Latest release

# Australian Health Survey: Biomedical Results for Nutrients methodology

Reference period
2011-12
Released
11/12/2013
Next release Unknown
First release

## Explanatory notes

### Introduction

1 This publication is the second release of information from the 2011–12 National Health Measures Survey (NHMS), which forms part of the 2011–13 Australian Health Survey (AHS). The first release, which focussed on chronic disease biomarkers, was published in August 2013. See Australian Health Survey: Biomedical Results for Chronic Diseases for more information.

2 For more information on the structure of the AHS, see Structure of the Australian Health Survey. The following information focusses on the NHMS component of the survey only.

3 All people aged 5 years and over who participated in either the National Health Survey (NHS) or the National Nutrition and Physical Activity Survey (NNPAS) were invited to participate in the voluntary NHMS. The NHMS took place throughout Australia from March 2011 to September 2012. Participants voluntarily provided blood and urine samples, which were then analysed for specific biomarkers.

4 The 2011–12 NHMS collected information about:

• chronic disease biomarkers, including tests for diabetes, cholesterol, triglycerides, kidney disease and liver function; and
• nutrient biomarkers, including tests for iron, folate, iodine, Vitamin B12 and Vitamin D.

See Appendix A for the list of tests conducted in the NHMS.

5 In addition, the broader survey collected a wide range of information about health conditions, risk factors (for example, obesity), health service usage, medications and demographic and socioeconomic factors, which can be analysed in relation to the NHMS results.

6 The statistics presented in this publication focus on nutrient biomarkers, including iron, folate, iodine, Vitamin B12 and Vitamin D.The list of data items from the survey, as well as detailed information on the different tests used in the NHMS, is available in the Australian Health Survey: Users' Guide, 2011–13 (cat. no. 4363.0.55.001).

### Scope of the survey

7 The NHS and NNPAS included a combined sample of approximately 25,000 private dwellings across Australia. 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, except the Northern Territory where the population living in Very Remote areas accounts for around 23% of persons.

8 The 2011–13 AHS also included an additional representative sample of around 12,300 Aboriginal and Torres Strait Islander people, which was collected between April 2012 and July 2013. This is a separate collection of Aboriginal and Torres Strait Islander people living in remote and non-remote areas, including discrete Aboriginal and Torres Strait Islander communities. This survey also included a biomedical component. The first results from the National Aboriginal and Torres Strait Islander Health Survey were released on the 27th November 2013. See Australian Aboriginal and Torres Strait Islander Health Survey: First Results, Australia (cat. no. 4727.0.55.001) for more information. The remainder of the results will be released progressively into 2014.

9 Non-private dwellings such as hotels, motels, hospitals, nursing homes and short-stay caravan parks were excluded from the NHS and NNPAS. This may affect estimates of the number of people with some conditions; for example, conditions which may require periods of hospitalisation, such as kidney disease.

10 Within selected dwellings of the NHS and NNPAS, a random sub-sample of residents was selected as follows:

• one adult (aged 18 years and over); and where applicable
• one child aged 0–17 years (NHS) or
• one child aged 2–17 years (NNPAS).

11 The following groups were also excluded from the NHS and NNPAS:

• 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; and
• visitors to private dwellings.

12 All selected persons aged 5 years and over were then invited to participate in the voluntary NHMS. Children aged 5–11 years were asked to provide a urine sample only, whereas people aged 12 years and over were asked to provide both a blood and urine sample.

### Data collection

13 The interview components of the NHS and NNPAS were conducted under the Census and Statistics Act (CSA) 1905. Ethics approval for the NHMS component was sought and gained from the Australian Government Department of Health and Ageing’s Departmental Ethics Committee.

14 At the completion of NHS or NNPAS questions, interviewers explained the voluntary NHMS component and provided a written information sheet.

15 Informed consent was sought from adults and from parents/legal guardians of children through completion of a consent form. A copy of the consent form was left with the respondent. Those that agreed to take part were provided a referral form to complete (including whether specific medications or supplements were regularly taken) to provide to the collection clinic.

16 A follow-up reminder process was used for respondents who consented to the NHMS but had not yet attended a collection clinic. This process took the form of phone calls or letters arranged ten days apart from the interview date. Also, home visits and temporary clinics were offered to participants in certain circumstances to maximise participation rates, particularly in remote areas and for those who were incapacitated. To reduce expenses for travel, child-care or time off work, participants were able to claim a reimbursement of \$50 paid into an Australian bank account.

17 Most blood and urine samples were collected at Sonic Healthcare collection clinics or via a home visit using standard operating procedures for phlebotomy collection.

18 All blood and urine samples were then analysed at a central laboratory at Douglass Hanly Moir (DHM) Pathology in Sydney, Australia on machines accredited by the National Association of Testing Authorities (NATA). DHM conducted Internal Quality Control (QC) analysis for all instruments used to conduct analysis on the samples. More information on NHMS quality assurance methods and procedures will be available in the Australian Health Survey: Users' Guide, 2011–13 (cat. no. 4363.0.55.001).

19 All participants were provided with a pathology report of their results via post. Participants could also nominate for their results to be sent to their regular doctor. In cases where the results were outside the normal range, participants were contacted by a qualified health professional and encouraged to discuss their results with their doctor. If the test results showed a significantly high or low result which was dangerous to the person's health, they were contacted immediately and advised on the best course of action.

### Response rates

20 In the NHS and NNPAS combined, there were a total of 25,080 households fully responding, giving a response rate of 81.6%. With the selection of one adult and one child aged 2–17 years where applicable, this resulted in a total of 31,837 persons in sample (or 30,329 aged 5 years and over and 27,636 aged 12 years and over).

