NHMRC funding granted for population health project

This problem of sparse data has been regarded as intractable in the past; for example the confidence intervals associated with smoking prevalence in up to 40% of remote regions are suppressed due to unacceptably high standard errors. This means that regions miss out on the evidence of what works in their community. These are typically regions that are coping with multiple levels of disadvantage, for example higher levels of social exclusion and higher levels of risky behaviour such as smoking.

After a review of the state of the art of small area estimation, we will build a multilevel statistical model for the estimation of smoking prevalence in small areas across the whole of Australia. This will lead to mapping the prevalence of smoking prevalence with increased precision in sparsely sampled regions of Australia. A third part of the project will evaluate the impacts of interventions implemented at the small-area level (rather than a State or national) level on smoking prevalence over time.

While tobacco smoking is declining on average over time on both Indigenous and non-Indigenous populations, the prevalence is spatially diverse and distributed unevenly throughout the population. This variation makes monitoring the situation particularly difficult. Yet there is a tremendous demand for such information to ensure proper planning and resource allocation. This is particularly true for regions where policy interventions have taken place with little ability to use data to evaluate their success, and little ability to identify areas of best practice.

Research Team

Alice Richardson

Bernard Baffour

Susanna Cramb

Petra Kuhnert

Stephen Haslett

Ian Rayson, ABS

Mu Li

Five people sitting around a table, smiling at the camera.

Members of the SPARSE research team at lunch following a seminar by Professor Michel Guillot, University of Pennsylvannia. L-R: Michel Guillot, Bernard Baffour, Collin Payne, Susanna Cramb, Alice Richardson.

Mr Mu Li commenced his PhD studies on this project in August 2020. His project, ‘Two-Way Bivariate Outcomes Small Area Estimation using Spatial Bayesian Hierarchical Models’ aims to make several contributions to the field of small area estimation.

Mu’s Self Introduction:
My broad research interest resolves around small area estimation by spatial models, which focuses on multivariate outcomes distributional estimation by Bayesian hierarchical models and the m-quantile method. It is my great interest to reveal the cause and effect of smoking prevalence across Australia by the tool of statistics.
From 2017 to 2020, I tutored and demonstrated undergraduate mathematics at MSI in ANU. I also took the role of research assistant, located in the Statistical Consulting Unit, in February 2020 where I contributed to construct and implement a conditional autoregressive Bayesian model for narrowing down deviations of the estimation of Australian early childhood development. In August 2020, I joined in the School of Demography as a PhD candidate.
It is my honour to work in the SPARSE research team and pay my efforts to help with population health.