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Epidemiologic Approaches to Understanding Gonorrhea Transmission Dynamics and the Development of Antimicrobial Resistance



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Globally, the incidence of infection caused by Neisseria gonorrhoeae is the second highest among the bacterial sexually transmitted infections. In Canada, declining rates during the 1990s suggested progress toward curbing gonorrhea; however, those have been increasing since 1999, with rates in Saskatchewan among the highest in the country. Infection can cause serious complications in men and women, and reported resistance to third-generation cephalosporins could lead to potentially untreatable infections. Increased understanding of gonorrhea transmission dynamics, sexual networks, and predictors of antimicrobial resistance development is needed to inform the development of improved approaches to prevention and treatment. The research presented herein draws upon data from Shanghai, China, and Saskatchewan, Canada, to compare and contrast varying epidemiologic approaches to enhancing understanding of gonorrhea in the two settings. Using traditional statistical approaches, multi-level statistical modeling, social network analysis, and dynamic simulation modeling, questions related to sexual behavior, partner presentation, and antimicrobial resistance development are explored. Each technique is evaluated for its potential contribution to overall understanding of the issues related to the ongoing gonorrhea epidemic, globally, and in Saskatchewan. The relative strengths and limitations of the application of the analytical approaches in the different settings are described. Socio-demographic characteristics provided useful indicators of antimicrobial resistant infection among patients with gonorrhea from Shanghai. Further, socio-demographic characteristics were also useful for predicting presentation of a partner for testing and treatment and the use of condoms during intercourse, among this study population. In Saskatchewan, socio-demographic characteristics were useful in predicting coinfection with gonorrhea and chlamydia at the time of diagnosis as well as repeat infection with gonorrhea. Social network analysis of the Saskatchewan dataset provided little additional understanding of the gonorrhea epidemic in the province. This result was largely related to how STI data are collected and stored in the province. The utility of dynamic simulation modeling to investigate the potential impact of antimicrobial resistance in Saskatchewan was also limited due to the same data constraints. However, the insight gained from the model building process and findings from the working model did offer a starting point for conversations around the best ways to postpone the development of antimicrobial resistance in N. gonorrhoeae in Saskatchewan, as well as contribute additional information about how the ways in which STI data are collected and stored in the province considerably restrict the applicability of otherwise powerful epidemiologic tools. With persistently high rates of disease transmission, and the threat of untreatable infections due to antimicrobial resistance, N. gonorrhoeae remains a substantial public health threat locally and globally. The research presented herein describes various approaches to understanding and controlling this disease, applied in contrasting settings. There are a wide variety of elements that should be considered when choosing the appropriate tool(s) to address gonorrhea in a given population; there is no “one size fits all” solution. The local epidemiology of disease, cultural and behavioural norms, the characteristics of the notifiable disease reporting and information systems, and the availability of suitable data all affect the relative strengths and weaknesses of the available analytic methods and disease control approaches.



Neisseria gonorrhoeae, gonorrhea, antimicrobial resistance, AMR, sexually transmitted infection, STI, system dynamics modeling, social network analysis, multi-level modeling



Doctor of Philosophy (Ph.D.)


School of Public Health




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