Statistics and Indicators

BMI, healthy weight, children

    Chart

    Healthy weight BMI category, 5 - 17 years, ACT General Health Survey, 2011 - 2021

    Body Mass Index (BMI) is a simple index of weight-for-height that is commonly used to classify underweight, healthy weight, overweight and obesity. 

    BMI for children are categorised as underweight, healthy weight, overweight or obese according to the international cut-offs for children developed by the International Obesity Taskforce: https://pubmed.ncbi.nlm.nih.gov/22715120/

    Between 2020 and 2021, the proportion of respondents to the ACT General Health Survey aged 5-17 years who were a healthy weight has remained stable (2020: 62.6%; 2021: 59.6%). In 2021, there was no significant difference by sex for those reporting that they were a healthy weight (males: 60.9%; females: 58.2%).  

    BMI is based on self-reported height and weight. To calculate BMI, weight in kilograms was divided by the square of height in metres.

    For the purpose of reporting the ACT General Health Survey data on HealthStats, if the 95% confidence intervals of the estimates do not overlap, they are considered to be significantly different.

    Note: The indicator shows self-reported data collected through Computer Assisted Telephone Interviewing (CATI). Estimates were weighted to adjust for differences in the probability of selection among respondents and were benchmarked to the estimated residential population using the latest available Australian Bureau of Statistics population estimates.

    Responses for children aged 5-15 years were provided by the parent/carer who knows the most about the child's health. Persons includes male, female, other and refused sex respondents and may not always add to the sum of male and female.

    Statistically significant differences are difficult to detect for smaller jurisdictions such as the Australian Capital Territory. Sometimes, even large apparent differences may not be statistically significant. This is particularly the case in breakdowns of small populations because the small sample size means that there is not enough power to identify even large differences as statistically significant.

    To access the data please click on the "View source data" link at the bottom of the visualisation. This link will open up a data table that you can download.