Modeling of Novel Diagnostic Strategies for Active Tuberculosis – A Systematic Review: Current Practices and Recommendations

Introduction: The field of diagnostics for active tuberculosis (TB) is rapidly developing. TB diagnostic modeling can help to inform policy makers and support complicated decisions on diagnostic strategy, with important budgetary implications. Demand for TB diagnostic modeling is likely to increase, and an evaluation of current practice is important. We aimed to systematically review all studies employing mathematical modeling to evaluate cost-effectiveness or epidemiological impact of novel diagnostic strategies for active TB. Methods: Pubmed, personal libraries and reference lists were searched to identify eligible papers. We extracted data on a wide variety of model structure, parameter choices, sensitivity analyses and study conclusions, which were discussed during a meeting of content experts. Results & Discussion From 5619 records a total of 36 papers were included in the analysis. Sixteen papers included population impact/transmission modeling, 5 were health systems models, and 24 included estimates of cost-effectiveness. Transmission and health systems models included specific structure to explore the importance of the diagnostic pathway (n = 4), key determinants of diagnostic delay (n = 5), operational context (n = 5), and the pre-diagnostic infectious period (n = 1). The majority of models implemented sensitivity analysis, although only 18 studies described multi-way sensitivity analysis of more than 2 parameters simultaneously. Among the models used to make cost-effectiveness estimates, most frequent diagnostic assays studied included Xpert MTB/RIF (n = 7), and alternative nucleic acid amplification tests (NAATs) (n = 4). Most (n = 16) of the cost-effectiveness models compared new assays to an existing baseline and generated an incremental cost-effectiveness ratio (ICER). Conclusion: Although models have addressed a small number of important issues, many decisions regarding implementation of TB diagnostics are being made without the full benefits of insight from mathematical models. Further models are needed that address a wider array of diagnostic and epidemiological settings, that explore the inherent uncertainty of models and that include additional epidemiological data on transmission implications of false-negative diagnosis and the pre-diagnostic period

The Impact of Antiretroviral Therapy on Mortality in HIV Positive People during Tuberculosis Treatment: A Systematic Review and Meta-Analysis

Objective: To quantify the impact of antiretroviral therapy (ART) on mortality in HIV-positive people during tuberculosis (TB) treatment. Design: We conducted a systematic literature review and meta-analysis. Studies published from 1996 through February 15, 2013, were identified by searching electronic resources (Pubmed and Embase) and conference books, manual searches of references, and expert consultation. Pooled estimates for the outcome of interest were acquired using random effects meta-analysis. Subjects The study population included individuals receiving ART before or during TB treatment. Main Outcome Measures: Main outcome measures were: (i) TB-case fatality ratio (CFR), defined as the proportion of individuals dying during TB treatment and, if mortality in HIV-positive people not on ART was also reported, (ii) the relative risk of death during TB treatment by ART status. Results: Twenty-one studies were included in the systematic review. Random effects pooled meta-analysis estimated the CFR between 8% and 14% (pooled estimate 11%). Among HIV-positive TB cases, those receiving ART had a reduction in mortality during TB treatment of between 44% and 71% (RR = 0.42, 95%CI: 0.29–0.56). Conclusion: Starting ART before or during TB therapy reduces the risk of death during TB treatment by around three-fifths in clinical settings. National programmes should continue to expand coverage of ART for HIV positive in order to control the dual epidemic

Global burden of latent multidrug-resistant tuberculosis: trends and estimates based on mathematical modelling.

