The geographical structures available for the latest Social Health Atlases of Australia and Aboriginal and Torres Strait Islander Social Health Atlas are detailed below. Data released by pre-ASGS data structures are available in the Data Archive
Population Health Areas (PHAs) are comprised of a combination of whole SA2s and multiple (aggregates of) SA2s, where the SA2 is an area in the ABS structure.
Australian Statistical Geographical Standard (ASGS)
For further information, refer to the Population Health Areas: Overview.
To find the code and/or name of a PHA, refer to the full list of Population Health Areas 2021 (including Statistical Areas Level 2 (SA2)). In addition, to search for a suburb and determine which PHAs this suburb falls into and what percentage of the suburb's population is in a PHA, refer to the suburb to PHA correspondence for 2021, 2016 and 2011 (xls).
For shapefiles of PHAs, refer to the PHA shape files.
The Local Government Areas (LGAs) presented in the PHIDU Social Health Atlases are based on the Australian Statistical Geography Standard (ASGS): Edition 3 - Non ABS Structures, July 2021.
LGAs are an Australian Bureau of Statistics (ABS) approximation of officially gazetted LGAs as defined by each State and Territory Local Government Department. LGAs cover incorporated areas of Australia. For further information regarding the LGA structure, refer to the ABS information at: Australian Statistical Geography Standard (ASGS): Edition 3 - Non ABS Structures, July 2021.
Caveat on LGA data quality
The data presented by Local Government Area (LGA) have in the majority of cases, been estimated from the postcode or Statistical Areas Level 2 of the address, as the LGA of usual residence was not more accurately geocoded (e.g., from the street address). Estimates produced in this way are of variable quality, as the allocation from a postcode to one or more LGAs is based on the proportion of the total population falling within LGAs covered by the postcode. The proportional allocations are as providedby the Australian Bureau of Statistics (ABS)
This method does not account for variations in the proportion of a postcode varying between different constituent LGAs or parts of LGAs according to different population characteristics such as age groups or Indigenous status, or the location of facilities which can alter the proportion (e.g., a nursing home in part of the postcode will not be recognised by this method).
Cherbourg in Queensland provides an example of the poor quality of outcomes that can result from this process. Under the ABS allocation, Cherbourg is allocated 26.2% of the population of postcode 4605. However, almost three quarters (72.4%) of the Aboriginal and Torres Strait Islander population of postcode 4605 is estimated to live in Cherbourg and not in either of the other two LGAs allocated population from the postcode. As a result of this approach, the Australian Childhood Immunisation Register (ACIR) recorded 14 children at age 1 year in 2017, 12 of whom were immunised. Using data more accurately geocoded by ABS, there were 46 births in Cherbourg, indicating a substantial shortfall in the data in the ACIR.
The quality indicators of LGA estimates produced by the ABS include Good (348 LGAs), Acceptable (126 LGAs) or Poor (70 LGAs) as listed here. The definitions for these quality indicators are as follows:
PHIDU is in contact with data custodians to obtain better quality data for LGAs.
[1]All Population Census data and data for deaths (as presented for premature and avoidable mortality) are geocoded more accurately, as are the data for some records for some states for hospital admissions, perinatal data, cancer incidence and cancer screening. Data for childhood immunisations, HPV vaccinations and income support payments have been produced using ABS correspondence files and are therefore less accurate.
Primary Health Networks (PHNs) comprise 31 primary health care organisations across Australia. For further information, including digital boundary and concordance files, refer to the Department of Health Primary Health Networks information.
Background
The Quintiles of Socioeconomic Disadvantage of Area data for the whole population, presented as Inequality graphs, are based on the ABS Index of Relative Socio-economic Disadvantage 2016. The Inequality graphs for the Aboriginal and Torres Strait Islander population were produced as described below but using the Indigenous Relative Socioeconomic Outcomes index (IRSEO), an Indigenous-specific index derived by the Centre for Aboriginal Economic Policy Research (CAEPR) from the 2016 Census of Population and Housing.
Calculation of quintiles
To calculate rates by socioeconomic status, Population Health Areas (PHAs1) were ranked based on the Index of Relative Socio-economic Disadvantage score from the 2016 Census. The listing of PHAs was then divided into five groups with each group comprised of approximately 20% of the population in Australia. The data for each indicator in this atlas were then allocated (at the PHA level) to the appropriate group and the rate for that indicator calculated for each of the five groups; the groups are referred to as quintiles of socioeconomic disadvantage of area, reflecting the area-based measure on which they are based. This exercise was repeated for PHAs in each State and Territory, each Greater Capital City and each Rest of State/Territory area.
