4364.0.55.011 - Australian Health Survey: Consumption of added sugars, 2011-12  
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1 This publication is the fifth release of nutrition data from the 2011-12 National Nutrition and Physical Activity Survey (NNPAS). The first release was published in May 2014. The statistics presented in this publication are only a selection of the nutrition information collected from the NNPAS.

2 The 2011-12 NNPAS was conducted throughout Australia from May 2011 to June 2012. The NNPAS was collected as one of a suite of surveys conducted from 2011-2013, called the Australian Health Survey (AHS).

3 The Australian Health Survey: Consumption of Added Sugar publication contains usual (long term) added and free sugar intake information modelled from two days of 24-hour dietary recall data using the National Cancer Institute (NCI) method. For more information on the NCI method, see the Overview of the NCI Method chapter of the AHS: Users' Guide, 2011-13 (cat. no. 4363.0.55.001).

4 Usual intakes of added and free sugars are provided by age groups and sex at the national level, including comparison with the World Health Organisation (WHO) recommended intake of free sugars. More information on WHO recommendation of free sugars is available on the Sugars Intake for adults and children website.


5 The National Nutrition and Physical Activity Survey (NNPAS) contains a sample of approximately 9,500 private dwellings across Australia.

6 Urban and rural areas in all states and territories were included, while Very Remote areas of Australia and discrete Aboriginal and Torres Strait Islander communities (and the remainder of the Collection Districts in which these communities were located) were excluded. These exclusions are unlikely to affect national estimates, and will only have a minor effect on aggregate estimates produced for individual states and territories, excepting the Northern Territory where the population living in Very Remote areas accounts for around 23% of persons.

7 Non-private dwellings such as hotels, motels, hospitals, nursing homes and short-stay caravan parks were excluded from the survey. This may affect estimates of the number of people with some chronic health conditions (for example, conditions which may require periods of hospitalisation).

8 Within each selected dwelling, one adult (aged 18 years and over) and, where possible, one child (aged 2 years and over) were randomly selected for inclusion in the survey. Sub-sampling within households enabled more information to be collected from each respondent than would have been possible had all usual residents of selected dwellings been included in the survey.

9 The following groups were excluded from the survey:

    • certain diplomatic personnel of overseas governments, customarily excluded from the Census and estimated resident population
    • persons whose usual place of residence was outside Australia
    • members of non-Australian Defence Forces (and their dependents) stationed in Australia
    • visitors to private dwellings.


10 Trained ABS interviewers conducted personal interviews with selected residents in sampled dwellings. One person aged 18 years and over in each dwelling was selected and interviewed about their own health characteristics including a 24-hour dietary recall and a physical activity module. An adult, nominated by the household, was interviewed about one child (aged two years and over) in the household. Selected children aged 15-17 years may have been personally interviewed with parental consent. An adult, nominated by the household, was also asked to provide information about the household, such as the combined income of other household members. Children aged 6-14 years were encouraged to be involved in the survey, particularly for the 24-hour dietary recall and physical activity module. For further information, see Data Collection in the AHS: Users' Guide, 2011-13 (cat. no. 4363.0.55.001).

11 All selected persons were required to have a follow-up phone interview at least eight days after the face to face interview to collect a further 24-hour dietary recall. For those who participated, pedometer data was reported during this telephone interview.


12 Dwellings were selected at random using a multistage area sample of private dwellings for the NNPAS.

The initial sample selected for the survey consisted of approximately 14,400 dwellings. This was reduced to approximately 12,400 dwellings after sample loss (for example, households selected in the survey which had no residents in scope of the survey, vacant or derelict buildings, or buildings under construction). Of those remaining dwellings, 9,519 (or 77.0%) were fully or adequately responding, yielding a total sample for the survey of 12,153 persons (aged two years and over).



Households approached (after sample loss)
2 227
1 983
1 988
1 551
1 545
1 155
1 006
12 366
Households in sample
1 666
1 371
1 525
1 211
1 334
1 003
9 519
Response rate (%)
Persons in sample
2 139
1 749
1 964
1 526
1 706
1 245
1 061
12 153

13 Of the 12,153 people in the final sample, 98% provided the first (Day 1), with the missing 2% of Day 1 dietary recalls being imputed. The second 24-hour dietary recall (Day 2) had 7,735 participants (64% of the total). The Day 2 24-hour dietary recall participation was slightly higher among older respondents, and sex did not appear as a factor in participation.

14 More information on response rates and imputation is provided in the AHS: Users' Guide, 2011-13 (cat. no. 4363.0.55.001).

15 To take account of possible seasonal effects on health and nutrition characteristics, the NNPAS sample was spread randomly across a 12-month enumeration period. Between August and September 2011, survey enumeration was suspended due to field work associated with the 2011 Census of Population and Housing.


