Mental or physical disorders shorten people’s life spans. Yet, previous attempts at assessing life expectancy of people with disorders have failed at addressing the fact that diseases occur at different ages. This project resolves this methodological shortcoming and develops precision metrics, which will be used to determine trends of mental health or physical disorders that affect Australians. By analysing national linkage data, we will identify gaps in longevity between those diagnosed and the general population. The outcomes of this project will inform people with disorders, healthcare providers and decision-makers regarding exact life expectancies and, thereby, generate information on diseases, which ought to be addressed more comprehensively. Thus, all levels of the health system need supporting to improve quality of life for those suffering from health disorders. The findings from this project will provide critical insights into the recent gains and losses in Australian life expectancy of populations diagnosed disorders; which will help in establishing priorities for coming public health efforts.
Vladimir Canudas-Romo is internationally known for his expertise in mathematical demography and his research on the longevity revolution. He has made major contributions to the demographic debates on alternative measures of longevity and the development of decomposition methods to better understand these issues. He has received funding for his work from: i) the European Research Council, ii) The USA insurance company, AIG, and iii) the Australian Research Council. More recently, he has advised on mortality-forecasting methods the Social Security Administration in the USA, the World Bank and the Australian Government Actuary at Treasury. Canudas-Romo is thus in a position to communicate demographic perspectives on important public topics involving length of life to decision makers and the actuarial industry. Canudas-Romo’s international experience is extensive having worked both in Europe and the USA, before moving to the School of Demography at the Australian National University in 2017.
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The 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 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 just at 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.
This project is with Alice Richardson, from the ANU Statistical Consulting Unit (previously of the National Centre of Epidemiology and Public Health).
Bernard Baffour is currently a Senior Lecturer at the School of Demography, and completed his PhD in social statistics from the University of Southampton, and was at the University of Queensland before joining the ANU in 2017. His main research interests are (a) understanding and modelling demographic change in Australia (specifically on overcoming data limitations due to sparse and inconsistent data), and (b) population estimation, census and survey methodology (particularly in improving the design and analysis of censuses and surveys to cope with non-response and measurement error).