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

Impact of Community-Wide Tuberculosis Active Case Finding and Human Immunodeficiency Virus Testing on Tuberculosis Trends in Malawi

BACKGROUND

Tuberculosis case-finding interventions are critical to meeting World Health Organization End TB strategy goals. We investigated the impact of community-wide tuberculosis active case finding (ACF) alongside scale-up of human immunodeficiency virus (HIV) testing and care on trends in adult tuberculosis case notification rates (CNRs) in Blantyre, Malawi.

METHODS

Five rounds of ACF for tuberculosis (1-2 weeks of leafleting, door-to-door enquiry for cough and sputum microscopy) were delivered to neighborhoods ("ACF areas") in North-West Blantyre between April 2011 and August 2014. Many of these neighborhoods also had concurrent HIV testing interventions. The remaining neighborhoods in Blantyre City ("non-ACF areas") provided a non-randomized comparator. We analyzed TB CNRs from January 2009 until December 2018. We used interrupted time series analysis to compare tuberculosis CNRs before ACF and after ACF, and between ACF and non-ACF areas.

RESULTS

Tuberculosis CNRs increased in Blantyre concurrently with start of ACF for tuberculosis in both ACF and non-ACF areas, with a larger magnitude in ACF areas. Compared to a counterfactual where pre-ACF CNR trends continued during ACF period, we estimated there were an additional 101 (95% confidence interval [CI] 42 to 160) microbiologically confirmed (Bac+) tuberculosis diagnoses per 100 000 person-years in the ACF areas in 3 and a half years of ACF. Compared to a counterfactual where trends in ACF area were the same as trends in non-ACF areas, we estimated an additional 63 (95% CI 38 to 90) Bac + diagnoses per 100 000 person-years in the same period.

CONCLUSIONS

Tuberculosis ACF was associated with a rapid increase in people diagnosed with tuberculosis in Blantyre.

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.

Integrating economic and health evidence to inform Covid-19 policy in low- and middle- income countries

Covid-19 requires policy makers to consider evidence on both population health and economic welfare. Over the last two decades, the field of health economics has developed a range of analytical approaches and contributed to the institutionalisation of processes to employ economic evidence in health policy. We present a discussion outlining how these approaches and processes need to be applied more widely to inform Covid-19 policy; highlighting where they may need to be adapted conceptually and methodologically, and providing examples of work to date. We focus on the evidential and policy needs of low- and middle-income countries; where there is an urgent need for evidence to navigate the policy trade-offs between health and economic well-being posed by the Covid-19 pandemic.

Costs and cost-effectiveness of a comprehensive tuberculosis case finding strategy in Zambia

Introduction

Active-case finding (ACF) programs have an important role in addressing case detection gaps and halting tuberculosis (TB) transmission. Evidence is limited on the cost-effectiveness of ACF interventions, particularly on how their value is impacted by different operational, epidemiological and patient care-seeking patterns.

Methods

We evaluated the costs and cost-effectiveness of a combined facility and community-based ACF intervention in Zambia that utilized mobile chest X-ray with computer-aided reading/interpretation software and laboratory-based Xpert MTB/RIF testing. Programmatic costs (in 2018 US dollars) were assessed from the health system perspective using prospectively collected cost and operational data. Cost-effectiveness of the ACF intervention was assessed as the incremental cost per TB death averted over a five-year time horizon using a multi-stage Markov state-transition model reflecting patient symptom-associated care-seeking and TB care under ACF compared to passive care.

Results

Over 18 months of field operations, the ACF intervention costed $435 to diagnose and initiate treatment for one person with TB. After accounting for patient symptom-associated care-seeking patterns in Zambia, we estimate that this one-time ACF intervention would incrementally diagnose 407 (7,207 versus 6,800) TB patients and avert 502 (611 versus 1,113) TB-associated deaths compared to the status quo (passive case finding), at an incremental cost of $2,284 per death averted over the next five-year period. HIV/TB mortality rate, patient symptom-associated care-seeking probabilities in the absence of ACF, and the costs of ACF patient screening were key drivers of cost-effectiveness.

