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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.

Sourya Shrestha,K. Winglee,A. Hill,T. Shaw,Jonathan P Smith,J. Kammerer,B. Silk,S. Marks,D. Dowdy

Published 2022 in Clinical Infectious Diseases

ABSTRACT

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 CDC during 2012-2016 in the United States and separately in California, Florida, New York, and Texas. Using these models, we estimated the mean number of secondary cases generated per infectious case (R0) and individual-level heterogeneity in R0 at state and national levels. We also assessed how different definitions of clustering and variation in case ascertainment affected these estimates. RESULTS In clusters of genotypically linked TB cases occurring within a state over a 5-year period (reference scenario), the estimated R0 was 0.29 (95% CI: 0.28-0.31) in the United States. Transmission was highly heterogeneous: 0.24% of simulated cases with individual R0>10 generated 19% of all recent secondary transmissions. R0 estimate was 0.16 (0.15-0.17) when a cluster was defined as cases occurring within the same county over a 3-year period. Transmission varied across states: estimated R0s were 0.34 (0.3-0.4) in California, 0.28 (0.24-0.36) in Florida, 0.19 (0.15-0.27) in New York, and 0.38 (0.33-0.46) 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 in the United States and worldwide.

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