Australian Health Survey: Nutrition - Supplements methodology

Latest release
Reference period
2011-12 financial year
Next release Unknown
First release

Explanatory notes


1 This publication presents a selection of results from the 2011-12 National Nutrition and Physical Activity Survey (NNPAS), with the focus on the use of dietary supplements and their contribution to nutritional status. The information is provided by age groups and sex at the national level.

2 The 2011-12 NNPAS was conducted throughout Australia from May 2011 to June 2012. The NNPAS was collected as part of the Australian Health Survey (AHS) conducted from 2011-2013.

3 The Australian Health Survey: Nutrition - Supplements publication contains selected food and nutrient information from a 24-hour dietary recall, based on a single day's intake (Day 1). No adjustments have been made using the second day of 24-hour dietary recall information. This publication complements data in Australian Health Survey: Usual Nutrient Intakes, 2011-12 (cat. no. 4363.0.55.008) which represents persons' usual nutrient intakes.

4 The statistics presented in this publication are only a selection of the information collected in the NNPAS. Further publications from the Australian Health Survey are outlined in the Release Schedule, while the list of data items currently available from the survey are available in the Australian Health Survey: Users' Guide, 2011-13 (cat. no. 4363.0.55.001).

Scope of the survey

5 The National Nutrition and Physical Activity Survey 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.

Data collection

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 2 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 Australian Health Survey: 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 8 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.

Survey design

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, 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 2 years and over).

National Nutrition and Physical Activity Survey 2011-12, approached sample, final sample and response rates

Households approached (after sample loss)2 2271 9831 9881 5511 5451 1559111 00612 366
Households in sample1 6661 3711 5251 2111 3341 0035928179 519
Response rate (%)74.869.176.778.186.386.865.081.277.0
Persons in sample2 1391 7491 9641 5261 7061 2457631 06112 153

13 Of the 12,153 people in the final sample, 98% provided dietary recall information for the day before the first interview (Day 1), with information for 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 Australian Health Survey: 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.

Weighting, benchmarking and estimation

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.

Reliability of estimates

21 All sample surveys are subject to sampling and non-sampling error.

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. Indications of the level of sampling error are given by the Relative Standard Error (RSE) and 95% Margin of Error (MoE).

23 In this publication, estimates with an RSE of 25% to 50% are preceded by an asterisk (e.g. *3.4) to indicate that the estimate has a high level of sampling error relative to the size of the estimate, and should be used with caution. Estimates with an RSE over 50% are indicated by a double asterisk (e.g. **0.6) and are generally considered too unreliable for most purposes. These estimates can be used to aggregate with other estimates to reduce the overall sampling error. Another factor, particular to the NNPAS, that may explain certain high RSEs are some of the food groupings that make up the Food Classification. That is, a relatively high variance would be expected where foods are combined that have very different amounts of consumption. For example, within the sub-major level food group of Herbs, spices, seasonings and stock cubes there are foods that have relatively small gram amounts of consumption (such as herbs and spices) grouped with foods that are consumed in substantially greater amounts (such as liquid stock). For more information on the Food classification see Food Intake in the Australian Health Survey: Users' Guide, 2011-13.

24 The MoEs are provided for all proportion and average estimates to assist users in assessing the reliability of these types of estimates. Users may find this measure is more convenient to use, rather than the RSE, in particular for small and large proportion estimates. The estimate combined with the MoE defines a range which is expected to include the true population value with a given level of confidence. This is known as the confidence interval. This range should be considered by users to inform decisions based on the estimate.

25 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.

26 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 results of the 1995 National Nutrition Survey (NNS) and the 2011-12 NNPAS suggests that, like other nutrition surveys, there has been some under-reporting of food intake by participants, and that patterns of under-reporting have changed over time. However, it is difficult from the available data, to accurately estimate levels of under-reporting that have occurred and therefore how much energy and nutrients might be missing from intakes reported by respondents. One method is to estimate the average amount of energy required for a population to achieve an Energy Intake: Basal Metabolic Rate ratio of 1.55 (i.e. the conservative minimum energy requirement for a normally active but sedentary population). Using this method, it is estimated that average energy intakes in the 2011-12 NNPAS may be under-reported by as much as 17% for males and 21% for females. The factor most closely associated with under-reporting was BMI, where overweight or obese people in the NNPAS were most likely to have lower than expected energy intakes. For more information see Under-reporting in Nutrition Surveys in the Australian Health Survey: Users' Guide, 2011-13.

27 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.