Conclusions

A one-time comprehensive ACF intervention simultaneously operating in public health clinics and corresponding catchment communities can have important medium-term impact on case-finding and be cost-effective in Zambia. The value of such interventions increases if targeted to populations with high HIV/TB mortality, substantial barriers (both behavioral and physical) to care-seeking exist, and when ACF interventions can optimize screening by achieving operational efficiency.

Digital chest radiography enhances screening efficiency for pulmonary tuberculosis in primary health clinics, South Africa

BACKGROUND

Optimized tuberculosis (TB) screening in high burden settings is essential for case finding. We evaluated digital chest x-ray with computer-aided detection (CAD) software (d-CXR) for identifying undiagnosed TB in three primary health clinics in South Africa.

METHODS

The cross-sectional study consented adults who were sequentially screened for TB using the World Health Organization (WHO) four symptom questionnaire and d-CXR. Participants reporting ≥1 TB symptom and/or CAD score ≥60 (suggestive of TB) provided two spot sputum for Xpert MTB/RIF Ultra (Xpert Ultra) and liquid culture testing respectively. TB yield (proportion of screened tested positive) and number needed to test [NNT] (no of tests to identify one TB patient) were calculated. Risk factors for microbiologically confirmed or presumed (on radiological grounds) were determined.

RESULTS

Among 3041 participants, 45% (1356/3,041) screened positive on either d-CXR or symptoms. TB yield was 2.3% (71/3041) using Xpert Ultra and 2.7% (82/3041) using Xpert Ultra plus culture. Modelled TB yield (identified by Xpert Ultra) by screening approach was: 1.9% (59/3041) for d-CXR alone, 2.0% (62/3041) for symptoms alone and 2.3% (71/3041) for both. The NNT was 9.7 for d-CXR, 17.8 for symptoms and 19.1 for d-CXR and/or symptom. Males, those with previous TB, untreated HIV or unknown HIV status, and acute illness were at higher risk of developing TB.

CONCLUSION

d-CXR screening identified a similar yield of undiagnosed TB compared to symptom-based screening, however required fewer diagnostic tests. Due to its objective nature, d-CXR screening may improve case detection in clinics.

Operational research: A multidisciplinary approach for the management of infectious disease in a global context

Infectious diseases, both established and emerging, impose a significant burden globally. Successful management of infectious diseases requires considerable effort and a multidisciplinary approach to tackle the complex web of interconnected biological, public health and economic systems. Through a wide range of problem-solving techniques and computational methods, operational research can strengthen health systems and support decision-making at all levels of disease control. From improved understanding of disease biology, intervention planning and implementation, assessing economic feasibility of new strategies, identifying opportunities for cost reductions in routine processes, and informing health policy, this paper highlights areas of opportunity for operational research to contribute to effective and efficient infectious disease management and improved health outcomes.

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.

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

BACKGROUND

Lateral flow urine lipoarabinomannan (LAM) tests could offer important new opportunities for the early detection of tuberculosis (TB). The currently licensed LAM test, Alere Determine TB LAM Ag ('LF-LAM'), performs best in the sickest people living with HIV (PLHIV). However, the technology continues to improve, with newer LAM tests, such as Fujifilm SILVAMP TB LAM ('SILVAMP-LAM') showing improved sensitivity, including amongst HIV-negative patients. It is important to anticipate the epidemiological impact that current and future LAM tests may have on TB incidence and mortality.