Measures
There are two measures discussed with respect to the quintiles; one is the variation across the quintiles (referred to as the social gradient), the other is the difference in rates between the most and the least disadvantaged areas (measured by the rate ratio).
To describe the magnitude of variation between the most disadvantaged (Quintile 5) and least disadvantaged (Quintile 1) quintile, a rate ratio is calculated for each indicator. A rate ratio of 1 indicates that the rate in the least and most disadvantaged quintiles is the same. A rate ratio greater than 1 shows there is a higher rate in the most disadvantaged quintile, e.g., a rate ratio of 2 would indicate there is double the activity in the most disadvantaged compared to the least disadvantaged quintile. Rate ratios that are greater than or less than 1 are indicative that there may be some inequality in access to services across population groups, or in early death when looked at by level of disadvantage. See Figure 1, below, for an example of a rate ratio.
It is also important, and informative, to note that variations typically occur across all the quintiles, and that in many cases, there is a social gradient, a variation in the data that shows percentages or rates indicating poorer outcomes across the socioeconomic spectrum. This social gradient is often described as being ‘stepwise’, with, for example, a higher premature mortality rate in each subsequent quintile, as in Figure 1, below.
Source: https://phidu.torrens.edu.au/current/graphs/sha-aust/quintiles/aust/premature-mortality-sex.html
Quintiles of Socioeconomic Disadvantage of Area over time
A separate module presents data in this way for indicators which are available over time. The IRSD scores for each Census were used to produce the quintiles for the Census year and the two years before and the two years after that Census year. So, the 2001 IRSD score was used to produce quintiles for 1999, 2000, 2001, 2002 and 2003. The exceptions were the 1986 IRSD, which was applied to the years 1987 and 1988, and the 2016 IRSD, which was applied to the years 2014 to 2017. A limitation of this approach using the IRSD, is that the Australian Bureau of Statistics, who produce the IRSD, undertake a principal components analysis following each Census, with some change in variables in the analysis. However, we are not aware of a more robust process for making this comparison.
To access the Quintiles of Socioeconomic Disadvantage of Area over time data - see Time Series
Quintiles of Socioeconomic Disadvantage of Area within Primary Health Networks
The Quintiles of Socioeconomic Disadvantage of Area data for the whole population, presented as Inequality graphs, are based on the ABS Index of Relative Socio-economic Disadvantage.
To calculate rates by socioeconomic status within Primary Health Networks (PHNs), Statistical Areas Level 2 (SA2s) or Population Health Areas (PHAs1) were ranked based on the Index of Relative Socio-economic Disadvantage score from the 2016 Census, within their PHN. The listing was then divided into five groups with each group comprised of approximately 20% of the PHN's population. The data for each indicator in this atlas were then allocated (at the SA2 or PHA level) to the appropriate group and the rate for that indicator calculated for each of the five groups; the groups are referred to as quintiles of socioeconomic disadvantage, reflecting the measure on which they are based.
In addition to showing the variation between the quintiles for each indicator in a chart, a rate ratio is given to describe the magnitude of variation between the most disadvantaged (Quintile 5) and least disadvantaged (Quintile 1) quintile for each indicator. A rate ratio of 1 indicates that the rate in the least and most disadvantaged quintiles is the same. A rate ratio greater than 1 shows there is a higher rate in the most disadvantaged quintile, e.g., a rate ratio of 2 would indicate there is double the activity in the most disadvantaged compared to the least disadvantaged quintile. Rate ratios that are greater than or less than 1 are indicative that there may be some inequality in access to services across population groups, or in early death when looked at by level of disadvantage. However, it is also important, and informative, to note that variations occur across all the quintiles: in many cases there is a social gradient, a variation in the data that runs from top to bottom of the socioeconomic spectrum.
1Prior to 2011, quintiles of socioeconomic disadvantage of area were based on Statistical Local Areas.
The Remoteness graphs and associated data are based on the ABS Remoteness Structure.
The Australian Bureau of Statistics' (ABS) Australian Statistical Geography Standard (ASGS): Volume 5 - Remoteness Structure1 is a framework for statistical geography, which defines locations in terms of remoteness2. Geographic remoteness is essentially a measure of a physical location's level of access to goods and services3. Large population centres tend to have a greater range of goods and services available than small centres.
