Complexity and the intersection of social and sexual structure, ecological niches and the epidemic potential of sexually transmitted and bloodborne infections: empirical and theoretical observations
| dc.contributor.author | Shaw, Souradet Yuh-Nan | |
| dc.contributor.examiningcommittee | Wylie, John (Community Health Sciences) Gumel, Abba (Mathematics) Jennings, Jacky (Johns Hopkins University) | en_US |
| dc.contributor.supervisor | Blanchard, James (Community Health Science) | en_US |
| dc.date.accessioned | 2018-01-09T13:49:44Z | |
| dc.date.available | 2018-01-09T13:49:44Z | |
| dc.date.issued | 2017 | |
| dc.degree.discipline | Community Health Sciences | en_US |
| dc.degree.level | Doctor of Philosophy (Ph.D.) | en_US |
| dc.description.abstract | Introduction: Incomplete understanding of how context explains heterogeneity in transmission dynamics of sexually transmitted and bloodborne infections (STBBIs) has led to deficiencies in prevention and control activities. Like place-based analyses, social network analysis has held much promise for incorporating context to the study of STBBIs. The costs and complexity associated with empirical network data have limited its full potential. Recent advances in the use of exponential random graph models (ERGM) and molecular epidemiology have re-invigorated network-based research. ERGM theory focuses on local processes creating global network structure, embodying a generative approach to network formation; this approach contends that networks unfold and evolve predictably, thus epidemics should also be similarly predictable. This dissertation aims to combine traditional surveillance methods with advances in network methodologies and orient their use to an applied public health context. Methods: Using public health surveillance data, and focusing on the epidemiology of STBBIs in Winnipeg, the three studies employed a context-based perspective in understanding underlying processes creating observed empirical data. The inequality in the distribution of STBBIs was examined. Networks created through molecular genotyping and through traditional case-and-contact investigations were compared using descriptive statistics and univariate network metrics. Stochastic simulation modelling, based on the ERGM framework, examined the interaction between pathogen characteristics, mixing patterns and network topology. Results: Each STBBI had its own ecological niche, although these were malleable over time. Geographic inequality in the distribution of gonorrhea was decreasing in the context of a growth phase, while also occupying similar geographic space as chlamydia. Molecular epidemiology served a complementary role, revealing potentially hidden links between cases. The most successfully transmitted gonorrhea subtype was associated with chlamydia co-infection. Simulation modelling revealed a relationship between assortative mixing and pathogen infection duration; high levels of assortative mixing muted the modeled epidemic trajectory, with the most drastic effect on infections with shorter duration of infectivity. Conclusion: The three studies cohesively address current challenges in applying context to public health analyses, while expanding our understanding of the mechanisms needed to alter the trajectory of STBBI epidemics. Insights gained from the included analyses form the basis of a proposed context-based surveillance framework. | en_US |
| dc.description.note | February 2018 | en_US |
| dc.identifier.uri | http://hdl.handle.net/1993/32748 | |
| dc.language.iso | eng | en_US |
| dc.rights | open access | en_US |
| dc.subject | Epidemiology | en_US |
| dc.subject | Sexually transmitted and bloodborne infections | en_US |
| dc.subject | Network epidemiology | en_US |
| dc.subject | Complexity | en_US |
| dc.subject | Surveillance | en_US |
| dc.subject | Public health | en_US |
| dc.title | Complexity and the intersection of social and sexual structure, ecological niches and the epidemic potential of sexually transmitted and bloodborne infections: empirical and theoretical observations | en_US |
| dc.type | doctoral thesis | en_US |
| local.subject.manitoba | yes | en_US |