Technical notes

Australian Bureau of Statistics data

This report uses data from the following surveys conducted by the Australian Bureau of Statistics (ABS):

  • 2022 National Health Survey (NHS)
  • 2018–19 National Aboriginal and Torres Strait Islander Health Survey (NATSIHS)
  • 2017–18 NHS
  • 2014–15 NHS
  • 2012–13 Australian Aboriginal and Torres Strait Islander Health Survey (AATSIHS)
  • 2011–12 Australian Health Survey (AHS)
  • 2007–08 NHS
  • 1995 National Nutrition Survey (NNS).

These data sources were chosen because they provide nationally representative measured height and weight data. Please note, the most recent 2020–21 NHS was conducted during the COVID-19 pandemic. To ensure the safety of interviewers and respondents, there were no face-to-face interviews, and no measured height and weight data was collected. Only self-reported height and weight data was collected but these data usually underestimate the measured height and weight. For this reason, the 2020–21 NHS was not used to report on trends on overweight or obesity. For more information, please refer to the ABS release on Self-reported height and weight and the ABS National Health Survey: First results methodology.

Information about the surveys, including data quality statements, is available on the ABS website.

The scope of these surveys was restricted to residents of private dwellings, and excluded residents of non-private dwellings such as hospitals, nursing homes, hotels, motels, boarding schools, and prisons.

The 2022 NHS, 2017–18 NHS, 2014–15 NHS, 2011–12 AHS, 2007–08 NHS and 1995 NNS excluded people living in Very remote areas of Australia and discrete First Nations communities.

The 2018–19 NATSIHS and 2012–13 AATSIHS only collected information from people who identified as Aboriginal or Torres Strait Islander. These surveys included people living in non-remote and remote areas, including discrete First Nations communities.

All of these surveys (except the 2007–08 NHS) included measured height and weight data for people aged 2 and over. The 2007–08 NHS included these data for people aged 5 and over.

Each survey included the collection of measured height and weight by trained interviewers. The tools used for measuring height and weight varied over time, and in particular this changed the maximum weight that could be measured. For example, the 1995 NNS used scales that could weigh a maximum weight of 140 kg. However, the 2007–08 NHS used scales that could weigh a maximum weight of 150 kg, and the 2017–18 NHS used scales that could weigh a maximum weight of 200 kg.

The response rates for physical measures varied between surveys with decreasing response rates over time. The ABS imputed BMI for those people for whom BMI was not measured in the 2014–15 NHS, 2017–18 NHS and ABS 2022 NHS, and the 2018–19 NATSIHS. In this method, participants with a missing response were given the response of similar participants.

A very similar imputation method was used for the 2017–18 NHS and the 2018–19 NATSIHS, but this method was different for adults and children. For adults, the similarity of participants was based on age group, sex, part of state, self-perceived body mass, level of exercise, whether or not a participant had high cholesterol as a long-term health condition, and self-reported BMI category (calculated from self-reported height and weight) (ABS 2018, 2019). For 2–14-year-olds, the similarity was based on age group, sex, self-reported BMI and part of state, while for 15–17-year-olds, level of exercise and self-perceived body mass (only if a person answered for themselves) were also used.

The imputation method for the 2014–15 NHS was similar to the 2017–18 NHS and 2018–19 NATSIHS, except it did not use self-reported BMI (ABS 2015).

There was no imputation of BMI in the 1995 NNS, 2007–08 NHS, 2011–12 AHS and 2012–13 AATSIHS and participants without a measured BMI were excluded from analysis.

Primary Health Networks

This report includes the proportion of adults aged 18 years and over who were classified as overweight or obese, by Primary Health Network (PHN). PHNs are local organisations that connect health services across a specific geographic area, with the boundaries defined by the Australian Government Department of Health.

Proportions have been age standardised to the 2001 Australian population to account for differences in the age structure of the population in different areas. Results are presented in Table S9 as both crude and age-standardised rates.

The quality of estimates from the NHS can vary across PHN areas, as the survey was not specifically designed to produce estimates at this level of geography. Table S9 includes 95% confidence intervals, as an indication of the reliability of the proportions.

Proportions that have a margin of error that is 10 percentage points or greater have been indicated and these should be used with caution due to the wide confidence interval.

Data for the Northern Territory should be interpreted with caution as the 2022 NHS excluded Very Remote areas.

