The Distribution of Sex Partners in the United States By Sexual Identity, 2002 and 2006-2010

Background: Heterogeneity in sexual behavior is a key factor in sustaining STD transmission. Among those with opposite-sex partners, sexual behaviors are highly concentrated within sub-populations that are often at the highest risk for STDs. However, little is known about the distribution of behaviors by sexual identity.  

Methods: We used combined data from the 2002 to 2006-2010 National Survey of Family Growth. We examined sexual identity by demographics (age, race/ethnicity, income-poverty ratio, metropolitan residence, and education). We also analyzed the number of recent opposite-sex and same-sex partners (past 12 months) by sexual identity (heterosexual or /straight; homosexual, gay, or lesbian; bisexual) for females and males, separately. Data analyses included means tests and measures of distribution (e.g., Gini coefficients, which range from 0 [equality] to 1 [inequality]); 95% confidence intervals were used to examine significant differences.

Results: Sexual identity significantly varied by every demographic analyzed for females and males, except age for males. Among females, bisexual women reported a higher mean number of recent opposite-sex partners (1.58; 95%CI: 1.45, 1.71) than heterosexual women (1.07; 95%CI: 1.05, 1.10) but fewer same-sex partners (0.66; 95%CI: 0.56, 0.76) than homosexual women (1.19; 95%CI: 1.00, 1.38). Among males, homosexual men reported a higher mean number of recent partners (same-sex: 1.99; 95%CI: 1.65, 2.32) than heterosexual men (opposite-sex: 1.26; 95%CI: 1.23, 1.29) and bisexual men (opposite-sex: 0.99; 95%CI: 0.83, 1.14; same-sex: 0.66; 95%CI: 0.46, 0.87). Partnerships were more concentrated among homosexual and bisexual females (Gini = 0.46 for both) and males (Gini = 0.55 and 0.60, respectively) as compared to heterosexuals (Gini = 0.33 for females and 0.41 for males).

Conclusions: Sexual minorities report higher numbers of recent sex partners; however, fewer group members account for more of the partnerships. Therefore, interventions that target the most at-risk within a sub-population could have greater impact.