Injury now surpasses disease as the leading global cause of premature death and disability, claiming over 5.8 millions lives each year. However, unlike disease, which has been subjected to a rigorous epidemiologic approach, the field of injury prevention and control has been a relative newcomer to scientific investigation. With the distribution of injury now well described (i.e. ???who???, ???what???, ???where??? and ???when???), the underlying hypothesis is that progress in understanding ???how??? and ???why??? lies in classifying injury occurrences aetiologically. The advancement of a means of classifying injury aetiology has so far been inhibited by two related limitations: 1. Structural limitation: The absence of a cohesive and validated aetiological taxonomy for injury, and; 2. Methodological limitation: The need to manually classify large numbers of injury cases to determine aetiological patterns. This work is directed at overcoming these impediments to injury research. An aetiological taxonomy for injury was developed consistent with epidemiologic principles, along with clear conventions and a defined three-tier hierarchical structure. Validation testing revealed that the taxonomy could be applied with a high degree of accuracy (coder/gold standard agreement was 92.5-95.0%), and with high inter- and intra- coder reliability (93.0-96.3% and 93.5-96.3%). Practical application demonstrated the emergence of strong aetiological patterns which provided insight into causative sequences leading to injury, and led to the identification of effective control measures to reduce injury frequency and severity. However, limitations related to the inefficient and error-prone manual classification process (i.e. average 4.75 minute/case processing time and 5.0-7.5% error rate), revealed the need for an automated approach. To overcome these limitations, a knowledge acquisition (KA) software tool was developed, tested and applied, based on an expertsystems technique known as ripple down rules (RDR). It was found that the KA system was able acquire tacit knowledge from a human expert and apply learned rules to efficiently and accurately classify large numbers of injury cases. Ultimately, coding error rates dropped to 3.1%, which, along with an average 2.50 minute processing time, compared favourably with results from manual classification. As such, the developed taxonomy and KA tool offer significant advantages to injury researchers who have a need to deduce useful patterns from injury data and test hypotheses regarding causation and prevention.

Injury now surpasses disease as the leading global cause of premature death and disability, claiming over 5.8 millions lives each year. However, unlike disease, which has been subjected to a rigorous epidemiologic approach, the field of injury prevention and control has been a relative newcomer to scientific investigation. With the distribution of injury now well described (i.e. ???who???, ???what???, ???where??? and ???when???), the underlying hypothesis is that progress in understanding ???how??? and ???why??? lies in classifying injury occurrences aetiologically. The advancement of a means of classifying injury aetiology has so far been inhibited by two related limitations: 1. Structural limitation: The absence of a cohesive and validated aetiological taxonomy for injury, and; 2. Methodological limitation: The need to manually classify large numbers of injury cases to determine aetiological patterns. This work is directed at overcoming these impediments to injury research. An aetiological taxonomy for injury was developed consistent with epidemiologic principles, along with clear conventions and a defined three-tier hierarchical structure. Validation testing revealed that the taxonomy could be applied with a high degree of accuracy (coder/gold standard agreement was 92.5-95.0%), and with high inter- and intra- coder reliability (93.0-96.3% and 93.5-96.3%). Practical application demonstrated the emergence of strong aetiological patterns which provided insight into causative sequences leading to injury, and led to the identification of effective control measures to reduce injury frequency and severity. However, limitations related to the inefficient and error-prone manual classification process (i.e. average 4.75 minute/case processing time and 5.0-7.5% error rate), revealed the need for an automated approach. To overcome these limitations, a knowledge acquisition (KA) software tool was developed, tested and applied, based on an expertsystems technique known as ripple down rules (RDR). It was found that the KA system was able acquire tacit knowledge from a human expert and apply learned rules to efficiently and accurately classify large numbers of injury cases. Ultimately, coding error rates dropped to 3.1%, which, along with an average 2.50 minute processing time, compared favourably with results from manual classification. As such, the developed taxonomy and KA tool offer significant advantages to injury researchers who have a need to deduce useful patterns from injury data and test hypotheses regarding causation and prevention.