METHODS AND FINDINGS

Concentrating on South Africa, we examined the impact that widening LAM test eligibility would have on TB incidence and mortality. We developed a mathematical model of TB transmission to project the impact of LAM tests, distinguishing 'current' tests (with sensitivity consistent with LF-LAM), from hypothetical 'future' tests (having sensitivity consistent with SILVAMP-LAM). We modelled the impact of both tests, assuming full adoption of the 2019 WHO guidelines for the use of these tests amongst those receiving HIV care. We also simulated the hypothetical deployment of future LAM tests for all people presenting to care with TB symptoms, not restricted to PLHIV. Our model projects that 2,700,000 (95% credible interval [CrI] 2,000,000-3,600,000) and 420,000 (95% CrI 350,000-520,000) cumulative TB incident cases and deaths, respectively, would occur between 2020 and 2035 if the status quo is maintained. Relative to this comparator, current and future LAM tests would respectively avert 54 (95% CrI 33-86) and 90 (95% CrI 55-145) TB deaths amongst inpatients between 2020 and 2035, i.e., reductions of 5% (95% CrI 4%-6%) and 9% (95% CrI 7%-11%) in inpatient TB mortality. This impact in absolute deaths averted doubles if testing is expanded to include outpatients, yet remains <1% of country-level TB deaths. Similar patterns apply to incidence results. However, deploying a future LAM test for all people presenting to care with TB symptoms would avert 470,000 (95% CrI 220,000-870,000) incident TB cases (18% reduction, 95% CrI 9%-29%) and 120,000 (95% CrI 69,000-210,000) deaths (30% reduction, 95% CrI 18%-44%) between 2020 and 2035. Notably, this increase in impact arises largely from diagnosis of TB amongst those with HIV who are not yet in HIV care, and who would thus be ineligible for a LAM test under current guidelines. Qualitatively similar results apply under an alternative comparator assuming expanded use of GeneXpert MTB/RIF ('Xpert') for TB diagnosis. Sensitivity analysis demonstrates qualitatively similar results in a setting like Kenya, which also has a generalised HIV epidemic, but a lower burden of HIV/TB coinfection. Amongst limitations of this analysis, we do not address the cost or cost-effectiveness of future tests. Our model neglects drug resistance and focuses on the country-level epidemic, thus ignoring subnational variations in HIV and TB burden.

CONCLUSIONS

These results suggest that LAM tests could have an important effect in averting TB deaths amongst PLHIV with advanced disease. However, achieving population-level impact on the TB epidemic, even in high-HIV-burden settings, will require future LAM tests to have sufficient performance to be deployed more broadly than in HIV care.

The predicted impact of tuberculosis preventive therapy: the importance of disease progression assumptions

BACKGROUND

Following infection with Mycobacterium tuberculosis (M.tb), individuals may rapidly develop tuberculosis (TB) disease or enter a "latent" infection state with a low risk of progression to disease. Mathematical models use a variety of structures and parameterisations to represent this process. The effect of these different assumptions on the predicted impact of TB interventions has not been assessed.

METHODS

We explored how the assumptions made about progression from infection to disease affect the predicted impact of TB preventive therapy. We compared the predictions using three commonly used model structures, and parameters derived from two different data sources.

RESULTS

The predicted impact of preventive therapy depended on both the model structure and parameterisation. At a baseline annual TB incidence of 500/100,000, there was a greater than 2.5-fold difference in the predicted reduction in incidence due to preventive therapy (ranging from 6 to 16%), and the number needed to treat to avert one TB case varied between 67 and 157. The relative importance of structure and parameters depended on baseline TB incidence and assumptions about the efficacy of preventive therapy, with the choice of structure becoming more important at higher incidence.

CONCLUSIONS

The assumptions use to represent progression to disease in models are likely to influence the predicted impact of preventive therapy and other TB interventions. Modelling estimates of TB preventive therapy should consider routinely incorporating structural uncertainty, particularly in higher burden settings. Not doing so may lead to inaccurate and over confident conclusions, and sub-optimal evidence for decision making.

