1280. Geospatial Spread of HIV in the Cologne-Bonn Region, Germany: From 2001 to 2016. (26th November 2018)

• Record Type: Journal Article
• Authors: Stecher, Melanie; Hoenigl, Martin; Eis-Huebinger, Anna-Maria; Lehmann, Clara; Fätkenheuer, Gerd; Wasmuth, Jan-Christian; Knops, Elena; Metha, Sanjay; Vehreschild, Janne; Chaillon, Antoine
• Is Part Of: Open forum infectious diseases. Volume 5(2018)Supplement 1
• Page Start: S390
• Page End: S390
• Publication Date: 2018-11-26
• Subjects: Communicable diseases -- Periodicals; Medical microbiology -- Periodicals; Infection -- Periodicals; 616.9
• DOI: 10.1093/ofid/ofy210.1113
• Languages: English
• ISSNs: 2328-8957

Abstract

Abstract: Background: Geographical targeting of interventions of hotspots of HIV transmission increases the impact of HIV intervention. We combined molecular epidemiology and geospatial analyses to provide insights into the drivers of HIV transmission and the contribution of geographical hot spots to the rapidly evolving local HIV epidemic of Cologne-Bonn. Methods: We included 714 HIV-1-infected ART naïve individuals, followed at the University Hospitals Cologne and Bonn between 2001 and 2016. Phylogenetic and network analyses were performed to infer putative relationships. Assortativity index (AI, i.e., shared attributes) and characteristics of genetically linked individuals were analyzed. The geospatial diffusion of the local epidemic (i.e., viral gene flow) was evaluated using a Slatkin-Maddison approach. Geospatial dispersal of local HIV transmission was determined by calculating the average distance between genetically linked individuals (centroids of 3-digit zip code of residency, ArcGIS®). Results: Of 714 sequences, 217 (30.4%) had a putative linkage with at least one other sequence, forming 77 clusters (size range: 2–8). Genetically linked individuals were significantly more likely to live in suburban areas ( P = 0.035), <30 years of age ( P = 0.013), infected with HIV-1 subtype B (P = 0.002). AI for concurrent area of residency showed that individuals were nonassortative in the network (−0.0026, P = 0.046), indicating that clustering individuals tended to cluster with individuals living in a different zip code. Geospatial analyses revealed that the median distance between genetically linked individuals was 23.4 km, significantly lower than expected (median 39.68 km; P < 0.001) (Figure 1A). Slatkin Maddison analyses revealed increased gene flow originating from Central Cologne toward the surrounding areas ( P < 0.001, Figure 1B). Conclusion: Phylogeographic analysis suggests that central Cologne may be a significant driver of the regional epidemic. While clustering individuals lived closer than unlinked individuals, they were less likely to be linked to others from their same zip code. This may reflect individuals reaching out of their neighborhoods and social circles to meet new partners. Disclosures: All authors: No reported disclosures.