A thesis submitted to the Faculty of Health Sciences, University of the Witwatersrand, in fulfillment of the requirements for the degree of Doctor of Philosophy (PhD) in Bioethics and Health Law Johannesburg, 2019 / Road traffic injuries and deaths (RTID) are a global public health crisis affecting the ethically charged road traffic system, and disproportionately affect the poor. By world standards South Africa has extremely high crash rates and in many respects is failing to apply road safety best practice, despite being a signatory to the UN Decade of Action for Road Safety 20112020. In the economic hub of Johannesburg the minibus taxi industry (MTI) is a dominant mode of paratransit (informal public transport) which offers flexible and affordable services and helps reduce the social divide caused by the lingering spatial realities of apartheid. It is also a source of economic empowerment and much-needed jobs – however, as with paratransit systems elsewhere, unsafe driving is common and many of the taxis are elderly or defective. Frequent MTI crashes contribute to Johannesburg’s road deaths being more than triple the international city average. Members of the public tend to vilify MTI drivers and ascribe a high degree of moral responsibility to them, but this intuitive reasoning seems to disregard their work conditions and how these affect their driving behavior. It also fails to take into account the South African road safety status quo and the possibility that MTI drivers are akin to an indicator species in relation to the ills of our road traffic system. Prevailing views of road safety are shaped by the Vision Zero philosophy and the Safe System approach, which assign responsibilities both to road users and to system designers. In line with this, my study addresses the question of what moral responsibilities should be ascribed, and to whom, in relation to reducing RTID in the Johannesburg MTI. I answer this bioethical question by means of a dual descriptive-normative inquiry. My descriptive inquiry is based on my mixed-methods empirical research with drivers, aimed at addressing the dearth of knowledge of their work conditions and tapping their views on crash causation and road safety responsibilities. My results, viewed against the backdrop of road safety best practice, lead me to label the operating principles of the Johannesburg MTI ‘contra-constitutional’ due to their violating the drivers’ labour rights as well as the human rights of drivers, passengers and other road users alike. I also analyse the South African road safety situation with regards to road safety best practice and comparative information from three groups of reference countries: the BRICS, our African neighbours (and two other African countries with similar paratransit), and several aspirational countries with very low RTID. This analysis leads me to develop the term ‘crashogenic’ to describe our road traffic system. My normative inquiry draws on arguments which have been made by other authors focusing on moral considerations in relation to road safety. It applies Nihlen Falquist’s moral responsibility ascription framework – developed with regards to Sweden’s Vision Zero policy – in a novel fashion, employing graphical representation in addition to narrative reasoning. Thus, I use her three categories of blame responsibility, causal responsibility and forwardlooking responsibility and ascribe specific moral responsibilities to identified rolepayers, with a view to reducing RTID in the Johannesburg MTI. My study makes an original contribution to the bioethical debate on road safety, with a unique South African perspective. It also extends the existing knowledge base regarding drivers’ work conditions in paratransit systems. / MT 2019

Sparse information is available concerning use of emergency departments (EDs) by the elderly in rural areas. We reviewed records of all patients seeking care at EDs of three rural hospitals during 7 days in October 1991. We found that elderly people did not use EDs in proportion to their numbers in the community (15.2% versus 19.3%). Compared with younger ED patients, more elderly patients required an ambulance (40.8% versus 10.7%), more needed hospitalization (38.4% versus 11.9%), and their ED stays were longer (140 minutes versus 89 minutes). Falls/injuries (18.7%) and cardiac illness (18.1%) were the most frequent reasons for ED visits by the elderly, and relatively few (2.8%) had confusion. More elderly patients arrived during daytime hours than during the night, and more on weekends than weekdays. Also, we found no difference between patients in the 65- to 74-year-old age group and those aged 75 years and older.