GENOMIC CLUSTERS OF INVASIVE PNEUMOCOCCAL DISEASE (IPD) ISOLATES, USA, 2015-2017 (ID 833)

Session Name
Population Sciences - Epidemics, Outbreaks and Special Settings
Presenter
  • Yuan Li, United States of America
Authors
  • Yuan Li, United States of America
  • Sopio Chochua, United States of America
  • Benjamin J. Metcalf, United States of America
  • Jasmine Varghese, United States of America
  • Zhongya Li, United States of America
  • Theresa Tran, United States of America
  • Hollis Walker, United States of America
  • Lesley McGee, United States of America
  • Tamara Pilishvili, United States of America
  • Bernard Beall, United States of America

Abstract

Background

Outbreaks of pneumococcal infections have been reported in closed settings and are often caused by clusters of genomically highly related isolates (genomic clusters), suggesting close transmission connections.

Methods

We used whole-genome sequencing (WGS) to characterize all IPD isolates identified through the Active Bacterial Core surveillance (ABCs) from 2015 to 2017. We identified genomic clusters by performing hierarchical cluster analysis of pair-wise genomic distances and applying a cut-off value of 3-base difference per 1.8Mb shared genome.

Results

WGS characterized 8029 isolates representing 87% of all IPD cases. The cluster analysis identified 379 genomic clusters accounting for 847 (11%) of the isolates. Higher proportions of clustered isolates (isolates belonging to any clusters) were found in serotypes 12F (36%), 4 (26%), and 7F (25%), while lower proportions were found in serotypes 35F, 7C, 15A, 23B (each <1.5%), and penicillin non-susceptible isolates (4%). Clustered isolates were significantly associated with patients who were homeless (OR=3.3), who were injecting drugs (OR=2.1), and who were 25 to 50 years of age (OR=1.5; all p-values < 0.001).

Conclusions

IPD genomic clusters were associated with specific patient and strain features in the ABCs population. Understanding the dynamics and demographics of vaccine-serotype clusters could help identify new target groups to inform vaccine policy.

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