Supplementary material from "Multiple exposures, reinfection and risk of progression to active tuberculosis"

• Main Authors: Ackley, Sarah F., Lee, Robyn S., Worden, Lee, Zwick, Erin, Porco, Travis C., Behr, Marcel A., Pepperell, Caitlin S.
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: Figshare 2019
• Subjects: Health Care; Diseases; 111706 Epidemiology; FOS Health sciences; inuit
• Online Access: https://dx.doi.org/10.6084/m9.figshare.c.4430093; https://rs.figshare.com/collections/Supplementary_material_from_Multiple_exposures_reinfection_and_risk_of_progression_to_active_tuberculosis_/4430093

A recent study reported on a tuberculosis (TB) outbreak in a largely Inuit village. Among newly infected individuals, exposure to additional active cases was associated with an increasing probability of developing active disease within a year. Using binomial risk models, we evaluated two potential mechanisms by which multiple infections during the first year following initial infection could account for increasing disease risk with increasing exposures. In the reinfection model , each infectious contact confers an independent risk of an infection, and infections contribute independently to active disease. In the threshold model , disease risk follows a sigmoidal function with small numbers of infectious contacts conferring a low risk of active disease and large numbers of contacts conferring a high risk. To determine the dynamic impact of reinfection during the early phase of infection, we performed simulations from a modified Reed–Frost model of TB dynamics following spread from an initial number of cases. We parametrized this model with the maximum-likelihood estimates from the reinfection and threshold models in addition to the observed distribution of exposures among new infections. We find that both models can plausibly account for the observed increase in disease risk with increasing infectious contacts, but the threshold model confers a better fit than a nested model without a threshold ( p = 0.04). Our simulations indicate that multiple exposures to infectious individuals during this critical time period can lead to dramatic increases in outbreak size. In order to decrease TB burden in high-prevalence settings, it may be necessary to implement measures aimed at preventing repeated exposures, in addition to preventing primary infection.