The Relationship between Online Social Networking and Sexual Risk Behaviors among Men Who Have Sex with Men (MSM)

<div><p></p><p>Online social networking usage is growing rapidly, especially among at-risk populations, such as men who have sex with men (MSM). However, little research has studied the relationship between online social networking usage and sexual risk behaviors among at-risk populations. One hundred and eighteen Facebook-registered MSM (60.1% Latino, 28% African American; 11.9% other) were recruited from online (social networking websites and banner advertisements) and offline (local clinics, restaurants and organizations) venues frequented by minority MSM. Inclusion criteria required participants to be men who were 18 years of age or older, had had sex with a man in the past 12 months, were living in Los Angeles, and had a Facebook account. Participants completed an online survey on their social media usage and sexual risk behaviors. Results from a multivariable regression suggest that number of sexual partners met from online social networking technologies is associated with increased: 1) likelihood of having exchanged sex for food, drugs, or a place to stay within the past 3 months; 2) number of new partners within the past 3 months; 3) number of male sex partners within the past 3 months; and 4) frequency of engaging in oral sex within the past 3 months, controlling for age, race, education, and total number of sexual partners. Understanding the relationship between social media sex-seeking and sexual risk behaviors among at-risk populations will help inform population-focused HIV prevention and treatment interventions.</p></div

Demographic and health behaviors in pregnant and postpartum women (n = 377).

<p>Demographic and health behaviors in pregnant and postpartum women (n = 377).</p

Adjusted and unadjusted odds ratio for risk factors of HIV acquisition and transmission in pregnant and postpartum women.

<p>Adjusted and unadjusted odds ratio for risk factors of HIV acquisition and transmission in pregnant and postpartum women.</p

Mean frequency of vaginal sex acts per month by pregnancy vs. postpartum status and self-reported condom use (n = 377 women).

<p>Mean frequency of vaginal sex acts per month by pregnancy vs. postpartum status and self-reported condom use (n = 377 women).</p

Demographic characteristics among men at post-intervention assessment.

<p>Demographic characteristics among men at post-intervention assessment.</p

Demographic characteristics of participants (n = 26).

<p>Demographic characteristics of participants (n = 26).</p

Quotes of HIV-uninfected pregnant women by theme.

<p>Quotes of HIV-uninfected pregnant women by theme.</p

Rates of post-intervention preventive behavior among CSA or CPA men.

<p>Rates of post-intervention preventive behavior among CSA or CPA men.</p

Comparison of the change in HIV diversity between enrollment and follow-up in women in the MAA recent and known non-recent groups (paired analysis).

**<p>HIV diversity was measured using a high resolution melting (HRM) diversity assay, which expresses the genetic diversity in each region analyzed as a single numeric HRM score.</p>a<p>Median paired difference of HRM scores at enrollment and follow-up; interquartile ranges are shown in parentheses.</p>b<p>P values were calculated using Wilcoxon sign rank test for within group change being zero.</p>c<p>P values were calculated using Wilcoxon rank sum test for changes being the same for the MAA recent and known non-recent groups.</p

Use of the HRM diversity assay as part of a multi-assay algorithm for HIV incidence determination.

<p>Panel A shows one example of a multi-assay algorithm developed for HIV incidence determination. In this algorithm, samples from HIV-infected individuals are first tested using the BED-CEIA assay, using a high assay cutoff to indicate non-recent HIV infection (BED screen). Samples that are below the BED screen cutoff (BED recent samples) are then tested using a second serologic assay, such as one based on antibody avidity (avidity screen). Samples that are below the cutoff for the second serologic assay are considered to be “serologic recent” samples. Samples with low CD4 cell count test results are then excluded as non-recent (note that CD4 cell count test results are usually obtained for all HIV-infected individuals at the time of sample collection). Finally, samples that are not excluded based on CD4 cell count are tested using a viral load assay, and samples with low viral loads are excluded as non-recent. The remaining samples are characterized as recent for the purpose of estimating HIV incidence. Panel B shows an alternative multi-assay algorithm that incorporates the HRM diversity assay. In this algorithm, samples that are characterized as serologic recent based on two assays (BED screen and an avidity screen) are tested with a multi-region HRM diversity assay. Samples that have a high HRM score in at least one of the regions tested are excluded as non-recent. Samples that fail to amplify in all regions tested are also excluded as non-recent, based on the assumption that they have low viral loads; this could be confirmed with a viral load assay. Samples that have low HRM scores in all regions tested are characterized as recent for the purpose of estimating HIV incidence.</p