Notes on the data: Chronic diseases and conditions

Estimated population with diabetes mellitus, 2017–18

 

Policy context:  Diabetes mellitus is a chronic disease characterised by high blood glucose levels resulting from defective insulin production, insulin action or both [1]. There are a number of different forms of diabetes, which can cause a number of serious complications, especially cardiovascular, eye and renal diseases, of which type 2 diabetes is the most prevalent.

Aboriginal and Torres Strait Islander peoples and others who are socioeconomically disadvantaged are at higher risk of developing diabetes mellitus, and have much greater hospitalisation and death rates from diabetes than other Australians.

Around one in twenty (5.1%) Australians aged 18 years and over had diabetes according to a fasting plasma glucose test and self-reported information collected by the Australian Bureau of Statistics in 2011-12. This comprised 4.2% with known diabetes and 0.9% with diabetes newly diagnosed from their test results. This indicates that there was approximately one newly diagnosed case of diabetes for every four diagnosed cases. A further 3.1% of adults had impaired fasting plasma glucose results, which indicates that they were at high risk of diabetes. This means that there were an extra three people at high risk of diabetes for every four people who had been diagnosed with diabetes [2].

In 2017-18, one in twenty Australians (4.9% or 1.2 million people) had diabetes. Since 2001, this rate has increased from 3.3%, however, has remained relatively stable since 2014-15 (5.1%). Diabetes continued to be more common among males than females (5.5% and 4.3% respectively). The prevalence of diabetes has increased for both males and females since 2001 (both 3.3%) [2].

References

  1. World Health Organization (WHO). Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: Diagnosis and classification of diabetes mellitus. Geneva: Department of Noncommunicable Disease Surveillance, WHO; 1999.
  2. Australian Bureau of Statistics (ABS). Diabetes mellitus. National Health Survey: First Results, 2017-18. Available from: https://www.abs.gov.au/ausstats/abs@.nsf/Lookup/by%20Subject/4364.0.55.001~2017-18~Main%20Features~Diabetes%20mellitus~50; last accessed 15 October 2019.
 

Notes:  

Change in measure:

In 2007–08 and 2014–15 PHIDU published data for type 2 diabetes, whereas in this atlas the data are for all forms of diabetes.

Differences from data published by the Australian Bureau of Statistics (ABS):

Data by quintile of socioeconomic disadvantage and Remoteness will differ to the extent that data extracted from Survey TableBuilder have been randomised, whereas those published by the ABS are not. In addition, rates published by the ABS for modelled estimates are generally crude rates; rates published by PHIDU are age-standardised.

Modelled Estimates:

In the absence of data from administrative data sets, estimates are provided for selected health risk factors from the 2017–18 National Health Survey (NHS), conducted by the Australian Bureau of Statistics (ABS). Details of the method used and accuracy of results are available from the ABS paper Explanatory Notes: Modelled estimates for small areas based on the 2017-18 National Health Survey, available here.

Estimates at the Population Health Area (PHA) level are modelled estimates produced by the ABS, as described below (estimates at the Local Government Area (LGA) and Primary Health Network (PHN) level were derived from the PHA estimates).

For the Primary Health Network (PHN) data, differences between the PHN totals and the sum of LGAs within PHNs result from the use of different geographic correspondence files.

Estimates for quintile of socioeconomic disadvantage of area and Remoteness Area are direct estimates, extracted using the ABS Survey TableBuilder.

Users of these modelled estimates should note that they do not represent data collected in administrative or other data sets. As such, they should be used with caution, and treated as indicative of the likely social dimensions present in an area with these demographic and socioeconomic characteristics.

The numbers are estimates for an area, not measured events as are, for example, death statistics. As such, they should be viewed as a tool that, when used in conjunction with local area knowledge and taking into consideration the prediction reliability, can provide useful information that can assist with decision making for small geographic regions. Of particular note is that the true value of the published estimates is also likely to vary within a range of values as shown by the upper and lower limits published in the data (xlsx) and viewable in the column chart in the single map atlases.

What the modelled estimates do achieve, however, is to summarise the various demographic, socioeconomic and administrative information available for an area in a way that indicates the expected level of each health indicator for an area with those characteristics. In the absence of accurate, localised information about the health indicator, such predictions can usefully contribute to policy and program development, service planning and other decision-making processes that require an indication of the geographic distribution of the health indicator.

The relatively high survey response rate in the NHS provides a high level of coverage across the population; however, the response rate among some groups is lower than among other groups, e.g., those living in the most disadvantaged areas have a lower response rate than those living in less disadvantaged areas. Although the sample includes the majority of people living in households in private dwellings, it excludes those living in the very remote areas of Australia and discrete Aboriginal and Torres Strait Islander communities; whereas these areas comprise less than 3% of the total population, Aboriginal people comprise up to one third of the population. The survey does not include persons usually resident in non-private dwellings (hospitals, gaols, nursing homes - and also excludes members of the armed forces).

This and other limitations of the method mean that estimates have not been published for PHAs with populations under 1,000.

The ABS used a number of methods to measure the quality of the estimates, one of which is the relative root mean squared error (RRMSE) of the modelled estimates. The RRMSEs are included with the data. Users are advised that:

  • estimates with RRMSEs less than 25% are considered reliable for most purposes;
  • estimates with RRMSEs from 0.25 and to 0.50 have been marked (~) to indicate that they should be used with caution; and
  • those greater than 0.50 but less than 1 are marked (~~) to indicate that the estimate is considered too unreliable for general use.

Indicator detail

These data refers to persons who self-reported having been told by a doctor or nurse that they had diabetes mellitus, irrespective of whether the person considered their diabetes to be current or long-term.

 

Geography: Data available by Population Health Area, Local Government Area, Primary Health Network, quintile of socioeconomic disadvantage of area and Remoteness Area

 

Numerator:  Estimated number of people with diabetes mellitus

 

Denominator:  Total population

 

Detail of analysis:  Indirectly age-standardised rate per 100 population; and/or indirectly age-standardised ratio, based on the Australian standard

 

Source:  

PHA, LGA & PHN: Age-standardised rates are based on Australian Bureau of Statistics data, produced as a consultancy for PHIDU, from the 2017—18 National Health Survey.

Quintiles & Remoteness: Compiled by PHIDU based on direct estimates from the 2017–18 National Health Survey, ABS Survey TableBuilder.

 

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