Australian Health Survey: Usual Nutrient Intakes methodology

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

Explanatory notes


1 This publication is the second release of nutrition data from the 2011-12 National Nutrition and Physical Activity Survey (NNPAS). The first release was published in May 2014.

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: Usual Nutrient Intakes publication contains usual (long term) nutrient intake information modelled from two days of 24-hour dietary recall data. Usual intakes of nutrients are provided by age groups and sex at the national level, including comparison with nutrient requirements, where relevant.

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 AHS: Users' Guide, 2011-13 (cat. no. 4363.0.55.001).

Scope of the survey

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.

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 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.

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, 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).

NNPAS, approached sample, final sample and response rates

 New South WalesVictoriaQueenslandSouth AustraliaWestern AustraliaTasmaniaNorthern TerritoryAustralian Capital TerritoryAustralia
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 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.

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. 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. 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, RSEs are provided for all count estimates. 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.

24 MoEs are provided for all proportion estimates to assist users in assessing the reliability of these types of estimates. 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.

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 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.

27 Where comparisons with guideline values (nutrient reference values or NRVs) have been made, any error in these guideline values will affect the quality of the resulting estimates. The NRVs are a set of recommendations made by the Australian National Health and Medical Research Council and the New Zealand Ministry of Health for nutritional intake, based on currently available scientific knowledge. More information on the methods used to derive the NRVs for each nutrient is available on the Nutrient Reference Values for Australia and New Zealand website.

28 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.

29 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.

30 Another 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 specifically for the survey. A complete nutrient profile of 44 nutrients was created based on FSANZ’s latest available data, however, not all data was based on directly analysed foods. Some data was 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.

31 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.

32 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.

33 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.

Comparisons with 1995 NNS

34 Comparisons of this publication with 1995 NNS usual nutrient intakes are not recommended due to changes in usual intake adjustment methodology and different survey methodology. See the Comparisons with 1995 NNS chapter of the AHS: Users' guide 2011-13 (cat. no. 4363.0.55.001) 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 In this publication, confidentiality is protected due to modelling of age and sex groups only. No data is presented for small groups or individual respondents.


37 Estimates presented in this publication have been rounded. As a result, sums of components may not add exactly to totals. Estimates of zero or rounded to zero and their corresponding measures of error have been represented by a dash.

38 All statistics relating to proportion of persons are rounded to one decimal place and all statistics relating to number of persons are rounded to whole numbers (‘000). Percentiles of usual nutrient intakes and mean usual nutrient intakes are rounded to one decimal place or whole numbers, depending on the corresponding Nutrient Reference Value or the scale of the data.


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 This publication is a joint release by the ABS and Food Standards Australia New Zealand (FSANZ). 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.

Products and services

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

44 Because the NCI method produces estimates of usual nutrient intakes for groups and not individuals, usual nutrient intake data is not available at the unit record level.

45 Summary tables containing aggregated estimates of the prevalence of inadequate intakes, intakes above the upper level and intakes outside of acceptable macronutrient distribution ranges are available in the Data downloads section in this release. Information on how to aggregate estimates for different age and sex groups is in Summary Tables in the Users' Guide.

Related publications

46 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. Estimates derived from models, including the NCI method, are also subject to prediction error and simulation variance. 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.

6 Prediction error is the variability attributed to the statistical accuracy of the model used in this publication, including bias due to specification of the model. Simulation error is the variability due to simulating different random effects in order to generate usual distribution intakes. Although every effort is made to ensure an appropriate model specification is used, through external literature research and statistical testing, these errors are not quantified and also add to the uncertainty of the estimates.

Standard errors of proportions and percentages

7 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

8 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:

\(\large{SE \left(x-y \right)=\sqrt{[{SE}({x})]^{2}+\left[{SE}\left.({y})\right]{^2}\right.}}\)

9 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.

10 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.

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

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

12 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


13 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

14 Standard errors can be calculated using the estimates and the corresponding RSEs. For example, for females aged 19-30 years, the mean usual intake of protein was 77 grams. The RSE for this estimate is 2%, and the SE is calculated by:

\(\large{\begin{aligned} SE\ of \ estimate &=\left(\frac{R S E}{100}\right) \times estimate \\ \\ &=0.02 \times 77 \\ \\ &=1.54 \end{aligned}}\)

15 Standard errors can also be calculated using the MoE. For example the MoE for the estimate of the proportion of females aged 71 years and over whose usual daily protein intake was below 46 grams is +/- 2.5 percentage points. The SE is calculated by:

\(\large{\begin{aligned} SE \ of \ estimate &=\left(\frac{M O E}{1.96}\right) \\ \\&=\left(\frac{2.5}{1.96}\right) \\ \\ &=1.3 \end{aligned}}\)

16 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.

17 There are about 19 chances in 20 that the estimate of the proportion of females aged 71 years and over whose usual daily protein intake was below 46 grams is +/- 2.5 percentage points from the population value.

18 Similarly, there are about 19 chances in 20 that the proportions of females aged 71 years and over whose usual daily protein intake was below 46 grams is within the confidence interval of 1.3% to 6.3%.

Significance testing

19 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:


20 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.


The definitions used in this survey are not necessarily identical to those used for similar items in other collections. Additional information is contained in the Australian Health Survey: Users' Guide (cat. no. 4363.0.55.001).

Show all


Show all

Back to top of the page