16 Weighting is a process of adjusting results from a sample survey to infer results for the in-scope total population. To do this, a weight is allocated to each sample unit; for example, a household or a person. The weight is a value which indicates how many population units are represented by the sample unit.

17 The first step in calculating weights for each person was to assign an initial weight, which was equal to the inverse of the probability of being selected in the survey. For example, if the probability of a person being selected in the survey was 1 in 600, then the person would have an initial weight of 600 (that is, they represent 600 others). An adjustment was then made to these initial weights to account for the time period in which a person was assigned to be enumerated.

18 The weights are calibrated to align with independent estimates of the population of interest, referred to as 'benchmarks', in designated categories of sex by age by area of usual residence. Weights calibrated against population benchmarks compensate for over or under-enumeration of particular categories of persons and ensure that the survey estimates conform to the independently estimated distribution of the population by age, sex and area of usual residence, rather than to the distribution within the sample itself.

19 The NNPAS was benchmarked to the estimated resident population living in private dwellings in non-Very Remote areas of Australia at 31 October 2011. Excluded from these benchmarks were persons living in discrete Aboriginal and Torres Strait Islander communities, as well as a small number of persons living within Collection Districts that include discrete Aboriginal and Torres Strait Islander communities. The benchmarks, and hence the estimates from the survey, do not (and are not intended to) match estimates of the total Australian resident population (which include persons living in Very Remote areas or in non-private dwellings, such as hotels) obtained from other sources. For the NNPAS, a seasonal adjustment was also incorporated into the person weights.

20 Survey estimates of counts of persons are obtained by summing the weights of persons with the characteristic of interest. Estimates of non-person counts (for example, number of organised physical activities) are obtained by multiplying the characteristic of interest with the weight of the reporting person and aggregating.


21 All sample surveys are subject to sampling and non-sampling error. Estimates derived from models, including the NCI method, are also subject to prediction error and simulation variance.

22 Sampling error is the difference between estimates, derived from a sample of persons, and the value that would have been produced if all persons in scope of the survey had been included. For more information refer to the Technical note. Indication of the level of sampling error is given by the 95% Margin of Error (MoE).

23 In this publication, MoEs are provided for all estimates (unless noted otherwise) to assist users in assessing the reliability of these types of estimate. The estimate combined with the MoE defines a range which is expected to include the true population value with a 95% level of confidence. This is known as the 95% confidence interval. This range should be considered by users to inform decisions based on the estimate.

24 Non-sampling error may occur in any data collection, whether it is based on a sample or a full count such as a census. Non-sampling errors occur when survey processes work less effectively than intended. Sources of non-sampling error include non-response, errors in reporting by respondents or in recording of answers by interviewers, and occasional errors in coding and processing data.

25 Prediction error and simulation variance are forms of error which may occur when using a model such as the NCI method. Care was taken to ensure the input 24-hour dietary recall data was suitable for use in the model. Every effort is made to ensure an appropriate model specification is used through external literature research and statistical testing. For more information see Data Quality in the Users' Guide.

26 Where comparisons with WHO recommended intake have been made, any error in these guideline values will affect the quality of the resulting estimates. The WHO recommends both adults and children to reduce their intake of free sugars to less than 10% of total energy intake.

27 Of particular importance to nutrition surveys is a widely observed tendency for people to under-report their food intake. This can include:
    • actual changes in foods eaten because people know they will be participating in the survey
    • misrepresentation (deliberate, unconscious or accidental), e.g. to make their diets appear more ‘healthy’ or be quicker to report.

Analysis of the 2011-12 NNPAS suggests that, like other nutrition surveys, there has been some under-reporting of food intake by participants in this survey. Given the association of under-reporting with overweight/obesity and consciousness of socially acceptable/desirable dietary patterns, under-reporting is unlikely to affect all foods and nutrients equally. No respondents were excluded from the sample on the basis of low total reported energy intakes (low energy reporters were included in the input data set for usual nutrient intakes). For more information see Under-reporting in Nutrition Surveys in the AHS Users' Guide, 2011-13.

28 Another factor affecting the accuracy of the 24-hour dietary recall data is that most young children are unable to recall their intakes. Similarly, parents/carers of school-aged children may not be aware of a child’s total food intake, which can lead to systematic under-reporting. Young children were encouraged to assist in answering the dietary recall questions. See the Interviews section of Data Collection for more information on proxy use in the 24-hour dietary recall module.

