Discovery Early Career Researcher Award - Grant ID: DE150100309
Funder
Australian Research Council
Funding Amount
$328,614.00
Summary
Understanding the Dynamics of Socioeconomic Related Health Inequalities. Health differences across socio-economic groups have persisted in many countries, including Australia, despite decades of considerable improvements in life expectancy and average health status. Little is known of how policies may influence socio-economic health inequalities as the mechanisms underlying them are complex and the causes differ across population groups and over the lifecycle. This project aims to develop method ....Understanding the Dynamics of Socioeconomic Related Health Inequalities. Health differences across socio-economic groups have persisted in many countries, including Australia, despite decades of considerable improvements in life expectancy and average health status. Little is known of how policies may influence socio-economic health inequalities as the mechanisms underlying them are complex and the causes differ across population groups and over the lifecycle. This project aims to develop methods to quantify the major mechanisms that give rise to changes in socio-economic health inequalities in Australia. This project aims to improve our understanding of the dynamic factors that drive changes in health inequalities, thus providing useful information for decision makers about which policies will be cost effective at reducing them.Read moreRead less
Flexible methods for latent variable models applied to Health Economics. This project aims to develop flexible and powerful methods for estimating models containing variables that are unobserved, that is, latent. Such models are often used to capture individual heterogeneity and time dependence in data collected on individuals, with each individual observed for several time periods. Latent variables can also infer group membership, where such membership is unavailable from the data. The intended ....Flexible methods for latent variable models applied to Health Economics. This project aims to develop flexible and powerful methods for estimating models containing variables that are unobserved, that is, latent. Such models are often used to capture individual heterogeneity and time dependence in data collected on individuals, with each individual observed for several time periods. Latent variables can also infer group membership, where such membership is unavailable from the data. The intended methodology is Bayesian and based on new particle methods that allow users to select between models and predict future observations even in complex situations. The research aims to inform decision making through improved use of data in health economics and related fields.Read moreRead less