Remoteness areas

This report uses the remoteness areas from the 2021 Australian Statistical Geography Standard (ABS 2023a). The national health surveys exclude Very Remote Australia so these are not included in results in this publication.

Due to low sample size, Remote Australia has been combined with Outer Regional Australia for results presented by remoteness areas.

Socioeconomic areas

Information on socioeconomic areas in this report is based on Socio-Economic Indexes for Areas (SEIFA) 2021, a product developed by the ABS that ranks areas in Australia according to relative socioeconomic advantage and disadvantage. The indexes are based on information from the five-yearly Census. Each index is a summary of a different subset of Census variables and focuses on a different aspect of socioeconomic advantage and disadvantage (ABS 2023b).

This report uses the Index of Relative Socio-economic Disadvantage (IRSD) from 2021, based on the Statistical Area Level 1 (SA1) that each household was within. Areas were ranked and put into 5 equally sized groups based on the IRSD score of these SA1s; these form the socioeconomic areas used in this report. The 20% of areas living with the greatest overall level of disadvantage are described as living in the lowest socioeconomic areas. The 20% of areas at the other end of the scale – those living in areas with the least overall level of disadvantage – are described as living in the highest socioeconomic areas.



Crude and age-standardised prevalence estimates are presented as percentages in this report. Crude prevalence, as a percentage, is defined as the number of people with a particular characteristic, divided by the number of people in the population of interest, multiplied by 100.

In calculating crude prevalence estimates, those people for whom BMI was not available were excluded from the denominator. For the 2014–15 NHS, 2017–18 NHS, 2022 NHS and the 2018–19 NATSIHS, imputed data were used for those people for whom BMI had not been measured.

All prevalence estimates in this report are weighted estimates that use person weights allocated to each survey participant by the ABS.

The jack-knife weight replication method was used to derive the standard error (SE) for each estimate, using replicate weights provided by the ABS.

The statistical significance of any difference in prevalence (percentage) estimates between people across time or population groups (e.g. between age groups, socioeconomic quintile, or sex) was assessed using z scores or 95% confidence intervals.

Age-standardised estimates

Age-standardised prevalence estimates are presented to remove the influence of age when comparing populations with different age structures. This is necessary because rates of overweight and obesity vary (usually increasing) with age.

The age-standardised proportions in this report have been directly age-standardised to the 2001 Australian standard population.

Measuring overweight and obesity

For children and adolescents, age- and sex-specific half-year BMI cut-off points were used to classify overweight and obesity (Cole et al. 2000).

For adults:

  • overweight and obesity was classified as a BMI of 25.00 kg/m² or more
  • obesity was classified as a BMI of 30.00 kg/m² or more (WHO 2000).

Relative standard error, margin of error and confidence intervals

The relative standard error (RSE) of an estimate is a measure of the error likely to have occurred due to sampling. The RSEs of the estimates were calculated using the standard errors (SEs):

RSE%25 = SE(estimate) / estimate * 100

The margin of error (MoE) at the 95% confidence level for each estimate was calculated using 1.96 as the critical value:

MoE = 1.96 * SE(estimate)

The MoE was then used to calculate the 95% confidence interval (CI) around each estimate:

95%25 CI = estimate +- MoE(estimate)

The 95% CI is a range of values determined by the variability in data, within which there is a 95% chance that the confidence interval will contain the true value of the population quantity being estimated.

Significance testing

Variation or difference in observed values or rates may be due to a number of causes including, among other things, actual differences in the study’s populations and sampling error. A statistical test of significance indicates how incompatible the observed data are with a specified statistical model. To assess whether differences between estimates are incompatible with a null hypothesis that the survey estimates are normally distributed and that there is no difference between the groups being compared, 95% CIs were used.

A difference between estimates was considered statistically significant if the 95% CIs around the estimates did not overlap. Where there was an overlap between 95% CIs, a 95% CI for the difference between estimates was calculated. To do this, the SE of the difference was approximated by:

SE = sqrt(SE(estimate1)^2 + SE(estimate2)^2)

The 95% CI for the difference between estimates was then calculated as:

95%25 CI = (estimate1 – estimate2) +- (1.96 * SE(estimate1 – estimate2))

If the 95% CI for the difference between estimates included 0, then the difference was not statistically significant. If it excluded 0, then the difference was considered to be statistically significant.