CROI 2017 Abstract e-Book

Abstract eBook

Poster and Themed Discussion Abstracts

Results: Of 7071 PLWDH presumed living in KC in 2015, 5286 (75%) were cis MSM and 60 (<1%) were trans. Compared to cis PLWDH, proportionally more trans PLWDH were Hispanic (28% vs. 13%), <35 years (25% vs. 15%), virally unsuppressed (25% vs. 19%), diagnosed with AIDS within 1 year of HIV diagnosis (35% vs. 29%), and had a history of injection drug use (27% vs. 13%) Of Trans Pride Survey respondents who were assigned male at birth and identify as a woman and/or non-binary, 2% reported an HIV-positive sex partner, 35% a cis male partner, 43% cis female partner, and 41% trans partner. The corresponding percentages for respondents who were assigned female at birth and identify as a man were 1%, 27%, 43%, and 34%. Among PLWDH, a recent trans sex partner was reported by 0.8% of newly diagnosed cases and 0.1% of HIV care patients who participated in MMP. Among HIV-negative cis-MSM, 4% of Pride Survey respondents reported a recent trans partner and 3% of STD Clinic patients reported ever having a trans partner. Conclusion: The PHSKC HIV surveillance system includes a small number of trans people. Along with evidence of limited overlap between trans sexual networks and people with HIV or at high-risk for HIV, this may suggest a lower burden of HIV among trans people in KC than reported in other areas. These findings are impacted by data collection methods and may not be generalizable. Evaluations of sexual network characteristics may be informative, especially when case counts and population denominators cannot be reliably estimated. Efforts to more systematically monitor HIV among trans persons are needed.

Poster and Themed Discussion Abstracts

847 SPATIAL CLUSTERING OF HIV IN KENYA MAY NOT MATCH WELL-KNOWN EPIDEMIC PATTERN

Anthony Waruru 1 , Thomas Achia 2 , James Nganga 3 , Joyce Wamicwe 4 , James Tobias 5 , Mary Mwangi 1 , Evelyn Muthama 1 , Emily Zielinski-Gutierrez 2 , Tom Oluoch 6 , Thorkild Tylleskär 7 1 US CDC (CDC), Nairobi, Kenya, 2 US CDC, Nairobi, Kenya, 3 Natl Bureau of Statistics, Nairobi, Kenya, 4 Natl AIDS and STI Control Prog (NASCOP), Nairobi, Kenya, 5 US CDC, Atlanta, GA, 6 US CDC, Atlanta, USA, 7 Univ of Bergen, Bergen, Norway Background: In a spatially well-known and dispersed HIV epidemic, identifying geographic clusters with significantly higher HIV-prevalence is important for focusing interventions. We conducted geo-spatial analysis on data from a nationally representative Kenya AIDS Indicator Survey 2012 to identify clusters with high number of HIV-infected persons 15-64 years old in Kenya. Methods: We used Kulldorff spatial-scan statistics implemented in SATScan™ program to assess whether HIV prevalence is randomly distributed over space or whether a cluster can be detected with higher than random distribution of PLHIV by using a Poisson-based model. We classified HIV-infected persons as belonging to high vs. lower prevalence (HP/ LP) clusters. Using this classification, we assessed distributions and associations of clustering with socio-demographic and bio-behavioral HIV risk factors. We used a χ-square test to compare weighted proportions. Results: Out of 358 survey clusters, 238 (66.5%) had at least one HIV-infected person (Figure 1). Of those, 41(17%) were HP, with 1.05-4.15 times greater PLHIV observed than expected. Fewer respondents in HP clusters (4.3%, 95% CI 3.2-5.3) had no formal education compared to respondents in LP clusters (7.8%, 95% CI 6.2-9.5), p=0.0025. Half of respondents in HP clusters (50.0%, 95% CI 40.1-59.9) were living in rural areas compared to 66.7% (95% CI 62.5-70.9) in LP clusters, p=0.0097. Fewer respondents in HP clusters had travelled away in the past 12 months (42.3%, 95% CI 38.9-45.6) than in LP clusters (53.0%, 95% CI 50.7-55.2), p<0.0001. Fewer respondents in HP clusters vs. LP clusters had tested for HIV only once, 26.0% (95% CI, 23.7-28.4) and 32.1% (95% CI, 30.3- 33.8) respectively, p<0.001. Among men, 22.1% in HP clusters had ever been clients of female sex

CROI 2017 366