Model-based Analysis of Tuberculosis Genotype Clusters in the United States Reveals High Degree of Heterogeneity in Transmission and State-level Differences Across California, Florida, New York, and Texas

Background. Reductions in tuberculosis (TB) transmission have been instrumental in lowering TB incidence in the United States. Sustaining and augmenting these reductions are key public health priorities. Methods. We fit mechanistic transmission models to distributions of genotype clusters of TB cases reported to the Centers for Disease Control and Prevention during 2012-2016 in the United States and separately in California, Florida, New York, and Texas. We estimated the mean number of secondary cases generated per infectious case (R-0) and individual-level heterogeneity in R-0 at state and national levels and assessed how different definitions of clustering affected these estimates. Results. In clusters of genotypically linked TB cases that occurred within a state over a 5-year period (reference scenario), the estimated R-0 was 0.29 (95% confidence interval [CI], .28-.31) in the United States. Transmission was highly heterogeneous; 0.24% of simulated cases with individual R-0 >10 generated 19% of all recent secondary transmissions. R-0 estimate was 0.16 (95% CI, .15-.17) when a cluster was defined as cases occurring within the same county over a 3-year period. Transmission varied across states: estimated R(0)s were 0.34 (95% CI, .3-.4) in California, 0.28 (95% CI, .24-.36) in Florida, 0.19 (95% CI, .15-.27) in New York, and 0.38 (95% CI, .33-.46) in Texas. Conclusions. TB transmission in the United States is characterized by pronounced heterogeneity at the individual and state levels. Improving detection of transmission clusters through incorporation of whole-genome sequencing and identifying the drivers of this heterogeneity will be essential to reducing TB transmission.

Automated summary

This study uses mechanistic transmission models to analyze the transmission of tuberculosis in the United States and finds significant heterogeneity at both the individual and state levels. Improving detection of transmission clusters and identifying the drivers of heterogeneity will be crucial for reducing TB transmission.