Background The HIV epidemic has shown a considerable amount of heterogeneity, complicating the design and placement of prevention, intervention and treatment programmes. Place-based analyses providing specific information on pathogen prevalence, risk behaviours and other micro-level behaviours can help to target public health responses.
Methods Data were from a cross-sectional survey of most-at-risk populations (MARPs) from Winnipeg, Canada. Respondents were recruited through respondent-driven sampling, and biological, behavioural and egocentric network data were collected. Respondents named locations where they frequented and where they engaged in risk behaviours, including the use of crack cocaine, injectable and non-injectable drug use, and solvents; and either seeking sex work clients or sex workers. Locations were geo-coded up to Statistics Canada dissemination areas. Two-mode network visualisation and centrality, degree, and betweenness measures were generated using UciNet (V.6).
Results From a sample size of 600, nine locations were named by 10 or more respondents. The following results pertain only to these nine locations (N: 231). Locations corresponded to three “hot spots” in Winnipeg’s inner and outer core areas. Across the sample, HIV and HCV prevalence was 9.8% and 51.5%, respectively. Prevalence varied considerably by location, ranging from 0% to 15% for HIV and 20% to 70% for HCV. Degree ranged from 0.054 to 0.330, closeness from 0.178 to 0.411 and betweenness from 0.054 to 0.521. No association between prevalence of HIV and HCV and network metrics was found. Substantial heterogeneity in pathogen prevalence and risk behaviour was observed by location, while pathogen, risk and mixing characteristics of populations bridging the nine locations were made apparent by two-mode visualisation.
Conclusion Two-mode analysis of egocentric network data revealed geographic clustering of risk behaviours, while at the same time demonstrating substantial mixing between high-risk bridge populations. Targeted prevention and intervention efforts can be aided by use of micro-level analyses.
- geographical analysis
- HIV/HCV coinfection
- social network analysis