Geotemporal Analysis of Neisseria meningitidis Clones in the United States: 2000-2005

• Authors: Ann E. Wiringa, University of Pittsburgh; Kathleen A. Shutt, University of Pittsburgh; Jane W. Marsh, University of Pittsburgh; Amanda C. Cohn, Centers for Disease Control and Prevention; Nancy E. Messonnier, Centers for Disease Control and Prevention; Shelley M. Zansky, New York State Department of Health; Susan Petit, Connecticut Department of Public Health; Monica Farley, Emory University; Ken Gershman, Colorado Department of Public Health and Environment; Ruth Lynfield, Minnesota Department of Health; Arthur Reingold, University of California Berkeley; William Schaffner, Vanderbilt University; Jamie Thompson, Oregon Public Health Division; Shawn T. Brown, Carnegie Mellon University; Bruce Y. Lee, University of Pittsburgh; Lee H. Harrison, University of Pittsburgh
• Journal Title: PLOS ONE
• Volume: Volume 8, Number 12
• Publisher: PUBLIC LIBRARY SCIENCE | 2013-12-12, Pages e82048-e82048
• Type of Work: Article | Final Publisher PDF
• Publisher DOI: http://doi.org/10.1371/journal.pone.0082048
• Subjects: Health Sciences, Immunology; Biology, Virology
• Keywords: Science & Technology; Multidisciplinary Sciences; Science & Technology - Other Topics; INVASIVE MENINGOCOCCAL DISEASE; OUTBREAK DETECTION; SURVEILLANCE; INFECTION; RISK

Abstract

Background: The detection of meningococcal outbreaks relies on serogrouping and epidemiologic definitions. Advances in molecular epidemiology have improved the ability to distinguish unique Neisseria meningitidis strains, enabling the classification of isolates into clones. Around 98% of meningococcal cases in the United States are believed to be sporadic. Methods: Meningococcal isolates from 9 Active Bacterial Core surveillance sites throughout the United States from 2000 through 2005 were classified according to serogroup, multilocus sequence typing, and outer membrane protein (porA, porB, and fetA ) genotyping. Clones were defined as isolates that were indistinguishable according to this characterization. Case data were aggregated to the census tract level and all non-singleton clones were assessed for non-random spatial and temporal clustering using retrospective space-time analyses with a discrete Poisson probability model. Results: Among 1,062 geocoded cases with available isolates, 438 unique clones were identified, 78 of which had ≥2 isolates. 702 cases were attributable to non-singleton clones, accounting for 66.0% of all geocoded cases. 32 statistically significant clusters comprised of 107 cases (10.1% of all geocoded cases) were identified. Clusters had the following attributes: included 2 to 11 cases; 1 day to 33 months duration; radius of 0 to 61.7 km; and attack rate of 0.7 to 57.8 cases per 100,000 population. Serogroups represented among the clusters were: B (n = 12 clusters, 45 cases), C (n = 11 clusters, 27 cases), and Y (n = 9 clusters, 35 cases); 20 clusters (62.5%) were caused by serogroups represented in meningococcal vaccines that are commercially available in the United States. Conclusions: Around 10% of meningococcal disease cases in the U.S. could be assigned to a geotemporal cluster. Molecular characterization of isolates, combined with geotemporal analysis, is a useful tool for understanding the spread of virulent meningococcal clones and patterns of transmission in populations.