Thesis (M.Tech.: Emergency Medical Care)- Dept. of Emergency Medical Care and Rescue, Durban University of Technology, 2008. xii, 149 leaves, Appendices A-I. / Prehospital emergency medical service (EMS) data is essential for understanding the functioning of the services as well as the community's health. Being able to clearly and accurately define the patient population in terms of demographics and clinical condition may guide the EMS in resource management, clinical governance, research, education and political decisions. However, such data is limited in South Africa. This research, therefore, aimed to develop a data collection tool to determine the population of prehospital emergency medical care patients in South Africa. The objectives were: (i) determination of what data needed to be collected, (ii) development of a tool to collect the data, and (iii) testing the tool for ease and appropriateness of use and completeness of data collection in an authentic environment. A mixed-method, predominantly qualitative methodological design was used, with some elements of grounded theory. There were three phases corresponding to the objectives. The first two were qualitative and the third was both qualitative and quantitative. In the first phase expert consensus was sought, using a focus group discussion and Delphi study, to develop a minimum data set (MDS) to describe the patient population. The resultant MDS consisted of 18 data elements which could be categorised into demographics, time and location of EMS use, the clinical reasons for EMS use, and the actual use of the EMS. A tool and associated user instructions, based on the findings of Phase One, were developed and refined during Phase Two. Phase Three was used for testing the tool in an authentic environment. The tool was found to be acceptable and user-friendly. Further testing of the tool for accuracy and reliability is recommended.

published_or_final_version / Community Medicine / Master / Master of Public Health

published_or_final_version / Community Medicine / Master / Master of Public Health

published_or_final_version / Public Health / Master / Master of Public Health

Background: The seriousness of human influenza virus infection, in combination with the transmissibility of the virus, determines the impact that the virus will have in a population. However, the uncertainty surrounding the seriousness and changes in seriousness hindered the calibration of the early public health response. Methods: I applied statistical models to population-based mortality data and hospitalizations among patients with laboratory-confirmed H1N1pdm09. I estimated the disease burden, retrospectively and prospectively determined seriousness of influenza virus infections including the risk of death on a per-infection basis (IFR) and on a per-hospitalization (HFR) of H1N1pdm09, and investigated changes in seriousness. I used serologic surveillance data to estimate the cumulative incidence of infection in a population, and used it as the denominator of the IFR. I also conducted systematic reviews and meta-analysis to summarize published estimates of the risk of death among cases (CFR) and HFR of the pandemic influenza H1N1pdm09 virus. Results: I estimated that the first wave of H1N1pdm09 was associated with approximately 232 (95% confidence interval: 136, 328) excess deaths in all ages in Hong Kong, which was around 4 times the observed number of laboratory-confirmed deaths of H1N1pdm09. The point estimates for the IFR and HFR increased substantially with age. I included 77 estimates for the CFR from 50 published studies; whereas I included 187 estimates for the HFR from 184 published studies. The CFR was widely used to assess seriousness but the variation of a ‘case’ varied considerably in the literature. Variability in published estimates of the HFR was much less than variability in the CFR. Conclusions: Early in the next pandemic, estimation of the HFR, IFR or symptomatic CFR may provide a timely picture of the seriousness of infection, particularly if presented in comparison between two influenza virus infections in the same setting. Ongoing monitoring of mortality and influenza activity could permit identification of changes in seriousness of influenza virus infections. / published_or_final_version / Public Health / Doctoral / Doctor of Philosophy

Thesis (M.Tech.: Emergency Medical Care)- Dept. of Emergency Medical Care and Rescue, Durban University of Technology, 2008. xii, 149 leaves, Appendices A-I. / Prehospital emergency medical service (EMS) data is essential for understanding the functioning of the services as well as the community's health. Being able to clearly and accurately define the patient population in terms of demographics and clinical condition may guide the EMS in resource management, clinical governance, research, education and political decisions. However, such data is limited in South Africa. This research, therefore, aimed to develop a data collection tool to determine the population of prehospital emergency medical care patients in South Africa. The objectives were: (i) determination of what data needed to be collected, (ii) development of a tool to collect the data, and (iii) testing the tool for ease and appropriateness of use and completeness of data collection in an authentic environment. A mixed-method, predominantly qualitative methodological design was used, with some elements of grounded theory. There were three phases corresponding to the objectives. The first two were qualitative and the third was both qualitative and quantitative. In the first phase expert consensus was sought, using a focus group discussion and Delphi study, to develop a minimum data set (MDS) to describe the patient population. The resultant MDS consisted of 18 data elements which could be categorised into demographics, time and location of EMS use, the clinical reasons for EMS use, and the actual use of the EMS. A tool and associated user instructions, based on the findings of Phase One, were developed and refined during Phase Two. Phase Three was used for testing the tool in an authentic environment. The tool was found to be acceptable and user-friendly. Further testing of the tool for accuracy and reliability is recommended.

Thesis (doctoral)--Göteborgs universitet, 2003. / Includes bibliographical references.

Thesis (M.S.)--Oregon State University, 2010. / Printout. Includes bibliographical references (leaves 57-59). Also available on the World Wide Web.

Public health surveillance is a critical part of understanding, and ultimately influencing, health behaviors. Traditional methods, such as questionnaires and focus groups have significant limitations including cost, delay, and size. Online social media data has the potential to overcome many of the challenges of traditional methods, but its exploitation is not trivial. We develop and apply computational techniques to enable public health surveillance in novel ways and on a larger scale than currently performed.In this regard, we present techniques for mining the who, what, and where of public health surveillance in social media. We show how computational methods can identify health content and conversations in social media, and that people do in fact speak openly about health topics, including those that might be considered private. In addition, we demonstrate how location information can be mined and used to study distributions of various conditions. Finally, and perhaps most importantly, we develop techniques to identify and leverage pertinent social network relationships in public health surveillance. We demonstrate each of these approaches in large data sets of actual social networks spanning blogs, micro-blogs, and video-sharing sites.

published_or_final_version / Community Medicine / Master / Master of Public Health