Greene Sharon

Nontyphoidal Salmonella, consisting of >2,500 distinct serotypes, is the leading bacterial agent of foodborne illness in the U.S., causing an estimated 1 million infections per year. In NYC, interviews of all case-patients (N≈1,100 annually) are attempted to support outbreak investigation and control. Salmonella clusters in NYC are typically identified either by notification from PulseNet, CDC, or other health departments or by a weekly analysis using the historical limits method. More systematic and timely cluster detection could inform resource prioritization and improve the effectiveness of public health interventions. We initiated daily analyses in May 2015 to detect spatio-temporal clusters by serotype among cases since February 23. In July 2015, an analysis was added to detect purely temporal clusters among cases since May 1.

Objective

To prospectively identify serotype-specific clusters of salmonellosis in New York City (NYC).

Most public health surveillance systems in the United States do not capture individual-level measures of socioeconomic position. Without this information, socioeconomic disparities in health outcomes can be hidden. However, US Census data can be used to describe neighborhood-level socioeconomic conditions like poverty and crowding. Place matters. Neighborhood affects health independently of personal characteristics. Thus, important trends may be elucidated by linking geocoded public health surveillance data to area-based measures of socioeconomic position, such as the percentage of residents with incomes below the federal poverty level.

Objective

The panel will describe applying the methods of Harvard’s Public Health Disparities Geocoding Project to a diverse collection of infectious disease surveillance data from 14 US states and New York City. This session will demonstrate the feasibility and utility of using US Census data to reveal sub-populations vulnerable to infectious diseases.

The Bureau of Communicable Disease (BCD) at the NYC Department of Health and Mental Hygiene performs daily automated analyses using SaTScan to detect spatio-temporal clusters for 37 reportable diseases. Initially, we analyzed one address per patient, prioritizing home address if available. On September 25, 2015, a BCD investigator noticed two legionellosis cases with similar work addresses. A third case was identified in a nearby residential facility, and an investigation was initiated to identify a common exposure source. Four days later, after additional cases living nearby were reported, the SaTScan analysis detected a corresponding cluster.  In response to this signaling delay, we implemented a multiple address (MA) analysis to improve upon single address (SA) analyses by using all location data available on possible exposure sites.

Objective

To improve timeliness and sensitivity of legionellosis cluster detection in New York City (NYC) by using all addresses available for each patient in one analysis.