Molecular surveillance for large outbreaks of tuberculosis in the United States, 2014-2018

Abstract

Objective: This study describes characteristics of large tuberculosis (TB) outbreaks in the United States detected using novel molecular surveillance methods during 2014-2016 and followed for 2 years through 2018.

Methods: We developed 4 genotype-based detection algorithms to identify large TB outbreaks of ≥10 cases related by recent transmission during a 3-year period. We used whole-genome sequencing and epidemiologic data to assess evidence of recent transmission among cases.

Results: There were 24 large outbreaks involving 518 cases; patients were primarily U.S.-born (85.1%) racial/ethnic minorities (84.1%). Compared with all other TB patients, patients associated with large outbreaks were more likely to report substance use, homelessness, and having been diagnosed while incarcerated. Most large outbreaks primarily occurred within residences among families and nonfamilial social contacts. A source case with a prolonged infectious period and difficulties in eliciting contacts were commonly reported contributors to transmission.

Conclusion: Large outbreak surveillance can inform targeted interventions to decrease outbreak-associated TB morbidity.

Keywords: Outbreaks; Surveillance; Tuberculosis; Whole-genome sequencing.

Fig. 1.

Possible large outbreaks of tuberculosis (TB) by classification status and method of first detection. Large outbreaks that are detected through manual identification (TB program report and routine cluster review by CDC staff) are by default always counted large outbreaks at detection. One of the five outbreaks detected through manual identification was also identified by one of the detection algorithms after the outbreak had been identified manually.