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Hysterectomy, Oophorectomy And Long-term Chronic Disease - The HOLD Study
Funder
National Health and Medical Research Council
Funding Amount
$690,006.00
Summary
Hysterectomy, with or without the removal of ovaries, undertaken for non-cancerous problems may have long-term consequences for other health conditions like cardiovascular disease and cancer, but existing evidence is inconsistent. This large population-based study will use linked health data from the states and the Commonwealth to investigate these associations. The information from our study will help women and their doctors to make the better-informed decisions about their treatment.
Identifying Unintentional Effects Of Medication Using Statistical Genetics Analyses Of Large-scale Genetic And Genomic Data
Funder
National Health and Medical Research Council
Funding Amount
$251,441.00
Summary
An increasing number of studies have highlighted unknown adverse effects of medication, for example, use of statins to lower cholesterol with increased risk of type 2 diabetes. The gold standard approach to confirm these effects is randomised control trials, which may not always be feasible or ethical, and are very expensive. This project aims to apply innovative statistical genetics approaches to (genetic and genomic) 'big-data' to predict unknown effects of commonly prescribed medications.
Better Statistical Methods To Discover Host Genetic Factors In Symptom Response To SARS-CoV-2 Infection
Funder
National Health and Medical Research Council
Funding Amount
$290,137.00
Summary
The COVID-19 pandemic has infected >5 million people worldwide. While the majority of infected individuals recover within a few weeks of infection, others develop severe forms, that in some cases prove fatal. To date, the causes of differences in symptom response are unknown. In this proposal, we seek to discover genetic factors that can contribute to explaining these differences. Our findings have the potential to inform the design and analysis of clinical trials for vaccines and treatments.
Novel Modelling To Improve Decision-making For Neighbourhood Design To Reduce Chronic Disease Risk
Funder
National Health and Medical Research Council
Funding Amount
$901,564.00
Summary
Research on urban design that might support liveability and health (the 20-minute neighbourhood concept) has used analytic methods that do not account for the complexity of urban environments. This study innovatively uses a flexible and applicable approach (Bayesian Networks) to show where neighbourhood features operate uniquely or not, which features can be prioritised, which are cost effective, and how much of each feature is needed to achieve improvement in reducing risk of chronic disease.