Mapping the prevalence of smoking with increased precision in sparsely sampled regions of Australia

  • 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

    Sumonkanti Das

    Members of the SPARSE research team welcome Sumon on his first day on the ANU campus. L-R: Mu Li, Alice Richardson, Bernard Baffour, Sumon Das

    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.

    Dr. Sumonkanti Das (pictured above) has recently joined the SPARSE team in 2021.

    Sumonkanti’s Introduction:
    Sumonkanti Das completed his PhD in statistics from the University of Wollongong. He did his PhD in the area of small area estimation (SAE) focusing on robust inference in poverty mapping with a particular interest in developing countries like Bangladesh. After his PhD, he worked as a post-doctoral researcher at the Quantitative Economics department within the School of Business and Economics at Maastricht University, the Netherlands, during the period of 2018-2021. He contributed to several joint research projects of Maastricht University and the Statistics Netherlands (CBS), where he extensively worked on multilevel time series modelling to estimate mobility trends for small domains of the Dutch population by accounting for survey redesigns over the period 1999-2019. Before joining the SPARSE research team, he was also affiliated as a faculty member of the Department of Statistics, Shahjalal University of Science and Technology in Bangladesh. Mr. Das has an immense interest in implementation of SAE methodology in the field of population health and nutrition. Thus, the scope of working in the SPARSE project might open up a new window in his research career.