Refining Historical Limits Method to Improve Disease Cluster Detection, New York City, New York, USA

Emerging Infectious Diseases. 2015;21(2):265-272 DOI 10.3201/eid2102.140098

 

Journal Homepage

Journal Title: Emerging Infectious Diseases

ISSN: 1080-6040 (Print); 1080-6059 (Online)

Publisher: Centers for Disease Control and Prevention

LCC Subject Category: Medicine: Internal medicine: Infectious and parasitic diseases

Country of publisher: United States

Language of fulltext: English

Full-text formats available: PDF, HTML, XML

 

AUTHORS


Alison Levin-Rector

Elisha L. Wilson

Annie D. Fine

Sharon K. Greene

EDITORIAL INFORMATION

Peer review

Editorial Board

Instructions for authors

Time From Submission to Publication: 8 weeks

 

Abstract | Full Text

Since the early 2000s, the Bureau of Communicable Disease of the New York City Department of Health and Mental Hygiene has analyzed reportable infectious disease data weekly by using the historical limits method to detect unusual clusters that could represent outbreaks. This method typically produced too many signals for each to be investigated with available resources while possibly failing to signal during true disease outbreaks. We made method refinements that improved the consistency of case inclusion criteria and accounted for data lags and trends and aberrations in historical data. During a 12-week period in 2013, we prospectively assessed these refinements using actual surveillance data. The refined method yielded 74 signals, a 45% decrease from what the original method would have produced. Fewer and less biased signals included a true citywide increase in legionellosis and a localized campylobacteriosis cluster subsequently linked to live-poultry markets. Future evaluations using simulated data could complement this descriptive assessment.