P1-S5.43 Disparities in sexually transmitted diseases across race-income "Counties in the USA: a race-specific morbidity analysis". (10th July 2011) - Owusu-Edusei, K; Chesson, H; Leichliter, J; Kent, C; Aral, S
Abstract : Background: We examined the association between race-specific incidence rates for three major bacterial STDs and race-specific household income at the county level for all counties in the 48 contiguous states in the USA. Methods: Race-specific county-level median household incomes were obtained from the 2000 decennial census. We used the overall US median household income (ie, $41 994, in 2000 dollars) as the cut-point to categorise counties into six race-income county groups. "Race-income" county groups were defined by black household income and white household income at the county level (see Abstract P1-S5.43 table 1 ). County-level race-specific morbidity for chlamydia, gonorrhoea and primary and secondary (P&S) syphilis were obtained from the National Electronic Telecommunications System for Surveillance (NETSS) for 1999–2001. For each of the six county groups, we calculated race-specific STD rates (new cases per 100 000 residents) using the total number of cases and population size for all three years. In a supplemental analysis, we used a spatial regression technique to examine the association in more detail, controlling for county-level socio-demographic factors. Results: STD rates for Blacks in each category were at least 4 times higher than for whites. For chlamydia, county group 1 had the highest rate followed by county group 2. However, for gonorrhoea and P&S syphilis, county group 2 had the highest rate for both Blacks and Whites, followed by county group 1 (see Abstract P1-S5.43 table 1 ). In the regression analysis, gonorrhoea rates were significantly higher (+12%, p<0.05) in counties in which black household income is below the national average and white household income is above the national average than in counties in which black and white household incomes are both below the national average. Conclusions: Although other studies have demonstrated the association between income, race and STD rates, our race-income categorisation provides several new insights. Most importantly, the grouping of counties that we developed allows us to examine not only the association between STD incidence and income, but also the association between STD incidence and disparities in income. Racial disparities in income were associated with racial disparities in STDs. Specifically, when there is disparity in median household incomes for Blacks and Whites, rates of gonorrhoea and P&S syphilis for each race are higher than when there is more equity in the median household income.