Integrating economic and health evidence to inform Covid-19 policy in low- and middle- income countries

Covid-19 requires policy makers to consider evidence on both population health and economic welfare. Over the last two decades, the field of health economics has developed a range of analytical approaches and contributed to the institutionalisation of processes to employ economic evidence in health policy. We present a discussion outlining how these approaches and processes need to be applied more widely to inform Covid-19 policy; highlighting where they may need to be adapted conceptually and methodologically, and providing examples of work to date. We focus on the evidential and policy needs of low- and middle-income countries; where there is an urgent need for evidence to navigate the policy trade-offs between health and economic well-being posed by the Covid-19 pandemic.

Informing Balanced Investment in Services and Health Systems: A Case Study of Priority Setting for Tuberculosis Interventions in South Africa

OBJECTIVES

Health systems face nonfinancial constraints that can influence the opportunity cost of interventions. Empirical methods to explore their impact, however, are underdeveloped. We develop a conceptual framework for defining health system constraints and empirical estimation methods that rely on routine data. We then present an empirical approach for incorporating nonfinancial constraints in cost-effectiveness models of health benefit packages for the health sector.

METHODS

We illustrate the application of this approach through a case study of defining a package of services for tuberculosis case-finding in South Africa. An economic model combining transmission model outputs with unit costs was developed to examine the cost-effectiveness of alternative screening and diagnostic algorithms. Constraints were operationalized as restrictions on achievable coverage based on: (1) financial resources; (2) human resources; and (3) policy constraints around diagnostics purchasing. Cost-effectiveness of the interventions was assessed under one "unconstrained" and several "constrained" scenarios. For the unconstrained scenario, incremental cost-effectiveness ratios were estimated with and without the costs of "relaxing" constraints.

RESULTS

We find substantial differences in incremental cost-effectiveness ratios across scenarios, leading to variations in the decision rules for prioritizing interventions. In constrained scenarios, the limiting factor for most interventions was not financial, but rather the availability of human resources.

CONCLUSIONS

We find that optimal prioritization among different tuberculosis control strategies in South Africa is influenced by whether and how constraints are taken into consideration. We thus demonstrate both the importance and feasibility of considering nonfinancial constraints in health sector resource allocation models.

Performance of diagnostic and predictive host blood transcriptomic signatures for Tuberculosis disease: A systematic review and meta-analysis

INTRODUCTION

Host blood transcriptomic biomarkers have potential as rapid point-of-care triage, diagnostic, and predictive tests for Tuberculosis disease. We aimed to summarise the performance of host blood transcriptomic signatures for diagnosis of and prediction of progression to Tuberculosis disease; and compare their performance to the recommended World Health Organisation target product profile.

METHODS

A systematic review and meta-analysis of the performance of host blood mRNA signatures for diagnosing and predicting progression to Tuberculosis disease in HIV-negative adults and adolescents, in studies with an independent validation cohort. Medline, Scopus, Web of Science, and EBSCO libraries were searched for articles published between January 2005 and May 2019, complemented by a search of bibliographies. Study selection, data extraction and quality assessment were done independently by two reviewers. Meta-analysis was performed for signatures that were validated in ≥3 comparable cohorts, using a bivariate random effects model.

RESULTS

Twenty studies evaluating 25 signatures for diagnosis of or prediction of progression to TB disease in a total of 68 cohorts were included. Eighteen studies evaluated 24 signatures for TB diagnosis and 17 signatures met at least one TPP minimum performance criterion. Three diagnostic signatures were validated in clinically relevant cohorts to differentiate TB from other diseases, with pooled sensitivity 84%, 87% and 90% and pooled specificity 79%, 88% and 74%, respectively. Four studies evaluated signatures for progression to TB disease and performance of one signature, assessed within six months of TB diagnosis, met the minimal TPP for a predictive test for progression to TB disease.

CONCLUSION

Host blood mRNA signatures hold promise as triage tests for TB. Further optimisation is needed if mRNA signatures are to be used as standalone diagnostic or predictive tests for therapeutic decision-making.