Abstract
The literature has shown that surveillance data for common sexually transmitted infections (STIs) does not adequately measure disease occurrence or effectiveness of control measures because the number of notifications is strongly influenced by the amount of testing. This thesis explores how laboratory testing data could be used to improve the surveillance of STIs in the Northern Territory (NT), which has very high levels of STIs, particularly in the Aboriginal population.Over the past 15 years, innovations using testing data to improve STI surveillance systems have occurred in several countries, and more recently in two Australian states, with differing but promising success, but most of them lacked sustainability. I therefore examined the theoretical and practical aspects of enhancing an STI surveillance system using laboratory testing data, and conducted three projects to test the feasibility and effectiveness of this approach in the NT.
The first project used testing data to investigate the sharp decrease in gonococcal cultures performed in the NT, illustrating the data’s utility in monitoring testing activities at the jurisdiction level. The second project used testing data to calculate testing rates and test positivity rates to assist in the interpretation of time trends in the gonorrhoea notification rate at the district level. The third project used testing and notification data to evaluate the effectiveness of a sexual health program (that included population screening for STIs) in a group of remote communities.
The benefits of using laboratory testing data to enhance STI surveillance have been demonstrated in several countries and two Australian states. The thesis has demonstrated the feasibility of accessing and analysing such data in the NT and the benefits for STI control in a high-prevalence population. I therefore conclude by proposing a best practice model for how such an enhanced surveillance system could, and should, be implemented in the NT.
| Date of Award | 2015 |
|---|---|
| Original language | English |
| Supervisor | John Condon (Supervisor) & Steven Skov (Supervisor) |