CROI 2015 Program and Abstracts

Abstract Listing

Poster Abstracts

1044 Differences in Acute Retroviral Syndrome by HIV-1 Subtype in a Multicentre Cohort Study in Africa Eduard J. Sanders 1 ; Kimberly A. Powers 2 ; Etienne Karita 3 ; Anatoli Kamali 4 ;William Kilembe 5 ; Susan Allen 6 ; Eric Hunter 6 ; Omu Anzala 7 ; Pat Fast 8 ; Matthew Price 8

1 KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya; 2 University of North Carolina, Chapel Hill, NC, US; 3 Project San Francisco, Kigali, Rwanda; 4 Medical Research Council/Uganda Virus Research Institute, Entebbe, Uganda; 5 Zambia Emory Research Project, Lusaka, Zambia; 6 Emory University, Atlanta, GA, US; 7 Kenya AIDS Vaccine Initiative, Nairobi, Kenya; 8 International AIDS Vaccine Initiative,, New York, NY, US Background: Symptoms of acute retroviral syndrome (ARS) in African adults differ by region and timing of ascertainment, with two Kenyan cohort studies that followed subjects monthly showing a higher report of symptoms and signs than a cohort study from Zambia that followed subjects every 3 months. We sought to determine whether reporting of ARS was associated with HIV-1 subtype at nine participating African research centres (CRC), representing countries with predominant HIV-1 subtypes A, C and D. Methods: Adults with acute or early HIV-1 infection in a multicenter HIV-1 incidence study were enrolled in a sub-study assessing ARS. Estimated date of infection (EDI) was based on a positive plasma viral load or p24 antigen test prior to seroconversion, or the mid-point between a negative and positive HIV-1 serologic test. Eleven ARS signs and symptoms were assessed at sub-study enrollment. We used log-binomial regression to estimate the prevalence of ARS signs and symptoms ascertained in the period ≤ 42 days after EDI, by subtype, and sex. Results: Among 155 volunteers ascertained within 6 weeks following EDI, 67 (43.2%) had pol-derived subtype A, 66 (42.6%) subtype C, and 22 (14.2%) subtype D infection. The number of men and gender ratio by subtype for subtype A was 45 (67%) men; for subtype C: 39 (59%) men, and for subtype D: 13 (59%) men. Individuals with subtype A were statistically significantly more likely than individuals with subtypes C and D to report any of the specifically-listed ARS symptoms, and among those reporting any symptoms (figure), the mean number of symptoms was significantly greater among those with subtype A than among those with subtype C or D. These associations were not modified by sex.

Conclusions: In this multicenter African cohort of patients evaluated within 6 weeks following EDI, individuals with subtype A were significantly more likely than individuals with subtypes C and D to report any of the 11 specifically-listed ARS symptoms. Further studies elucidating differences in innate immune responses by HIV-1 subtype in patients with acute HIV infections are recommended. 1045 Using GPS Data to Construct a Spatial Map of the HIV Epidemic in Malawi Danielle E. Robbins ; Brian J. Coburn; Sally Blower David Geffen School of Medicine at University of California Los Angeles, Los Angeles, CA, US Background: In order to develop effective control strategies for HIV epidemics, it is essential to know howmany individuals are infected with HIV and their location; i.e. to construct a density of infection (DoI) map. Here we show how to construct such a map for Malawi where the prevalence of HIV is ~11%. Methods: We used geo-referenced data from the 2010 Malawi Demographic and Health Survey (MDHS), a nationally representative survey that included HIV testing. An individual’s demographic characteristics are linked with their test results. We used data from 7,091 women and 6,497 men aged 15-49 years old. We used these geo-referenced data and applied spatial interpolation techniques to construct a surface prevalence map of the HIV epidemic for the entire country. We then used WorldPop data to construct a demographic map showing the geographic distribution of the population and the population density. We then constructed the DoI map for Malawi by combining the surface prevalence and demographic maps using raster multiplication. Results: The surface prevalence maps shows that there is significant geographic variation in HIV prevalence throughout the country. In the rural northern region prevalence is ~7%. In the central region, which is semi-urban (i.e. a mix of rural and urban) prevalence is only slightly higher, ~8%. Notably, HIV prevalence is substantially higher (~15%) in the southern urban region. In all regions prevalence is higher in women than in men: ~8% vs. ~5% (northern region), ~9% vs. ~6% (central region), and ~18% vs. ~11% .The demographic map shows that there is significant spatial clustering of the population: 10% of the country contains 48% of the population. In addition 13% of the population lives in the northern region, 42% lives in the central region, and 45% in the southern region. Overall, over 60% of the population lives in urban centers. Our DoI map shows that although the HIV epidemic in Malawi is generalized, the majority of the infected individuals live in urban centers. Using our map we estimate that approximately 690,000 individuals aged 15-49 are infected. Conclusions: The newmethodology that we have developed enables us to identify, and locate, both diagnosed and undiagnosed individuals who are infected with HIV. The DoI maps can be used to identify the areas in greatest need of treatment and prevention programs. Notably, the methodology that we have developed can be used to construct DoI maps for 23 other sub-Saharan African countries. 1046 HIV Incidence in Rural Malawi DuringWidespread Antiretroviral Treatment Availability Alison Price 1 ; Menard Chihana 2 ; Ndoliwe Kayuni 2 ; Amelia C. Crampin 1 ; Milly Marston 1 ; Basia Zaba 1 ; Estelle McLean 1 ; Olivier Koole 1 ; Moffat Nyirenda 1 1 London School of Hygiene and Tropical Medicine, Chilumba, Malawi; 2 Karonga Prevention Study, Chilumba, Malawi Background: In Malawi adult mortality and death attributable to HIV has fallen with widespread availability of antiretroviral treatment (ART) since 2005 and HIV prevalence approaches 8%. However the population impact on HIV incidence in this period is unclear. Methods: We used population-level demographic surveillance and annual socioeconomic and HIV sero-survey data from Karonga Prevention Study in rural Malawi, collected between 2007 and 2011, to calculate age specific incidence rates and rate ratios (using Poisson regression), by calendar year and socio-demographic factors, with adjustment for potential confounders.

Poster Abstracts

611

CROI 2015