BACKGROUND: To end the global tuberculosis epidemic, latent tuberculosis infection needs to be addressed. All standard treatments for latent tuberculosis contain drugs to which multidrug-resistant (MDR) Mycobacterium tuberculosis is resistant. We aimed to estimate the global burden of multidrug-resistant latent tuberculosis infection to inform tuberculosis elimination policy. METHODS: By fitting a flexible statistical model to tuberculosis drug resistance surveillance and survey data collated by WHO, we estimated national trends in the proportion of new tuberculosis cases that were caused by MDR strains. We used these data as a proxy for the proportion of new infections caused by MDR M tuberculosis and multiplied trends in annual risk of infection from previous estimates of the burden of latent tuberculosis to generate trends in the annual risk of infection with MDR M tuberculosis. These estimates were used in a cohort model to estimate changes in the global and national prevalence of latent infection with MDR M tuberculosis. We also estimated recent infection levels (ie, in 2013 and 2014) and made predictions for the future burden of MDR tuberculosis in 2035 and 2050. FINDINGS: 19·1 million (95% uncertainty interval [UI] 16·4 million-21·7 million) people were latently infected with MDR tuberculosis in 2014-a global prevalence of 0·3% (95% UI 0·2-0·3). MDR strains accounted for 1·2% (95% UI 1·0-1·4) of the total latent tuberculosis burden overall, but for 2·9% (95% UI 2·6-3·1) of the burden among children younger than 15 years (risk ratio for those younger than 15 years vs those aged 15 years or older 2·65 [95% UI 2·11-3·25]). Recent latent infection with MDR M tuberculosis meant that 1·9 million (95% UI 1·7 million-2·3 million) people globally were at high risk of active MDR tuberculosis in 2015. INTERPRETATION: We estimate that three in every 1000 people globally carry latent MDR tuberculosis infection, and prevalence is around ten times higher among those younger than 15 years. If current trends continue, the proportion of latent tuberculosis caused by MDR strains will increase, which will pose serious challenges for management of latent tuberculosis-a cornerstone of tuberculosis elimination strategies. FUNDING: UK Medical Research Council, Bill & Melinda Gates Foundation, and European Research Council

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-specifi c intervention scenarios, which included screening for symptoms, active case fi nding, 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-specifi c tuberculosis epidemiology and health systems, are needed to reach the post-2015 End TB Strategy targets at country level

The potential impact of COVID-19-related disruption on tuberculosis burden.

Before the coronavirus disease 2019 (COVID-19) pandemic, over 4000 people were dying from tuberculosis (TB) every day. As with past emergencies, the impact of COVID-19 on TB outcomes is a serious cause for concern but is currently unknown. Health system overload, due to high numbers of COVID-19 cases, as well as interventions necessary to limit the transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), could result in severe reductions in health service availability and access for the detection and treatment of TB cases. However, physical distancing interventions could also limit Mycobacterium tuberculosis transmission outside of households, where most transmission occurs. This has not been adequately explored in concurrent work, and it is currently unclear whether social distancing could compensate for disruptions in TB services, and what the impact of these combined COVID-19 disruption effects on TB burden is likely to be

Small contribution of gold mines to the ongoing tuberculosis epidemic in South Africa: a modeling-based study.

BACKGROUND: Gold mines represent a potential hotspot for Mycobacterium tuberculosis (Mtb) transmission and may be exacerbating the tuberculosis (TB) epidemic in South Africa. However, the presence of multiple factors complicates estimation of the mining contribution to the TB burden in South Africa. METHODS: We developed two models of TB in South Africa, a static risk model and an individual-based model that accounts for longer-term trends. Both models account for four populations - mine workers, peri-mining residents, labor-sending residents, and other residents of South Africa - including the size and prevalence of latent TB infection, active TB, and HIV of each population and mixing between populations. We calibrated to mine- and country-level data and used the static model to estimate force of infection (FOI) and new infections attributable to local residents in each community compared to other residents. Using the individual-based model, we simulated a counterfactual scenario to estimate the fraction of overall TB incidence in South Africa attributable to recent transmission in mines. RESULTS: We estimated that the majority of FOI in each community is attributable to local residents: 93.9% (95% confidence interval 92.4-95.1%), 91.5% (91.4-91.5%), and 94.7% (94.7-94.7%) in gold mining, peri-mining, and labor-sending communities, respectively. Assuming a higher rate of Mtb transmission in mines, 4.1% (2.6-5.8%), 5.0% (4.5-5.5%), and 9.0% (8.8-9.1%) of new infections in South Africa are attributable to gold mine workers, peri-mining residents, and labor-sending residents, respectively. Therefore, mine workers with TB disease, who constitute ~ 2.5% of the prevalent TB cases in South Africa, contribute 1.62 (1.04-2.30) times as many new infections as TB cases in South Africa on average. By modeling TB on a longer time scale, we estimate 63.0% (58.5-67.7%) of incident TB disease in gold mining communities to be attributable to recent transmission, of which 92.5% (92.1-92.9%) is attributable to local transmission. CONCLUSIONS: Gold mine workers are estimated to contribute a disproportionately large number of Mtb infections in South Africa on a per-capita basis. However, mine workers contribute only a small fraction of overall Mtb infections in South Africa. Our results suggest that curtailing transmission in mines may have limited impact at the country level, despite potentially significant impact at the mining level

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

Ongoing challenges to understanding multidrug- and rifampicin-resistant tuberculosis in children versus adults.

