E O Laumann1, Y Youm. 1. The Department of Sociology, University of Chicago, Illinois 60637, USA.
Abstract
BACKGROUND: Many studies have observed that African Americans have comparatively high rates of selected STDs, often 10 to 20 times higher than whites and other racial/ethnic groups, but without convincing explanation. GOAL: This study attempts to solve this puzzle using data from a nationally representative probability sample and a network approach. STUDY DESIGN: The National Health and Social Life Survey (NHSLS) is a nationally representative probability sample of 1,511 men and 1,921 women in the United States. Logistic regression analysis of these data permit a multivariate analysis of the individual risk factors associated with STDs. Using loglinear analysis and a simulation, we also identify the effects of sexual network patterns within and between racial/ethnic groups. RESULTS: Logistic regression analysis of the NHSLS revealed, even after controlling for all the appropriate individual-level risk factors, that African Americans are almost five times more likely to be infected by bacterial diseases than the other racial/ethnic groups. CONCLUSIONS: African Americans' higher infection rate for bacterial diseases can be explained by the patterns of sexual networks within and between different racial/ethnic groups. First, infections are more widespread in the African American population at large because partner choice is more highly dissortative--meaning that "peripheral" African Americans (who have had only one partner in the past year) are five times more likely to choose "core' African Americans (who have had four or more partners in the past year) than "peripheral" whites are to choose "core" whites. Secondly, sexually transmitted infections stay within the African American population because their partner choices are more segregated (assortative mating) than other groups. The likelihood of African Americans having a sexually transmitted infection is 1.3 times greater than it is for whites because of this factor alone.
BACKGROUND: Many studies have observed that African Americans have comparatively high rates of selected STDs, often 10 to 20 times higher than whites and other racial/ethnic groups, but without convincing explanation. GOAL: This study attempts to solve this puzzle using data from a nationally representative probability sample and a network approach. STUDY DESIGN: The National Health and Social Life Survey (NHSLS) is a nationally representative probability sample of 1,511 men and 1,921 women in the United States. Logistic regression analysis of these data permit a multivariate analysis of the individual risk factors associated with STDs. Using loglinear analysis and a simulation, we also identify the effects of sexual network patterns within and between racial/ethnic groups. RESULTS: Logistic regression analysis of the NHSLS revealed, even after controlling for all the appropriate individual-level risk factors, that African Americans are almost five times more likely to be infected by bacterial diseases than the other racial/ethnic groups. CONCLUSIONS: African Americans' higher infection rate for bacterial diseases can be explained by the patterns of sexual networks within and between different racial/ethnic groups. First, infections are more widespread in the African American population at large because partner choice is more highly dissortative--meaning that "peripheral" African Americans (who have had only one partner in the past year) are five times more likely to choose "core' African Americans (who have had four or more partners in the past year) than "peripheral" whites are to choose "core" whites. Secondly, sexually transmitted infections stay within the African American population because their partner choices are more segregated (assortative mating) than other groups. The likelihood of African Americans having a sexually transmitted infection is 1.3 times greater than it is for whites because of this factor alone.
Authors: Jacky M Jennings; Ralph Taylor; Vince G Iannacchione; Susan M Rogers; Shang-En Chung; Steven Huettner; Jonathan M Ellen Journal: Ann Epidemiol Date: 2010-07 Impact factor: 3.797
Authors: George Ayala; Trista Bingham; Junyeop Kim; Darrell P Wheeler; Gregorio A Millett Journal: Am J Public Health Date: 2012-03-08 Impact factor: 9.308