Background:
Gonorrhea incidence rates in the United States have been declining since 1976. Magnitude of this decline has varied geographically.

Objective:
To explore the associations of economic and socio-demographic factors with the reported gonorrhea incidence rates at the postal zip code level, and generate hypotheses regarding factors that may influence the incidence rates.

Method:
Gonorrhea incidence rates (per 10,000 population) were calculated by postal zip code from cases reported to the CDC by one southeastern state (State X) during 2005. Socio-demographic and economic data for 2005 were obtained from the Community Sourcebook-America, (ESRI Inc.) for all zip codes in State X. Pearson's correlation coefficients were calculated to assess associations between 36 socio-demographic and economic variables and gonorrhea incidence rates. A multiple regression model with gonorrhea incidence rates as the dependent variable, and economic and socio-demographic factors as independent variables was built to find variables associated with the gonorrhea incidence rates.

Result:
Twenty-five of the 36 socio-demographic and economic variables were significantly (p < 0.05) associated with gonorrhea the incidence rates. In the multivariate regression model six factors accounted for 54% of the variation. Percentage of black population, percentage of households with income less than $25,000 per year and areas with residential properties valued less than $90,000 were positively associated with gonorrhea incidence rates; while population growth, potential index for health insurance and median housing value were negatively associated.

Conclusion:
Socio-demographic and economic factors are highly correlated with gonorrhea incidence rates at the micro (zip code) level in State X. This method can identify community level factors to consider when formulating intervention strategies and prevention program indicators at the local level.

Implications:
This presentation describes a method to analyze STD rates using community level data and illustrates how socio-demographic and economic factors may be influencing changes in gonorrhea incidence rates.