC tests that would allow for earlier TB diagnosis or rapid, comprehensive drug susceptibility testing, especially when considering the very high attrition rates observed between biomarker discovery and product market entry.

SUMMARY

In this review, we describe diagnostic strategies specifically for HIV and TB co-infected individuals. Molecular diagnostics in particular within the past 5 years have revolutionized and 'disrupted' this field. They lend themselves to integration of services with platforms capable of polyvalent testing. Impact on patient care is, however, still debatable. What has been highlighted is the need for health system strengthening and for true POC testing that can be used in active case finding.

Spectrum-Malaria: a user-friendly projection tool for health impact assessment and strategic planning by malaria control programmes in sub-Saharan Africa

BACKGROUND

Scale-up of malaria prevention and treatment needs to continue but national strategies and budget allocations are not always evidence-based. This article presents a new modelling tool projecting malaria infection, cases and deaths to support impact evaluation, target setting and strategic planning.

METHODS

Nested in the Spectrum suite of programme planning tools, the model includes historic estimates of case incidence and deaths in groups aged up to 4, 5-14, and 15+ years, and prevalence of Plasmodium falciparum infection (PfPR) among children 2-9 years, for 43 sub-Saharan African countries and their 602 provinces, from the WHO and malaria atlas project. Impacts over 2016-2030 are projected for insecticide-treated nets (ITNs), indoor residual spraying (IRS), seasonal malaria chemoprevention (SMC), and effective management of uncomplicated cases (CMU) and severe cases (CMS), using statistical functions fitted to proportional burden reductions simulated in the P. falciparum dynamic transmission model OpenMalaria.

RESULTS

In projections for Nigeria, ITNs, IRS, CMU, and CMS scale-up reduced health burdens in all age groups, with largest proportional and especially absolute reductions in children up to 4 years old. Impacts increased from 8 to 10 years following scale-up, reflecting dynamic effects. For scale-up of each intervention to 80% effective coverage, CMU had the largest impacts across all health outcomes, followed by ITNs and IRS; CMS and SMC conferred additional small but rapid mortality impacts.

DISCUSSION

Spectrum-Malaria's user-friendly interface and intuitive display of baseline data and scenario projections holds promise to facilitate capacity building and policy dialogue in malaria programme prioritization. The module's linking to the OneHealth Tool for costing will support use of the software for strategic budget allocation. In settings with moderately low coverage levels, such as Nigeria, improving case management and achieving universal coverage with ITNs could achieve considerable burden reductions. Projections remain to be refined and validated with local expert input data and actual policy scenarios.

Impact of the GeneXpert MTB/RIF Technology on Tuberculosis Control

Molecular technology revolutionized the diagnosis of tuberculosis (TB) with a paradigm shift to faster, more sensitive, clinically relevant patient care. The most recent molecular leader is the GeneXpert MTB/RIF assay (Xpert) (Cepheid, Sunnyvale, CA), which was endorsed by the World Health Organization with unprecedented speed in December 2010 as the initial diagnostic for detection of HIV-associated TB and for where high rates of drug resistance are suspected. South Africa elected to take an aggressive smear replacement approach to facilitate earlier diagnosis and treatment through the decision to implement the Xpert assay nationally in March 2011, against the backdrop of approximately 6.3 million HIV-infected individuals, one of highest global TB and HIV coinfection rates, no available implementation models, uncertainties around field performance and program costs, and lack of guidance on how to operationalize the assay into existing complex clinical algorithms. South Africa's national implementation was conducted as a phased, forecasted, and managed approach (March 2011 to September 2013), through political will and both treasury-funded and donor-funded support. Today there are 314 GeneXperts across 207 microscopy centers; over 8 million assays have been conducted, and South Africa accounts for over half the global test cartridge usage. As with any implementation of new technology, challenges were encountered, both predicted and unexpected. This chapter discusses the challenges and consequences of such large-scale implementation efforts, the opportunities for new innovations, and the need to strengthen health systems, as well as the impact of the Xpert assay on rifampin-sensitive and multidrug-resistant TB patient care that translated into global TB control as we move toward the sustainable development goals.

