Contribution of Reinfection to Annual Rate of Tuberculosis Infection (ARI) and Incidence of Tuberculosis Disease

Abstract

Background: Modeling studies have concluded that 60-80% of tuberculosis (TB) infections result from reinfection of previously infected persons. The annual rate of infection (ARI), a standard measure of the risk of TB infection in a community, may not accurately reflect the true risk of infection among previously infected persons. We constructed a model of infection and reinfection with Mycobacterium tuberculosis to explore the predictive accuracy of ARI and its effect on disease incidence.

Methods: We created a deterministic simulation of the progression from TB infection to disease and simulated the prevalence of TB infection at the beginning and end of a theoretical year of infection. We considered 10 disease prevalence scenarios ranging from 100/100 000 to 1000/100 000 in simulations where TB exposure probability was homogeneous across the whole simulated population or heterogeneously stratified into high-risk and low-risk groups. ARI values, rates of progression from infection to disease, and the effect of multiple reinfections were obtained from published studies.

Results: With homogeneous exposure risk, observed ARI values produced expected numbers of infections. However, when heterogeneous risk was introduced, observed ARI was seen to underestimate true ARI by 25-58%. Of the cases of TB disease that occurred, 36% were among previously infected persons when prevalence was 100/100 000, increasing to 79% of cases when prevalence was 1000/100 000.

Conclusions: Measured ARI underestimates true ARI as a result of heterogeneous population mixing. The true force of infection in a community may be greater than previously appreciated. Hyperendemic communities likely contribute disproportionally to the global TB disease burden.

Keywords: annual rate of tuberculosis infection; tuberculosis disease; tuberculosis infection; tuberculosis prevalence; tuberculosis reinfection.