29 Another source of non-sampling error specific to nutrition surveys is the accuracy of the nutrient and measures database containing thousands of foods used to derive the nutrient estimates. The databases used for the 2011-12 NNPAS were developed by Food Standards Australia New Zealand (FSANZ) specifically for the survey. A complete nutrient profile of 51 nutrients was created based on FSANZ’s latest available data and from this, intakes of 44 nutrients were reported in the NNPAS at the time when the survey results were first released. Not all data was based on directly analysed foods; some data was borrowed from overseas food composition tables, food label information, imputed from similar foods, or calculated using a recipe approach. See AUSNUT 2011-13 for more information.

30 Non-response occurs when people cannot or will not cooperate, or cannot be contacted. Non-response can affect the reliability of results and can introduce bias. The magnitude of any bias depends on the rate of non-response and the extent of the difference between the characteristics of those people who responded to the survey and those who did not.

31 The following methods were adopted to reduce the level and impact of non-response:
    • face-to-face interviews with respondents
    • the use of interviewers, where possible, who could speak languages other than English
    • follow-up of respondents if there was initially no response
    • weighting to population benchmarks to reduce non-response bias.

32 By careful design and testing of the questionnaire, training of interviewers, and extensive editing and quality control procedures at all stages of data collection and processing, other non-sampling error has been minimised. However, the information recorded in the survey is essentially 'as reported' by respondents, and hence may differ from information collected using different methodology.


33 There are three NCI model forms that can be applied: one-part, correlated two-part and uncorrelated-two part (see Model Implementation: Data used and Model Specification in the User’s Guide for more information on model forms).The one-part model was used when less than 5% of intakes had zero amounts. It was therefore used to model usual intake of added and free sugars as these nutrients were consumed nearly every day by almost everyone. Accordingly, percentage of energy from free sugars was also modelled using the one-part model form.

34 Percentage of energy from free sugars is a distribution of usual ratios. For this publication, these ratios have been first calculated on an individual basis as the total percentage of energy coming from free sugars on the intake day, divided by the total energy intake for the day. This was done for both Day 1 and Day 2 for each respondent. The NCI method was then run on these ratios in a similar way to other food groups. The group usual distribution of these ratios, output from the NCI method, are therefore usual ratio of intakes, that is the usual daily intake ratio. For more information, see Modelling Ratio, in the User’s Guide.

35 In the NCI method, covariates are data items or variables that describe characteristics of the individuals within a group, which are relevant to their nutrient or food intake. Four covariates were used in this publication for all models: sex, age, weekend vs weekday, and sequence effect. The purpose of each of these covariates is outlined in Model Implementation: Data used and Model Specification in the User’s Guide.


36 The Census and Statistics Act, 1905 provides the authority for the ABS to collect statistical information, and requires that statistical output shall not be published or disseminated in a manner that is likely to enable the identification of a particular person or organisation. This requirement means that the ABS must take care and make assurances that any statistical information about individual respondents cannot be derived from published data.


37 Estimates presented in this publication have been rounded. As a result, sums of components may not add exactly to totals.

38 All statistics are rounded to one decimal place in the data cubes.


39 ABS publications draw extensively on information provided freely by individuals, businesses, governments and other organisations. Their continued cooperation is very much appreciated; without it, the wide range of statistics published by the ABS would not be available. Information received by the ABS is treated in strict confidence as required by the Census and Statistics Act, 1905.

40 The ABS gratefully acknowledges and thanks the Agricultural Research Service of the USDA for giving permission to adapt and use their Dietary Intake Data System including the AMPM for collecting dietary intake information as well as other processing systems and associated materials.

41 FSANZ and the ABS jointly investigated and validated the use of the NCI method with the 2011-12 NNPAS. FSANZ was contracted to provide advice throughout the survey development, processing, and collection phases of the 2011-12 NNPAS, and to provide a nutrient database for the coding of foods and dietary supplements consumed. The ABS would like to acknowledge and thank FSANZ for providing their support, advice and expertise to the 2011-12 NNPAS.

42 The ABS gratefully acknowledges and thanks researchers at the National Cancer Institute (NCI) in the USA and elsewhere for developing and making available the NCI method and corresponding SAS macros, and providing expert advice on the use of the method.


43 Summary results from this survey are available in spreadsheet form from the 'Downloads' tab in this release.

44 Because the NCI method produces estimates of usual added and free sugar intakes for population sub-groups and not individuals, usual intake data is not available at the unit record level.

45 Summary tables containing aggregated estimates of the prevalence of intakes above the WHO recommended intake level are available in the ‘Downloads’ tab in this release. Information on how to aggregate estimates for different age and sex groups is in Summary Tables in the Users' Guide.


46 Other ABS publications which may be of interest are shown under the 'Related Information' tab of this release.

47 Current publications and other products released by the ABS are listed on the ABS website. The ABS also issues a daily Release Advice on the website which details products to be released in the week ahead.