Online Program

Spatial analysis of gonorrhea surveillance data in Richmond, Virginia

Tuesday, November 5, 2013 : 9:32 a.m. - 9:50 a.m.

Meaghan Munn, MPH, Department of Epidemiology and Community Health, Virginia Commonwealth University, Richmond, VA
Steven A. Cohen, DrPH, MPH, Department of Family Medicine and Population Health, Virginia Commonwealth University School of Medicine, Richmond, VA
River Pugsley, PhD, MPH, Division of Disease Prevention, Virginia Department of Health, Richmond, VA
Background: Gonorrhea is a sexually transmitted disease (STD) with historically high morbidity rates in Richmond, Virginia. To monitor gonorrhea incidence in the Richmond metropolitan area, the Virginia Department of Health (VDH) participates in the CDC-sponsored STD Surveillance Network (SSuN).

Methods: Our study uses SSuN surveillance data from the VDH on gonorrhea cases diagnosed in the Richmond area from July 2010 through July 2012. Using GIS software, this dataset was analyzed using Getis-Ord Gi* spatial statistics to determine statistically significant clusters of gonorrhea cases. Characteristics of patients residing within the clusters were compared to those residing outside the clusters using Pearson's chi-square test. Logistic regression was conducted to identify the best predictive model for patients living in a high-morbidity cluster.

Results: Two distinct high-morbidity clusters and one distinct low-morbidity cluster were identified within the city. Census block groups within the high-morbidity and low-morbidity clusters were significantly different from one another and had significantly different characteristics than block groups outside these clusters. Furthermore, compared to patients residing outside the clusters, patients living in the clusters exhibited significantly different individual characteristics and behaviors. Recent incarceration history (aOR=2.14; 95%CI: 1.01-2.22), high school education or less (aOR=1.99; 95%CI: 1.31-3.03), and being female (aOR=1.49; 95%CI: 1.01-2.22) were predictive of patients living in a high-morbidity cluster.

Conclusion: These findings can be utilized in the development of intervention programs targeted at neighborhoods within high-morbidity clusters to reduce gonorrhea morbidity in the city of Richmond.

Learning Areas:

Public health or related research

Learning Objectives:
Analyze the spatial distribution of gonorrhea surveillance data in Richmond, Virginia. Identify high-morbidity clusters within the city.

Keyword(s): Epidemiology, Geographic Information Systems

Presenting author's disclosure statement:

Qualified on the content I am responsible for because: I am a second-year Master of Public Health student who has worked on numerous public health projects and has an extensive background in epidemiology, statistics, and geographic information systems.
Any relevant financial relationships? No

I agree to comply with the American Public Health Association Conflict of Interest and Commercial Support Guidelines, and to disclose to the participants any off-label or experimental uses of a commercial product or service discussed in my presentation.