Cost-effectiveness and resource implications of aggressive action on tuberculosis in China, India, and South Africa: a combined analysis of nine models

BACKGROUND

The post-2015 End TB Strategy sets global targets of reducing tuberculosis incidence by 50% and mortality by 75% by 2025. We aimed to assess resource requirements and cost-effectiveness of strategies to achieve these targets in China, India, and South Africa.

METHODS

We examined intervention scenarios developed in consultation with country stakeholders, which scaled up existing interventions to high but feasible coverage by 2025. Nine independent modelling groups collaborated to estimate policy outcomes, and we estimated the cost of each scenario by synthesising service use estimates, empirical cost data, and expert opinion on implementation strategies. We estimated health effects (ie, disability-adjusted life-years averted) and resource implications for 2016-35, including patient-incurred costs. To assess resource requirements and cost-effectiveness, we compared scenarios with a base case representing continued current practice.

FINDINGS

Incremental tuberculosis service costs differed by scenario and country, and in some cases they more than doubled existing funding needs. In general, expansion of tuberculosis services substantially reduced patient-incurred costs and, in India and China, produced net cost savings for most interventions under a societal perspective. In all three countries, expansion of access to care produced substantial health gains. Compared with current practice and conventional cost-effectiveness thresholds, most intervention approaches seemed highly cost-effective.

INTERPRETATION

Expansion of tuberculosis services seems cost-effective for high-burden countries and could generate substantial health and economic benefits for patients, although substantial new funding would be required. Further work to determine the optimal intervention mix for each country is necessary.

FUNDING

Bill & Melinda Gates Foundation.

A Data-Driven Evaluation of the Stop TB Global Partnership Strategy of Targeting Key Populations at Greater Risk for Tuberculosis

Objective

Identifying those infected with tuberculosis (TB) is an important component of any strategy for reducing TB transmission and population prevalence. The Stop TB Global Partnership recently launched an initiative with a focus on key populations at greater risk for TB infection or poor clinical outcomes, due to housing and working conditions, incarceration, low household income, malnutrition, co-morbidities, exposure to tobacco and silica dust, or barriers to accessing medical care. To achieve operational targets, the global health community needs effective, low cost, and large-scale strategies for identifying key populations. Using South Africa as a test case, we assess the feasibility and effectiveness of targeting active case finding to populations with TB risk factors identified from regularly collected sources of data. Our approach is applicable to all countries with TB testing and census data. It allows countries to tailor their outreach activities to the particular risk factors of greatest significance in their national context.

Methods

We use a national database of TB test results to estimate municipality-level TB infection prevalence, and link it to Census data to measure population risk factors for TB including rates of urban households, informal settlements, household income, unemployment, and mobile phone ownership. To examine the relationship between TB prevalence and risk factors, we perform linear regression analysis and plot the set of population characteristics against TB prevalence and TB testing rate by municipality. We overlay lines of best fit and smoothed curves of best fit from locally weighted scatter plot smoothing.

Findings

Higher TB prevalence is statistically significantly associated with more urban municipalities (slope coefficient β₁ = 0.129, p < 0.0001, R² = 0.133), lower mobile phone access (β₁ = -0.053, p < 0.001, R² = 0.089), lower unemployment rates (β₁ = -0.020, p = 0.003, R² = 0.048), and a lower proportion of low-income households (β₁ = -0.048, p < 0.0001, R² = 0.084). Municipalities with more low-income households also have marginally higher TB testing rates, however, this association is not statistically significant (β₁ = -0.025, p = 0.676, R² = 0.001). There is no relationship between TB prevalence and the proportion of informal settlement households (β₁ = 0.021, p = 0.136, R² = 0.014).

Conclusions

These analyses reveal that the set of characteristics identified by the Global Plan as defining key populations do not adequately predict populations with high TB burden. For example, we find that higher TB prevalence is correlated with more urbanized municipalities but not with informal settlements. We highlight several factors that are counter-intuitively those most associated with high TB burdens and which should therefore play a large role in any effective targeting strategy. Targeting active case finding to key populations at higher risk of infection or poor clinical outcomes may prove more cost effective than broad efforts. However, these results should increase caution in current targeting of active case finding interventions.

