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Background Alongside traditional epidemiologic tools, network analyses and molecular epidemiology offer deeper insights into the structure of STBBI epidemics. In the context of a 2014 gonorrhea (NG) outbreak, this study sought to compare molecular networks to case-contact networks constructed from public health investigations. Methods Data were from enhanced public health investigations of NG in Winnipeg, Canada. NG-MAST was used to determine the molecular subtypes of NG. Subtypes were described by socio-demographic/clinical characteristics. Multivariable logistic regression models were used to assess the association of socio-demographic/clinical characteristics, and having the most frequently reported subtype. Networks constructed from case-contact investigations were visualized; components were characterized with univariate network statistics, including degree centralization. Conditional uniform graph (CUG) tests assessed observed degree centralization. Results In total, 126 NG cases were genotyped, with 41 subtypes found. Five subtypes accounted for 51% of all subtypes, with ST-3672 (n=22) predominant. At the bivariate level, infection with ST-3672 was associated with younger age (62% of those infected were 15–19 years old, p=0.002), and chlamydia co-infection (67% vs 37%, p=0.012). In multivariable analysis, age group remained significant, while an interaction between inner-core residency and chlamydia co-infection was detected. Case-contact networks were highly-fragmented, consisting mainly of dyads and triads. Of 85 components, the largest component included 6 nodes, while 61% were dyads. CUG testing indicated in-degree centralization was lower than expected (p Conclusion Molecular data revealed connections that were not apparent from case-contact investigations alone, leading to more cases potentially linked together, and over a wider geographic area. A handful of subtypes were responsible for the majority of infections. Early identification of dominant subtypes may potentially curtail transmission of NG. Disclosure No significant relationships.

Background There are large numbers of people who inject drugs (PWID) in Pakistan, with a high and growing HIV prevalence in many cities. Cross-sectional bio-behavioural surveys conducted over fifteen years have shown heterogeneous HIV epidemic trajectories in different cities with inconsistent associations between aggregate and individual measures of behaviour and epidemic trajectories. This study triangulates behavioural, biological, and network data to gain new insights into drivers of local epidemics among PWID. Methods A questionnaire was administered to 1,439 PWID in five cities in Pakistan in 2014 and dried blood spots were collected for HIV testing and sequencing. The questionnaire collected locations where participants injected recently and these were used to construct place-based injection networks. Demographic, behavioral, geographic, and network structure data were tested for associations with HIV and HIV phylogenetic analysis was conducted to identify transmission clusters. Results HIV prevalence varied between the cities, from 8.6% to 45.3%. Although aggregate individual behaviours were not strongly associated with HIV, there were differences between cities in injecting practices and network configurations. Injection network configurations differed between cities, ranging from networks with dense sub-regions to widely dispersed networks. A city that experienced an explosive HIV outbreak showed particularly dense geospatial networks and a large phylogenetic cluster (n=52) suggesting a link between geospatial concentration and rapid HIV transmission. In a city where the use of professional injectors was associated with HIV at the individual level, there were dense network subregions and HIV+ individuals were more likely to be more centrally located in networks (p Conclusion Contextual factors shape the injection practices and network configurations among PWID in Pakistan. Factors that influence HIV transmission dynamics are more complex than individual behaviours, and include professional injectors and geographic and network clustering. Disclosure No significant relationships.