28 Another non-sampling error specific to the NNPAS is the accuracy of the nutrient and measures database containing thousands of foods used to derive nutrient estimates. The databases used for the 2011-12 NNPAS were developed by Food Standards Australia New Zealand specifically for the survey. A complete nutrient profile of 44 nutrients was created based on FSANZ’s latest available data, however, not all data were based on directly analysed foods. Some data were borrowed from overseas food composition tables, food label information, imputed data from similar foods or data calculated using a recipe approach. See AUSNUT 2011-13 for more information.

29 Non-response occurs when people cannot or will not cooperate, or cannot be contacted for the purposes of a survey. 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.

30 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.

31 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, information recorded in the survey is essentially 'as reported' by respondents, and hence may differ from information collected using different methodology.


32 The Australian Health Survey food classification was produced by Food Standards Australia New Zealand. It is formed by grouping the 8-digit food codes into broader food groups comprising major, sub-major and minor groups, along with dietary supplements. The Australian Health Survey food classification is available as an Excel spreadsheet from the Downloads section of the Australian Health Survey: Users' Guide, 2011-13.

Comparisons with the 1995 National Nutrition Survey

33 The 2011-12 NNPAS has not been collected in its current form before. However, the ABS has previously conducted nutrition surveys, the most recent being the 1995 National Nutrition Survey (1995 NNS). Published results from the 1995 NNS include:

34 While the 1995 NNS collected similar food and nutrition data to the NNPAS, some important changes in the food classification and methodology mean that care needs to be taken in making direct comparisons between surveys. See Comparisons with 1995 NNS in the Australian Health Survey: Users' Guide, 2011-13 for more details.


35 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.

36 Some techniques used to guard against identification or disclosure of confidential information in statistical tables are suppression of sensitive cells, random adjustments to cells with very small values, and aggregation of data. To protect confidentiality within this publication, some cell values may have been suppressed and are not available for publication but included in totals where applicable. As a result, sums of components may not add exactly to totals due to the confidentialisation of individual cells.


37 Estimates presented in this publication have been rounded. As a result, sums of components may not add exactly to totals. Also note that due to rounding to one decimal place, estimates showing as 0.0 with a high RSE or MoE have a true figure being less than 0.05 but greater than 0.0.

38 Proportions presented in this publication are based on unrounded figures. Calculations using rounded figures may differ from those published.


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 Food Standards Australia New Zealand (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 supplements consumed. The ABS would like to acknowledge and thank FSANZ for their support, advice and expertise.

Products and services

42 Summary results from this survey are available in spreadsheet form from the Data downloads section in this release.

43 For users who wish to undertake more detailed analysis of the survey data, Survey Table Builder is available. Survey Table Builder is an online tool for creating tables from ABS survey data, where variables can be selected for cross-tabulation. It has been developed to complement the existing suite of ABS microdata products and services including Census TableBuilder and CURFs. Further information about ABS microdata, including conditions of use, is available via the Microdata section on the ABS web site.

44 Special tabulations are available on request. Subject to confidentiality and sampling variability constraints, tabulations can be produced from the survey incorporating data items, populations and geographic areas selected to meet individual requirements. A list of currently available data items is available from the Australian Health Survey: Users' Guide, 2011-13 (cat. no. 4363.0.55.001).

Related publications

45 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.

Technical note

Reliability of the estimates

1 Two types of error are possible in an estimate based on a sample survey: sampling error and non-sampling error. The sampling error is a measure of the variability that occurs by chance because a sample, rather than the entire population, is surveyed. Since the estimates in this publication are based on information obtained from occupants of a sample of dwellings they are subject to sampling variability; that is they may differ from the figures that would have been produced if all dwellings had been included in the survey. One measure of the likely difference is given by the standard error (SE). There are about two chances in three that a sample estimate will differ by less than one SE from the figure that would have been obtained if all dwellings had been included, and about 19 chances in 20 that the difference will be less than two SEs.

2 Another measure of the likely difference is the relative standard error (RSE), which is obtained by expressing the SE as a percentage of the estimate. The RSE is a useful measure in that it provides an immediate indication of the percentage errors likely to have occurred due to sampling, and thus avoids the need to refer also to the size of the estimate.

\(\large{R S E \%=\left(\frac{S E}{estimate}\right) \times 100}\)

3 RSEs for the published estimates and proportions are supplied in the Excel data tables, available via the Data downloads section.

4 The smaller the estimate the higher is the RSE. Very small estimates are subject to such high SEs (relative to the size of the estimate) as to detract seriously from their value for most reasonable uses. In the tables in this publication, only estimates with RSEs less than 25% are considered sufficiently reliable for most purposes. However, estimates with larger RSEs, between 25% and less than 50% have been included and are preceded by an asterisk (e.g. *3.4) to indicate they are subject to high SEs and should be used with caution. Estimates with RSEs of 50% or more are preceded with a double asterisk (e.g. **0.6). Such estimates are considered unreliable for most purposes.

