Medicine (RMH) - Research Publications
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ItemNo Preview AvailableThe Oversight of Clinical Innovation in a Medical Marketplace(Cambridge University Press, 2021-06-24)
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ItemNo Preview AvailableModelling the Economic Impacts of Epidemics in Developing Countries Under Alternative Intervention Strategies(Springer, 2020)Modern levels of global travel have intensified the risk of new infectious diseases becoming pandemics. Particularly at risk are developing countries whose health systems may be less well equipped to detect quickly and respond effectively to the importation of new infectious diseases. This chapter examines what might have been the economic consequences if the 2014 West African Ebola epidemic had been imported to a small Asia-Pacific country. Hypothetical outbreaks in two countries were modelled. The post-importation estimations were carried out with two linked models: a stochastic disease transmission (SEIR) model and a quarterly version of the multi-country GTAP model, GTAP-Q. The SEIR model provided daily estimates of the number of persons in various disease states. For each intervention strategy the stochastic disease model was run 500 times to obtain the probability distribution of disease outcomes. Typical daily country outcomes for both controlled and uncontrolled outbreaks under five alternative intervention strategies were converted to quarterly disease-state results, which in turn were used in the estimation of GTAP-Q shocks to country-specific health costs and labour productivity during the outbreak, and permanent reductions in each country’s population and labour force due to mortality. Estimated behavioural consequences (severe reductions in international tourism and crowd avoidance) formed further shocks. The GTAP-Q simulations revealed very large economic costs for each country if they experienced an uncontrolled Ebola outbreak, and considerable economic costs for controlled outbreaks in Fiji due to the importance of the tourism sector to its economy. A major finding was that purely reactive strategies had virtually no effect on preventing uncontrolled outbreaks, but pre-emptive strategies substantially reduced the proportion of uncontrolled outbreaks, and in turn the economic and social costs.
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ItemSex steroids and gender differences in muscle, bone, and fat(Elsevier, 2022)Sex steroids, comprising of the androgens, estrogens, and progestogens, are fundamentally important to the development of muscle, bone, and fat across the life course. Each has roles that differ between these tissues, the male and female sexes, and developmental stage. It is the differential production of sex steroids and expression of their receptors that mediates much of the pubertal development in muscle, bone, and fat, which in turn determines the typical dimorphic sexual phenotypes. It is similar to how this differential production changes over time that is responsible for much of the typical sex-specific changes seen with normal aging. This chapter considers the sex-specific production of sex steroids and their effects upon each muscle, bone, and fat. It additionally covers the developmental changes in sex steroid production, and how this contributes to age-related changes in these three tissues.
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ItemAutomated inter-ctal epileptiform discharge detection from routine EEG(IOS Press, 2021-04-19)Epilepsy is the most common neurological disorder. The diagnosis commonly requires manual visual electroencephalogram (EEG) analysis which is time-consuming. Deep learning has shown promising performance in detecting interictal epileptiform discharges (IED) and may improve the quality of epilepsy monitoring. However, most of the datasets in the literature are small (n≤100) and collected from single clinical centre, limiting the generalization across different devices and settings. To better automate IED detection, we cross-evaluated a Resnet architecture on 2 sets of routine EEG recordings from patients with idiopathic generalized epilepsy collected at the Alfred Health Hospital and Royal Melbourne Hospital (RMH). We split these EEG recordings into 2s windows with or without IED and evaluated different model variants in terms of how well they classified these windows. The results from our experiment showed that the architecture generalized well across different datasets with an AUC score of 0.894 (95% CI, 0.881–0.907) when trained on Alfred’s dataset and tested on RMH’s dataset, and 0.857 (95% CI, 0.847–0.867) vice versa. In addition, we compared our best model variant with Persyst and observed that the model was comparable.