Background: The residential segregation of black populations, often in areas of high-economic disadvantage and low social status, may play a crucial role in the observed racial inequities in sexually transmitted disease rates.
Methods: An ecological analysis of 2005 to 2009 average gonorrhea rates was performed across 277 US metropolitan statistical areas (MSAs). The black isolation index and Gini index of income inequality were used as proxy measures for racial and economic residential segregation respectively, derived from 2005 to 2009 US Census estimates. We used logistic regression modeling to produce estimates of odds ratios (ORs) and 95% confidence intervals (CIs) for the association between the segregation indices, both independently and in combination, on gonorrhea rates in MSAs. Effect measure modification was assessed by calculating the relative excess risk due to interaction between the 2 indices.
Results: Compared with MSAs with low levels of racial segregation, MSAs with high levels of racial segregation had increased odds of high gonorrhea rates (adjusted OR, 3.37; 95% CI, 1.23–9.21). Similarly, higher levels of income inequality predicted higher gonorrhea rates, although this association did not persist after adjustment for potential confounders (adjusted OR, 1.54; 95% CI, 0.74–3.24). In combined models, the influence of racial residential segregation on gonorrhea rates was stronger than that of income inequality–based segregation; there was no evidence of additivity or a multiplicative interaction.
Conclusions: Residential segregation by race or income equality may be a key component in the perpetuation of high rates of gonorrhea and other sexually transmitted diseases among black populations in the United States.
A study of residential segregation among US metropolitan statistical areas found that both the black isolation index and the Gini index of income inequality were associated with higher rates of gonorrhea.
From the *Department of Epidemiology and Community Health, Virginia Commonwealth University, Richmond, VA; Offices of †Epidemiology, ‡Child and Maternal Health, Virginia Department of Health, Richmond, VA; §Department of Social and Behavioral Health, Virginia Commonwealth University, Richmond, VA; and ¶Department of Quantitative Health Sciences, University of Massachusetts Medical School, Worcester, MA.
Conflict of interest: The authors have no conflict of interest to disclose.
Correspondence: River A. Pugsley, PhD, Office of Epidemiology, Virginia Department of Health, 109 Governor St, PO Box 2448, Room 326, Richmond, VA 23218-2448. E-mail: firstname.lastname@example.org
Received for publication August 23, 2012, and accepted December 28, 2012.