Previous analyses suggest that children with tuberculosis (TB) are no more or no less likely to have multidrug (MDR)- or rifampicin-resistant (RR)-TB than adults. However, the availability of new data, particularly for high MDR/RR-TB burden countries, suggest updates of country-specific estimates are warranted.We used data from population-representative surveys and surveillance collected between 2000 and 2018 to compare the odds ratio of MDR/RR-TB among children (aged <15 years) with TB, compared to the odds of MDR/RR-TB among adults (aged ≥15 years) with TB.In most settings (45 out of 55 countries), and globally as a whole, there is no evidence that age is associated with odds of MDR/RR-TB. However, in some settings, such as former Soviet Union countries in general, and Georgia, Kazakhstan, Lithuania, Tajikistan and Uzbekistan in particular, as well as Peru, MDR/RR-TB is positively associated with age ≥15 years. Meanwhile, in Western Europe in general, and the United Kingdom, Poland, Finland and Luxembourg in particular, MDR/RR-TB is positively associated with age <15 years. 16 countries had sufficient data to compare over time between 2000-2011 and 2012-2018, with evidence for decreases in the odds ratio in children compared to adults in Germany, Kazakhstan and the United States of America.Our results support findings that in most settings a child with TB is as likely as an adult with TB to have MDR/RR-TB. However, setting-specific heterogeneity requires further investigation. Furthermore, the odds ratio for MDR/RR-TB in children compared to adults is generally either stable or decreasing. There are important gaps in detection, recording and reporting of drug resistance among paediatric TB cases, limiting our understanding of transmission risks and measures needed to combat the global TB epidemic

Contribution of remote M. tuberculosis infection to tuberculosis disease: A 30-year population study

Background: The importance of remote infection with M.tuberculosis as a cause of tuberculosis disease (TB) is unclear, with limited evidence of impact on TB rates beyond 10 years. Our objective was to assess rates of tuberculosis over 30 years by M.tuberculosis infection status at baseline in Karonga District, Northern Malawi. Materials and methods: Population-based surveys of tuberculin skin testing (TST) from the 1980s were linked with follow-up and TB surveillance in Karonga district. We compared rates of microbiologically-confirmed TB by baseline TST induration &lt;5mm (no evidence of M.tuberculosis infection) and those with baseline TST &gt;17mm (evidence of M.tuberculosis infection), using hazard ratios by time since baseline and attributable risk percent. The attributable risk percent was calculated to estimate the proportion of TB in those infected that can be attributed to that prior infection. We analysed whole genome sequences of M.tuberculosis strains to identify recent transmission. Results: Over 412,959 person-years, 208 incident TB episodes were recorded. Compared to the small induration group, rates of TB were much higher in the first two years in the large induration group, and remained higher to 20 years: age, sex and area-adjusted hazard ratios (HR) 2–9 years post-TST 4.27 (95%CI 2.56–7.11); 10–19 years after TST 2.15 (1.10–4.21); ≥20 years post-TST 1.88 (0.76–4.65). The attributable risk percent of remote infection was 76.6% (60.9–85.9) 2–9 years post-TST, and 53.5% (9.1–76.2) 10–19 years post-TST. Individuals with large TST indurations had higher rates of unique-strain TB (HR adjusted for age, sex and area = HR 6.56 (95% CI 1.96–22.99)), suggesting disease following remote infection, but not of linked-strain TB (recent transmission). Conclusions: M.tuberculosis infection can increase the risk of TB far beyond 10 years, accounting for a substantial proportion of TB occurring among those remotely infected

The contribution of asymptomatic SARS-CoV-2 infections to transmission on the Diamond Princess cruise ship.

A key unknown for SARS-CoV-2 is how asymptomatic infections contribute to transmission. We used a transmission model with asymptomatic and presymptomatic states, calibrated to data on disease onset and test frequency from the Diamond Princess cruise ship outbreak, to quantify the contribution of asymptomatic infections to transmission. The model estimated that 74% (70-78%, 95% posterior interval) of infections proceeded asymptomatically. Despite intense testing, 53% (51-56%) of infections remained undetected, most of them asymptomatic. Asymptomatic individuals were the source for 69% (20-85%) of all infections. The data did not allow identification of the infectiousness of asymptomatic infections, however low ranges (0-25%) required a net reproduction number for individuals progressing through presymptomatic and symptomatic stages of at least 15. Asymptomatic SARS-CoV-2 infections may contribute substantially to transmission. Control measures, and models projecting their potential impact, need to look beyond the symptomatic cases if they are to understand and address ongoing transmission