CROI 2018 Abstract eBook

Abstract eBook

Poster Abstracts

varied by age group among males and among females (Figure), with the highest prevalence observed among females in the 30-39 age group (60.3% [114/189]). HIV prevalence was significantly higher among females compared to males in the 20-29 year age group (PR=2.55 [95%CI: 1.78-3.63]) and 30-39 year age group (PR=1.84 [95%CI: 1.45-2.32]; though near identical at the older age groups. The frequency of viral suppression (<1000 copies/mL) was 48.8% and similar among males (45% [99/220]) and females (51.5% [160/311]; age-adjusted PR=1.14 [95%CI: 0.94-1.37]). The incidence testing algorithm identified 4 males (of 220 HIV+ tested) and 11 females (of 311 HIV+ tested) as recently infected, yielding an overall annual HIV incidence estimate of 2.9% (95%CI=1.4-4.3). HIV incidence was higher among females (4.7 [95%CI=1. 9-7.6]) in comparison to males (1.4% [95%CI=0.0-2.9]). Conclusion: These results demonstrate a high burden and incidence of HIV infection among males and females in East London. In addition, the prevalence of HIV viral suppression in this population is substantially lower than 90-90-90 goal of 73%. Together these data support an expanded outreach in East London to identify, test, and treat these untreated HIV infected individuals with the ED serving as a promising point-of-contact for this underserved population.

0.82) in 2016; both were notably lower than the official prevalence estimate of 24.8% for Siaya. Ratios between official and RANC HIV prevalence were similar in 2014 and 2015. Conclusion: Results from Gem HDSS indicate RANC may over-estimate HIV prevalence in adults, and recent official estimates are consistently higher than RANC, suggesting Spectrummay over-estimate true adult HIV prevalence in Nyanza. Over-estimation would result in apparent under-achievement of the first and second 90% fast-track targets for knowing HIV status and overall ART coverage among persons living with HIV in Nyanza. This analysis illustrates the importance of triangulating all available data to monitor the HIV epidemic and guide the response.

Poster Abstracts

936 ASSOCIATION OF MOBILITY WITH HIV RECENT INFECTIONS AND VIREMIA IN BOTSWANA Mompati O. Mmalane 1 , Sikhulile Moyo 1 , Baraedi W. Sento 1 , Jean Leidner 2 , Kara Bennett 3 , Thandie Phindela 4 , Kutlo Manyake 1 , Ernest Moseki 1 , Tendani Gaolathe 1 , Joseph Makhema 1 , Molly Pretorius Holme 5 , Janet Moore 6 , Max Essex 5 , Shahin Lockman 5 , Kathleen Wirth 5 1 Botswana Harvard AIDS Inst Partnership, Gaborone, Botswana, 2 Goodtables Data Consulting, Norman, OK, USA, 3 Bennett Statistical Consulting, Inc, New York, NY, USA, 4 Botswana Ministry of Health, Gaborone, Botswana, 5 Harvard University, Cambridge, MA, USA, 6 CDC, Atlanta, GA, USA Background: Mobility may create opportunities for risky behavior in the context of diminished societal controls, constrain health-seeking behavior, and has been associated with higher HIV prevalence. We examined associations between mobility, recent HIV infection, and detectable HIV-1 RNA in Botswana, a country with a highly mobile population. Methods: As part of the Botswana Combination Prevention Project (BCPP), an ongoing cluster-randomized HIV prevention trial, consenting persons aged 16-64 years from a random 20% household sample in 30 peri-urban/ rural communities were surveyed. All participants without documentation of positive HIV status underwent HIV testing, and HIV-1 RNA was measured in all HIV-infected participants, regardless of treatment status with >400 copies/mL considered viremic. Recent HIV infection was assessed cross-sectionally using HIV testing and treatment history and Limiting-Antigen Avidity Assay (LAg) data. Mobility was defined as self-reported absence from the community ≥1 night during the past year. Modified Poisson generalized estimating equations were used to obtain crude and age- and gender-adjusted prevalence ratios (PR) and 95% confidence intervals (CI) accounting for the clustered design. Results: Among 12,583 participants with mobility data, 6,783 (54%) met the criteria for mobility; of these 26%were HIV-positive compared to 32% of non-mobile persons. One-third of sexually-active mobile persons reported concurrent partnerships during the past year compared to 25% of non-mobile persons (Table 1). Forty-two participants had recent HIV infection and 26% of all infected persons were viremic. Similar proportions of mobile (0.4%) and non-mobile (0.3%) participants were recently-infected (adjusted P=0.11). In contrast, HIV-positive persons who spent ≥1 night outside the community had a 20% higher probability of being viremic compared to those who reported no overnight travel (adjusted PR: 1.2; 95%CI: 1.1–1.4). A lower proportion of HIV-positive mobile participants knew their HIV status (P=0.001) or were on antiretroviral treatment (P=0.02) compared with non-mobile individuals.

935 ARE HIV PREVALENCE ESTIMATES FOR WESTERN KENYA TOO HIGH? Peter W. Young 1 , Emily C. Zielinski-Gutierrez 1 , Daniel Kwaro 2 , Martien W. Borgdorff 3 , Joshua Gitonga 4 , Joyce Wamicwe 5 , Samuel M. Mwalili 1 , Rachel Joseph 6 , Kevin M. De Cock 1 1 US CDC Nairobi, Nairobi, Kenya, 2 Kenya Medical Research Institute, Kisumu, Kenya, 3 University of Amsterdam, Amsterdam, Netherlands, 4 National AIDS Control Council, Nairobi, Kenya, 5 National AIDS and STD Control Programme, Nairobi, Kenya, 6 US CDC Kisumu, Kisumu, Kenya Background: The Nyanza region comprises six counties bordering Lake Victoria in Western Kenya including the three highest HIV prevalence counties in Kenya of Siaya, Homa Bay and Kisumu. Together, these three counties account for 27% of all patients on antiretroviral treatment (ART) and 31% of estimated unmet need for ART in Kenya. The UNAIDS-supported Spectrummodel is used to generate HIV incidence and prevalence estimates for Kenya which are distributed from regions to counties using a workbook method, resulting official estimates are used to set county HIV testing and treatment targets. We triangulated these estimates with routine antenatal care (RANC) HIV testing results and population-based HIV prevalence estimates from a health and demographic surveillance system (HDSS). Methods: We compiled annual official HIV prevalence estimates for 2014-2015 for Nyanza, and RANC prevalence frommonthly facility reports from 2014-2016 submitted to the national health information system, and HIV prevalence from a community-based HIV sero-surveillance activity that screened 15,627 persons aged 15-49 years in the HDSS in Gem sub-County, Siaya County in 2016. We compared RANC to population-based HIV prevalence within the HDSS catchment area, and to official estimates. Results: In 2015, official estimates were higher than pooled RANC HIV prevalence (14.8% vs. 12.4% for the six county Nyanza region, 24.8% vs. 16.7% for Siaya, 26.0% vs. 19.6% for Homa Bay, 19.9% vs. 15.5% for Kisumu); the ratio in adults ranged from 1.29 to 1.49 (Figure). The ratio between Spectrum estimates for pregnant women and RANC for Nyanza was 1.23 (15.3% vs. 12.4%). Gem RANC was slightly higher than Siaya RANC as a whole (17.2% vs. 16.7%). In Gem, HDSS-based prevalence estimates were lower than RANC (ratio

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