Feasibility of achieving the 2025 WHO global tuberculosis targets in South Africa, China, and India: a combined analysis of 11 mathematical models

Background

The post-2015 End TB Strategy proposes targets of 50% reduction in tuberculosis incidence and 75% reduction in mortality from tuberculosis by 2025. We aimed to assess whether these targets are feasible in three high-burden countries with contrasting epidemiology and previous programmatic achievements.

Methods

11 independently developed mathematical models of tuberculosis transmission projected the epidemiological impact of currently available tuberculosis interventions for prevention, diagnosis, and treatment in China, India, and South Africa. Models were calibrated with data on tuberculosis incidence and mortality in 2012. Representatives from national tuberculosis programmes and the advocacy community provided distinct country-specific intervention scenarios, which included screening for symptoms, active case finding, and preventive therapy.

Findings

Aggressive scale-up of any single intervention scenario could not achieve the post-2015 End TB Strategy targets in any country. However, the models projected that, in the South Africa national tuberculosis programme scenario, a combination of continuous isoniazid preventive therapy for individuals on antiretroviral therapy, expanded facility-based screening for symptoms of tuberculosis at health centres, and improved tuberculosis care could achieve a 55% reduction in incidence (range 31–62%) and a 72% reduction in mortality (range 64–82%) compared with 2015 levels. For India, and particularly for China, full scale-up of all interventions in tuberculosis-programme performance fell short of the 2025 targets, despite preventing a cumulative 3·4 million cases. The advocacy scenarios illustrated the high impact of detecting and treating latent tuberculosis.

Interpretation

Major reductions in tuberculosis burden seem possible with current interventions. However, additional interventions, adapted to country-specific tuberculosis epidemiology and health systems, are needed to reach the post-2015 End TB Strategy targets at country level.

Funding

Bill and Melinda Gates Foundation

Malaria intervention scale-up in Africa: effectiveness predictions for health programme planning tools, based on dynamic transmission modelling

Background

Scale-up of malaria prevention and treatment needs to continue to further important gains made in the past decade, but national strategies and budget allocations are not always evidence-based. Statistical models were developed summarizing dynamically simulated relations between increases in coverage and intervention impact, to inform a malaria module in the Spectrum health programme planning tool.

Methods

The dynamic Plasmodiumfalciparum transmission model OpenMalaria was used to simulate health effects of scale-up of insecticide-treated net (ITN) usage, indoor residual spraying (IRS), management of uncomplicated malaria cases (CM) and seasonal malaria chemoprophylaxis (SMC) over a 10-year horizon, over a range of settings with stable endemic malaria. Generalized linear regression models (GLMs) were used to summarize determinants of impact across a range of sub-Sahara African settings.

Results

Selected (best) GLMs explained 94–97 % of variation in simulated post-intervention parasite infection prevalence, 86–97 % of variation in case incidence (three age groups, three 3-year horizons), and 74–95 % of variation in malaria mortality. For any given effective population coverage, CM and ITNs were predicted to avert most prevalent infections, cases and deaths, with lower impacts for IRS, and impacts of SMC limited to young children reached. Proportional impacts were larger at lower endemicity, and (except for SMC) largest in low-endemic settings with little seasonality. Incremental health impacts for a given coverage increase started to diminish noticeably at above ~40 % coverage, while in high-endemic settings, CM and ITNs acted in synergy by lowering endemicity. Vector control and CM, by reducing endemicity and acquired immunity, entail a partial rebound in malaria mortality among people above 5 years of age from around 5–7 years following scale-up. SMC does not reduce endemicity, but slightly shifts malaria to older ages by reducing immunity in child cohorts reached.

Conclusion

Health improvements following malaria intervention scale-up vary with endemicity, seasonality, age and time. Statistical models can emulate epidemiological dynamics and inform strategic planning and target setting for malaria control.

Electronic supplementary material

The online version of this article (doi:10.1186/s12936-016-1461-9) contains supplementary material, which is available to authorized users.