5 The imprecision due to sampling variability, which is measured by the SE, should not be confused with inaccuracies that may occur because of imperfections in reporting by interviewers and respondents and errors made in coding and processing of data. Inaccuracies of this kind are referred to as the non-sampling error, and they may occur in any enumeration, whether it be in a full count or only a sample. In practice, the potential for non-sampling error adds to the uncertainty of the estimates caused by sampling variability. However, it is not possible to quantify the non-sampling error.

Standard errors of proportions and percentages

6 Proportions and percentages formed from the ratio of two estimates are also subject to sampling errors. The size of the error depends on the accuracy of both the numerator and the denominator. For proportions where the denominator is an estimate of the number of persons in a group and the numerator is the number of persons in a sub-group of the denominator group, the formula to approximate the RSE is given below. The formula is only valid when x is a subset of y.

\(\large{R S E\left(\frac{X}{Y}\right)=\sqrt{R S E(X)^{2}-R S }E(Y)^{2}}\)

Comparison of estimates

7 Published estimates may also be used to calculate the difference between two survey estimates. Such an estimate is subject to sampling error. The sampling error of the difference between two estimates depends on their SEs and the relationship (correlation) between them. An approximate SE of the difference between two estimates (x-y) may be calculated by the following formula:


8 While the above formula will be exact only for differences between separate and uncorrelated (unrelated) characteristics of sub-populations, it is expected that it will provide a reasonable approximation for all differences likely to be of interest in this publication.

9 Another measure is the Margin of Error (MoE), which describes the distance from the precision of the estimate at a given confidence level, and is specified at a given level of confidence. Confidence levels typically used are 90%, 95% and 99%. For example, at the 95% confidence level the MoE indicates that there are about 19 chances in 20 that the estimate will differ by less than the specified MoE from the population value (the figure obtained if all dwellings had been enumerated). The 95% MoE is calculated as 1.96 multiplied by the SE.

10 The 95% MoE can also be calculated from the RSE by:

\(\large{M O E(y) \approx \frac{R S E(y) \times y}{100} \times 1.96}\)

11 The MoEs in this publication are calculated at the 95% confidence level. This can easily be converted to a 90% confidence level by multiplying the MoE by


or to a 99% confidence level by multiplying by a factor of


12 A confidence interval expresses the sampling error as a range in which the population value is expected to lie at a given level of confidence. The confidence interval can easily be constructed from the MoE of the same level of confidence by taking the estimate plus or minus the MoE of the estimate.

Example of interpretation of sampling error

13 Standard errors can be calculated using the estimates and the corresponding RSEs. For example, for females aged 19-30 years, the mean intake of Citrus fruit was 16.5 grams. The RSE for this estimate is 16.8%, and the SE is calculated by:

\(\large{\begin{aligned} S E \ of \ estimate &=\left(\frac{R S E}{100}\right) \times estimate \\ \\ &=0.168 \times 16.5 \\ \\&=2.8 \end{aligned}}\)

14 Standard errors can also be calculated using the MoE. For example the MoE for the estimate of the proportion of females aged 19-30 years who ate a Citrus fruit on the day prior to interview is +/- 3.5 percentage points. The SE is calculated by:

\(\large{\begin{aligned} S E \ of \ estimate &=\left(\frac{M O E}{1.96}\right) \\ \\ &=\left(\frac{3.5}{1.96}\right) \\ \\ &=1.8 \end{aligned}}\)

15 Note due to rounding the SE calculated from the RSE may be slightly different to the SE calculated from the MoE for the same estimate.

16 There are about 19 chances in 20 that the estimate of the proportion of females aged 19-30 years who ate a Citrus fruit on the day prior to interview is within +/- 3.5 percentage points from the population value.

17 Similarly, there are about 19 chances in 20 that the proportions of females aged 19-30 years who ate a Citrus fruit on the day prior to interview is within the confidence interval of 8.8% to 15.8%.

Significance testing

18 For comparing estimates between surveys or between populations within a survey it is useful to determine whether apparent differences are 'real' differences between the corresponding population characteristics or simply the product of differences between the survey samples. One way to examine this is to determine whether the difference between the estimates is statistically significant. This is done by calculating the standard error of the difference between two estimates (x and y) and using that to calculate the test statistic using the formula below:

\(\LARGE\frac{|x-y|}{S E(x-y)}\)

19 If the value of the statistic is greater than 1.96 then we may say there is good evidence of a statistically significant difference at 95% confidence levels between the two populations with respect to that characteristic. Otherwise, it cannot be stated with confidence that there is a real difference between the populations.


Show all


Show all

Back to top of the page