The measures of remoteness used by the ABS are based on population estimates obtained from the Census of Population and Housing, conducted every five years. Remoteness measures are calculated using Accessibility/Remoteness Index of Australia (ARIA+) scores, which are based on the distance of geographic locations from the nearest population centre in various size ranges. The lower the ARIA+ score for a location, the better its level of access to goods and services.3
Box 1: Classification of Remoteness Areas in Australia
The ABS Australian Statistical Geography Standard (ASGS) Remoteness Structure allocates areas to one of six Remoteness Areas depending on their distance from urban centres, where the population size of the urban centre is considered to govern the range and types of services available. Remoteness Areas used in PHIDU reports cover the following five categories: Major Cities of Australia, Inner Regional Australia, Outer Regional Australia, Remote Australia and Very Remote Australia. The sixth Remoteness Area covers populations in areas recorded as off-shore, migratory and shipping and is not of relevance to the data in these reports.
The category Major Cities includes Australia’s capital cities, with the exceptions of Hobart and Darwin, which are classified as Inner Regional and Outer Regional, respectively.
From April 2018 data are being coded to Remoteness Areas released by the ABS in March 2018 and are presented under the `latest’ period heading.
In the Time Series set, data coded to classifications for 2006 and earlier have been re-compiled to match the 2011 Remoteness Area, and later data have been re-coded to those 2011 areas.
Readers should note that the presentation of data by Remoteness Area is dependent on the recording of addresses in the various administrative data collections from which data in this report are drawn.
1Australian Bureau of Statistics (ABS). Australian Statistical Geography Standard (ASGS): Volume 5 - Remoteness Structure, July 2011. (ABS Cat. no. 1270.055.005). Canberra: ABS; 2013
2Australian Population and Migration Research Centre (APMRC). ARIA (Accessibility/Remoteness Index of Australia), 2013. [cited 2016 April 1]. Available from: http://www.adelaide.edu.au/apmrc/research/projects/category/about_aria.html
3Australian Institute of Health and Welfare (AIHW). Remoteness classification (ASGS-RA) N – METeOR. [Internet; cited 2016 March 31]. Available from: http://meteor.aihw.gov.au/content/index.phtml/itemId/531713/meteorItemView/long
Regional Centres are based on the following criteria set out in the Centre for Aboriginal Economic Policy Research's (CAEPR) 2011 Census Paper Series – Paper 12 Regional Centres:
There are 46 Regional Centres across Australia (based on 2016 Census) data.
Indigenous Areas (IARE), based on the ABS 2021 ASGS Indigenous Structure, are medium sized geographical units designed to facilitate the release and analysis of more detailed statistics for Aboriginal and Torres Strait Islander Peoples. There are 430 IARE presented by the ABS 2021 ASGS Indigenous Areas (IARE).
Indigenous Regions (IREG), based on the ABS 2021 ASGS Indigenous Structure, are large geographical units loosely based on the former Aboriginal and Torres Strait Islander Commission boundaries. They are created by combining together one or more Indigenous Areas. There are 58 IREG presented by the ABS 2021 ASGS Indigenous Regions (IREG).
The data presented by quintile of Indigenous Relative Socioeconomic Outcomes, available on the website as Inequality graphs, are based on the Indigenous Relative Socioeconomic Outcomes index (IRSEO), an Indigenous-specific index derived by the Centre for Aboriginal Economic Policy Research (CAEPR) from the 2016 Census of Population and Housing.
To calculate rates by quintile, Indigenous Areas (IAERs) were ranked based on the Indigenous Relative Socioeconomic Outcomes index score (2016). The ranked listing was then divided into five groups with each group comprised of approximately 20% of the Aboriginal and Torres Strait Islander population in Australia. The data for each indicator in this atlas were then allocated (at the IARE level) to the appropriate group and the rate for that indicator calculated for each of the five groups, or quintiles. This exercise was repeated for IAERs in each State and the Northern Territory.
In addition to showing the variation between the quintiles for each indicator in a chart, a rate ratio is given to describe the magnitude of variation between the most disadvantaged and least disadvantaged quintile for each indicator. A rate ratio of 1 indicates that the rate in the least and most disadvantaged quintiles is the same. A rate ratio greater than 1 shows there is a higher rate in the most disadvantaged quintile, e.g., a rate ratio of 2 would indicate there is double the activity in the most disadvantaged compared to the least disadvantaged quintile. Rate ratios that are greater than or less than 1 is indicative that there may be some inequality in access to services across population groups, or in early death when looked at by level of disadvantage. However, it is also important, and informative, to note that variations occur across all the quintiles: in many cases there is a social gradient, a variation in the data that runs from top to bottom of the socioeconomic spectrum.
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