The utility of survey and administrative data to generate information for research and outcomes-based oral health services development

The aim of this research, which focused on the Irish adult population, was to generate information for policymakers by applying statistical analyses and current technologies to oral health administrative and survey databases. Objectives included identifying socio-demographic influences on oral health and utilisation of dental services, comparing epidemiologically-estimated dental treatment need with treatment provided, and investigating the potential of a dental administrative database to provide information on utilisation of services and the volume and types of treatment provided over time. Information was extracted from the claims databases for the Dental Treatment Benefit Scheme (DTBS) for employed adults and the Dental Treatment Services Scheme (DTSS) for less-well-off adults, the National Surveys of Adult Oral Health, and the 2007 Survey of Lifestyle Attitudes and Nutrition in Ireland. Factors associated with utilisation and retention of natural teeth were analysed using count data models and logistic regression. The chi-square test and the student’s t-test were used to compare epidemiologically-estimated need in a representative sample of adults with treatment provided. Differences were found in dental care utilisation and tooth retention by Socio-Economic Status. An analysis of the five-year utilisation behaviour of a 2003 cohort of DTBS dental attendees revealed that age and being female were positively associated with visiting annually and number of treatments. Number of adults using the DTBS increased, and mean number of treatments per patient decreased, between 1997 and 2008. As a percentage of overall treatments, restorations, dentures, and extractions decreased, while prophylaxis increased. Differences were found between epidemiologically-estimated treatment need and treatment provided for those using the DTBS and DTSS. This research confirms the utility of survey and administrative data to generate knowledge for policymakers. Public administrative databases have not been designed for research purposes, but they have the potential to provide a wealth of knowledge on treatments provided and utilisation patterns.

Profiling side-channel attacks on cryptographic algorithms

Traditionally, attacks on cryptographic algorithms looked for mathematical weaknesses in the underlying structure of a cipher. Side-channel attacks, however, look to extract secret key information based on the leakage from the device on which the cipher is implemented, be it smart-card, microprocessor, dedicated hardware or personal computer. Attacks based on the power consumption, electromagnetic emanations and execution time have all been practically demonstrated on a range of devices to reveal partial secret-key information from which the full key can be reconstructed. The focus of this thesis is power analysis, more specifically a class of attacks known as profiling attacks. These attacks assume a potential attacker has access to, or can control, an identical device to that which is under attack, which allows him to profile the power consumption of operations or data flow during encryption. This assumes a stronger adversary than traditional non-profiling attacks such as differential or correlation power analysis, however the ability to model a device allows templates to be used post-profiling to extract key information from many different target devices using the power consumption of very few encryptions. This allows an adversary to overcome protocols intended to prevent secret key recovery by restricting the number of available traces. In this thesis a detailed investigation of template attacks is conducted, along with how the selection of various attack parameters practically affect the efficiency of the secret key recovery, as well as examining the underlying assumption of profiling attacks in that the power consumption of one device can be used to extract secret keys from another. Trace only attacks, where the corresponding plaintext or ciphertext data is unavailable, are then investigated against both symmetric and asymmetric algorithms with the goal of key recovery from a single trace. This allows an adversary to bypass many of the currently proposed countermeasures, particularly in the asymmetric domain. An investigation into machine-learning methods for side-channel analysis as an alternative to template or stochastic methods is also conducted, with support vector machines, logistic regression and neural networks investigated from a side-channel viewpoint. Both binary and multi-class classification attack scenarios are examined in order to explore the relative strengths of each algorithm. Finally these machine-learning based alternatives are empirically compared with template attacks, with their respective merits examined with regards to attack efficiency.

Private health care coverage and increased risk of obstetric intervention

Background: When clinically indicated, common obstetric interventions can greatly improve maternal and neonatal outcomes. However, variation in intervention rates suggests that obstetric practice may not be solely driven by case criteria. Methods: Differences in obstetric intervention rates by private and public status in Ireland were examined using nationally representative hospital discharge data. A retrospective cohort study was performed on childbirth hospitalisations occurring between 2005 and 2010. Multivariate logistic regression analysis with correction for the relative risk was conducted to determine the risk of obstetric intervention (caesarean delivery, operative vaginal delivery, induction of labour or episiotomy) by private or public status while adjusting for obstetric risk factors. Results: 403,642 childbirth hospitalisations were reviewed; approximately one-third of maternities (30.2%) were booked privately. After controlling for relevant obstetric risk factors, women with private coverage were more likely to have an elective caesarean delivery (RR: 1.48; 95% CI: 1.45-1.51), an emergency caesarean delivery (RR: 1.13; 95% CI: 1.12-1.16) and an operative vaginal delivery (RR: 1.25; 95% CI: 1.22-1.27). Compared to women with public coverage who had a vaginal delivery, women with private coverage were 40% more likely to have an episiotomy (RR: 1.40; 95% CI: 1.38-1.43). Conclusions: Irrespective of obstetric risk factors, women who opted for private maternity care were significantly more likely to have an obstetric intervention. To better understand both clinical and non-clinical dynamics, future studies of examining health care coverage status and obstetric intervention would ideally apply mixed-method techniques.