Notes on the data: Health risk factors
Estimated female population, aged 18 years and over, who were obese, 2011-12
Policy context: Being obese has significant health, social and economic impacts, and is closely related to lack of exercise and to diet . Obesity increases the risk of suffering from a range of health conditions, including coronary heart disease, type 2 diabetes, some cancers, knee and hip problems, and sleep apnoea . In 2011-12, more than one in four adult Australians were obese . Rates of obesity were the same for men and women (both 27.5%). The proportion of people who are obese has increased across all age groups over time, up from 18.7% in 1995 to 27.5% in 2011-12 .
- Australian Bureau of Statistics (ABS). Measures of Australia’s progress, 2010. (ABS Cat. no. 1370.0). Canberra: ABS; 2010.
- ABS. Profiles of health, Australia. (ABS Cat. no. 4338.0). Canberra: ABS; 2013.
Notes: In the absence of data from administrative data sets, estimates are provided for certain chronic diseases and conditions and health risk factors from the 2011–12 Australian Health Survey (AHS), conducted by the Australian Bureau of Statistics (ABS).
Estimates at the Population Health Area (PHA), Local Government Area (LGA) and Primary Health Network (PHN) level are modelled estimates and were produced at the PHA level by the ABS, as described below.
Estimates for Quintiles and Remoteness Areas are direct estimates from the 2011–12 Australian Health Survey (AHS), extracted using the ABS Survey TableBuilder, and standardised using the average of the ABS Estimated Resident Population, 30 June 2011 and 30 June 2012.
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 (xls) and viewable in the bar 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 AHS' response rate of around 85% provides a high level of coverage across the population; however, the response rate among some groups, e.g., those living in the most disadvantaged areas, is lower than among those in less disadvantaged areas. Although the sample includes the majority of people living in households in private dwellings, it excludes those living in the most remote areas of Australia; whereas these areas comprise less than 3% of the total population, Aboriginal people comprise up to one third of the population in these areas. This and other limitations of the method mean that estimates have not been published for PHAs with populations under 1,000, or with a high proportion of their population in:
- non-private dwellings (hospitals, gaols, nursing homes - and also excludes members of the armed forces);
- in Very Remote areas;
- in discrete Aboriginal communities; and
- where the relative root mean square errors (RRMSEs) on the estimates was 1 or more (estimate replaced with ≠)
- 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.
- 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 concordances.
The Body Mass Index (BMI) (or Quetelet's index) is a measure of relative weight based on an individual's mass and height. The height (cm) and weight (kg) of respondents, as measured during the AHS interview, were used to calculate the BMI, and obesity was determined where a person’s BMI was 30 or greater. The BMI is a useful tool at a population level for measuring trends in body weight, and helping to define population groups who are at higher risk of developing long-term medical conditions associated with a high BMI, such as type 2 diabetes and cardiovascular disease.
Note that the modelled estimates are based on the 84.3% of persons 18 years and over in the sample who had their height and weight measured.
Numerator: Estimated number of females aged 18 years and over who were assessed as being obese, based on their measured height and weight
Denominator: Female population aged 18 years and over
Detail of analysis: Indirectly age-standardised rate per 100 females (aged 18 years and over); and/or indirectly age-standardised ratio, based on the Australian standard
PHA, LGA & PHN: Compiled by PHIDU based on modelled estimates from the 2011-12 Australian Health Survey, ABS (unpublished); and the average of the ABS Estimated Resident Population, 30 June 2011 and 30 June 2012, based on the Australian standard.
Quintiles & Remoteness: Compiled by PHIDU based on direct estimates from the 2011-12 Australian Health Survey, ABS Survey TableBuilder; and standardised using the average of the ABS Estimated Resident Population, 30 June 2011 and 30 June 2012.