Designing and Evaluating Interventions to Halt the Transmission of Tuberculosis

Abstract

To reduce the incidence of tuberculosis, it is insufficient to simply understand the dynamics of tuberculosis transmission. Rather, we must design and rigorously evaluate interventions to halt transmission, prioritizing those interventions most likely to achieve population-level impact. Synergy in reducing tuberculosis transmission may be attainable by combining interventions that shrink the reservoir of latent Mycobacterium tuberculosis infection (preventive therapy), shorten the time between disease onset and treatment initiation (case finding and diagnosis), and prevent transmission in key settings, such as the built environment (infection control). In evaluating efficacy and estimating population-level impact, cluster-randomized trials and mechanistic models play particularly prominent roles. Historical and contemporary evidence suggests that effective public health interventions can halt tuberculosis transmission, but an evidence-based approach based on knowledge of local epidemiology is necessary for success. We provide a roadmap for designing, evaluating, and modeling interventions to interrupt the process of transmission that fuels a diverse array of tuberculosis epidemics worldwide.

RISK6, a 6-gene transcriptomic signature of TB disease risk, diagnosis and treatment response

“…This finding highlights the importance of including clinical and epidemiological factors in studies of diagnostic biosignatures. Community-based case finding studies and prevalence surveys have shown that a substantial proportion of microbiologically-confirmed TB cases are asymptomatic [30][31][32] , highlighting the need for TB case finding in asymptomatic communities. Definitive diagnoses of ORD patients were not determined in the ScreenTB and AE-TBC study, but larger future studies with careful diagnoses of patients with ORD would be essential to evaluate the specificity of RISK6, and define respiratory diseases most difficult to differentiate from TB.…”

RISK6, a universal 6-gene transcriptomic signature of TB disease risk, diagnosis and treatment response

“…We acknowledge that the effect of HIV was not assessed in all of the validation cohorts and more such analyses are necessary to definitively establish the effects of underlying HIV infection on RISK6 performance. Regardless, other published blood-based transcriptomic TB signatures showed reduced diagnostic performance in HIV-infected compared to uninfected persons 6,[26][27][28][29] . Since most transcriptomic TB signatures detect the elevation of ISG expression during TB, this effect of HIV is not surprising given that strong Type I IFN responses constitute the typical anti-viral response 29 .…”

Modeling missing cases and transmission links in networks of extensively drug-resistant tuberculosis in KwaZulu-Natal, South Africa

“…To compare the empirical network with the simulated and sampled networks, we compared locations of the quantiles (10%, 25%, 50%, 100%) of the degree distribution. 8 Additional Sensitivity Analyses.…”