2902.0 - Census Update (Newsletter), Nov 2003  
Previous ISSUE Released at 11:30 AM (CANBERRA TIME) 28/11/2003   
   Page tools: Print Print Page Print all pages in this productPrint All RSS Feed RSS Bookmark and Share Search this Product

SEIFA Case Studies

Economic Modelling

The National Centre for Social and Economic Modelling (NATSEM) at the University of Canberra is a leader in microsimulation modelling and microdata analysis. A focus of their work is building models of the social security, tax and health systems to examine the impact of social policies. In the last few years, NATSEM has been developing regional modelling techniques to allow for analysis of the spatial impacts of social and economic policies and developments. The regional focus to their work has led them to use SEIFA indexes for a number of projects.

Three SEIFA indexes were used on a recent project for a government department assessing the socio-economic attractiveness of various regions around Australia to particular demographic profiles of interest.

NATSEM has also undertaken a number of regional analyses for Centrelink, using sources of socio-economic data such as the SEIFA Index of Disadvantage to provide context about the population characteristics of areas.

In a project examining the characteristics of hospital users in NSW, the SEIFA Index of Disadvantage was used to add socio-economic status to hospital patients based on the Collection District in which they lived. The SEIFA Index was found to be beneficial in the analysis of the socio-economic characteristics of hospital users.

Social Research

SEIFA provides a measure of socio-economic disadvantage that is extremely useful in social research.

Dr James Doughney of the Work and Economic Policy Research Unit at Victoria University has used SEIFA in his research into the economic and social impact of poker machines on Victorian communities.

Combining research showing the average poker machine losses per adult in Victorian Local Government Areas with the SEIFA Index of Disadvantage, Dr Doughney found a significant relationship between socio-economic disadvantage and poker machine losses.

According to this research, the most disadvantaged Local Government Areas (LGAs) also had the highest poker machine losses and poker machine venues were located in Census Collection Districts with lower average SEIFA rankings than LGAs overall.

Reasoning that the more disadvantaged communities are those that can least afford these losses, Dr Doughney has developed a "Pokie Loss Severity Index", which divides poker machine losses per head by the Index of Disadvantage.

Dr Doughney hopes that the Victorian Government will take something akin to this approximate indicator of community impact into account when determining regional caps on the number of poker machines.

"The government needs to do more than merely spread the number of machines equally across municipalities if it truly wants to even out the severity of poker machine impacts," Dr Doughney claims.

"To be fair, regional caps must reduce losses so as to equalise the severity index and minimise the pokie burden on communities that can least afford it."

Research such as this demonstrates the ability of SEIFA to be used in the investigation of contemporary social issues.

Health Research

The health field is one of the most useful areas for the application of SEIFA. Many causes of illness and death are strongly related to socio-economic status, and SEIFA provides a mechanism for analysing these relationships. Health interventions can then be targeted to particular population groups.

One useful summary measure of population health is life expectancy. Using ABS mortality statistics, researchers from the Victorian Burden of Disease Study in the Health Surveillance and Evaluation Section of the Victorian Department of Human Services were able to create estimates of life expectancy for males and females in Victoria by Local Government Area (LGA).

Using the SEIFA Index of Disadvantage, the researchers examined the correlation between the socio-economic conditions in Victorian Local Government Areas and the average life expectancy in those areas. They found that low socio-economic status was a relatively strong predictor of early mortality.

The Victorian Burden of Disease Study also used the SEIFA Index of Disadvantage to examine the relationship between socio-economic status and years of life lost due to specific causes.

The study found that years of life lost to diabetes, cardiovascular disease, road traffic accidents and lung cancer were all significantly higher in the more disadvantaged areas.

More information on the use of SEIFA and the distribution of morbidity across Victoria is available at a searchable on-line database of LGA burden of disease estimates, www.dhs.vic.gov.au/phd/bod.