AusDiab 3: Emerging Risk Factors For And Long-term Incidence Of Cardio-metabolic Diseases
Funder
National Health and Medical Research Council
Funding Amount
$2,616,397.00
Summary
This study will track 11,000 Australian adults over 12 years to determine how many develop diabetes, obesity, kidney and heart disease. The study will develop ways to best predict those who are going to develop these conditions before they have arisen, and will explore a range of novel risk factors to better understand these conditions.
Associations Between Urban Nature And Cardiovascular Disease Risk
Funder
National Health and Medical Research Council
Funding Amount
$318,768.00
Summary
Cardiovascular disease (CVD) is the leading cause of death in Australia. Urban nature (e.g. greenness, water, species diversity) is likely to protect against CVD, yet researchers lack knowledge about how this occurs. This project will develop new methods to measure urban nature and examine the relationships with different CVD risk factors (e.g. physical activity, air quality). The results of this project will inform urban planning policy, and help to create healthy cities that reduce CVD.
Novel Retinal Architectural Vascular Signs And Risk Of Cardiovascular Disease: The AusDiab Study
Funder
National Health and Medical Research Council
Funding Amount
$754,254.00
Summary
Cardiovascular disease (CVD) and diabetes are major health problems. Identifying 'people at risk' is critical to design preventative strategies. We have developed new computer software to measure detailed characteristics of retinal vessels. By appling this system to predict CVD or diabetes in the AusDiab Study we aim to find 'the best combination of risk factors' to predict CVD and diabetes. This will open up the possibility of new risk assessment using a simple 'eye scan.'
Advanced Bayesian Networks for Epidemiology. We will demonstrate the potential of advanced Artificial Intelligence for medical informatics by extending the capabilities of Bayesian Networks. Bayesian Networks excel when researchers need to combine causal and diagnostic reasoning in areas characterised by uncertainty. But they have one flaw which hinders their use: they do not yet easily mix continuous and discrete variables. We will extend them to handle such mixes, then demonstrate how much the ....Advanced Bayesian Networks for Epidemiology. We will demonstrate the potential of advanced Artificial Intelligence for medical informatics by extending the capabilities of Bayesian Networks. Bayesian Networks excel when researchers need to combine causal and diagnostic reasoning in areas characterised by uncertainty. But they have one flaw which hinders their use: they do not yet easily mix continuous and discrete variables. We will extend them to handle such mixes, then demonstrate how much they can improve on current methods for predicting, among other things, coronary heart disease (CHD).Read moreRead less
Epidemiological modelling of cardiovascular disease and diabetes in Australia. With Australia's population ageing and becoming increasingly obese, cardiovascular diseases and diabetes are predicted to be a massive burden on our already stretched health system. Preventing the onset of disease is clearly the best management strategy, but we also need effective treatment strategies for those with these diseases, and we need to ensure that we are spending our healthcare dollars in the most effectiv ....Epidemiological modelling of cardiovascular disease and diabetes in Australia. With Australia's population ageing and becoming increasingly obese, cardiovascular diseases and diabetes are predicted to be a massive burden on our already stretched health system. Preventing the onset of disease is clearly the best management strategy, but we also need effective treatment strategies for those with these diseases, and we need to ensure that we are spending our healthcare dollars in the most effective and cost-effective manner to achieve these aims. This research will evaluate how best to do this in a specifically Australian context.Read moreRead less