 Households approached (after sample loss) no. 30 721 Households in sample no. 25 080 Household response rate % 81.6 Persons in sample 2 years and over no. 31 837 5 years and over no. 30 329 12 years and over no. 27 636

21 The following table presents response rates for the NHMS.

NHMS response rates, 2011-12
Number of persons (no.)Proportion of persons (%)
5 YEARS AND OVER
Persons in sample (NHS/NNPAS)30 329100.0
Participated in NHMS11 24637.1
Urine sample provided10 53634.7
Did not participate in NHMS19 08162.9
12 YEARS AND OVER
Persons in sample (NHS/NNPAS)27 636100.0
Participated in NHMS10 40337.6
Blood sample provided
Fasting sample8 16829.6
Non-fasting sample2 0247.3
Did not participate in NHMS17 23365.3

22 The following table compares characteristics of persons who participated in the NHMS with those who participated in the NHS and NNPAS.

Comparisons between NHMS and NHS/NNPAS samples, persons aged 18 years and over, 2011-12
NHMS (unweighted) %NHS/NNPAS (unweighted) %
Married(a)58.552.8
Born in Australia70.971.4
Has a non-school qualification62.559.1
In the Labour Force63.666.5
Self-reported diabetes(b)7.06.5
Self-reported high cholesterol(c)12.09.6
Excellent or Very Good self-assessed health53.452.9
Current daily smoker12.017.6
Overweight/obese(d)66.464.9
1. Includes de facto couples.
2. Includes persons who self-reported they had diabetes, regardless if it was current or long-term (excludes gestational diabetes).
3. Includes persons who self-reported they had high cholesterol and it was current and long-term.
4. Includes only persons for whom height and weight were measured.

23 More information on response rates is available in the Australian Health Survey: Users' Guide, 2011–13 (cat. no. 4363.0.55.001).

### Weighting, benchmarking and estimation

24 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 person. The weight is a value which indicates how many population units are represented by the sample unit.

25 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.

26 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. The selection of benchmarks was chosen to maximise the accuracy of the estimates of biomedical characteristics, by reducing both random and systematic errors as much as possible.

27 The NHMS results were 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.

28 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 conditions) are obtained by multiplying the characteristic of interest with the weight of the reporting person and aggregating.

29 The weights for the NHMS are different to the weights for the combined NHS/NNPAS due to the differing response patterns between the surveys.

30 An investigation was undertaken to determine whether the accuracy of NHMS estimates could be improved by weighting with any other variables collected in the NHS and NNPAS, including smoking status, Body Mass Index, self-assessed health, physical activity, employment status, marital status, country of birth and blood pressure. While the use of some of these variables would have improved the accuracy of some NHMS estimates (e.g. the use of smoker status in the weighting process would have ensured that totals relating to current daily smokers were identical in the NHMS to those in the combined NHS and NNPAS), they made no difference to the main variables of interest in the NHMS (i.e. estimates of diabetes, cholesterol) and even in some cases increased the measure of sampling error or Relative Standard Error (RSE).

31 The decision to maximise the accuracy of these main variables of interest in the NHMS by not including those other variables in the calculation of weights for the NHMS means that, while variables collected in the NHMS can be analysed with variables collected in either the NHS or NNPAS, the NHS and NNPAS should be used when reporting on the prevalence of these variables. For example, for self-reported medical conditions and risk factors such as smoking, the most accurate prevalences should be calculated using the combined NHS and NNPAS sample.

### Reliability of estimates

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

33 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 Margin of Error (MoE).

34 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.

35 The MoEs are provided for all proportions to assist users in assessing their reliability. Users may find this measure is more convenient to use, rather than the RSE, in particular for small and large proportions. The proportion 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 proportion.

36 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 or missing test results, errors in reporting by respondents or in recording of answers by interviewers, and occasional errors in coding and processing data.

37 Non-response can affect the reliability of results and can introduce a 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.

38 Results for nutrient biomarkers may vary depending on the type of test and assay used, as well as the type of machine employed to spin the samples. Details around the procedures followed for each of the nutrient biomarkers in the NHMS are outlined in Australian Health Survey: Users' Guide, 2011–13 (cat. no. 4363.0.55.001).

39 In the NHMS, urinary iodine levels were measured using the inductively coupled plasma mass spectrometry (ICP-MS) method. Many other studies including the 2003–04 Australian National Iodine Nutrition Study and the 2009–10 Victorian Health Monitor used the Sandell-Koltoff spectrophotometric (S-K) method to measure iodine levels. The World Health Organization and the International Council for Control of Iodine Deficiency Disorders (ICCIDD) determined a set of cut-offs using the S-K method to define if a population is iodine deficient. However, no agreed cut-offs have been developed yet for the new ICP-MS method. Therefore, the cut-offs for the S-K method were applied to the ICP-MS results in the NHMS to determine iodine deficiency. Research has shown there to be good agreement between the two methods overall, but the ICP-MS method may be more sensitive in detecting iodine deficiency than the S-K method. Therefore any comparison of iodine deficiency between the NHMS and studies that used the S-K method method should be applied with caution.

40 In the NHMS, month of collection was used to analyse the seasonal effects of Vitamin D deficiency. Although there were proportionally more people who had their blood samples taken in Autumn than in Spring, this only had a very small impact on the overall rate of Vitamin D deficiency at the population level.

Distribution of the adult NHMS sample by season
Season% of sample
Summer25.2
Autumn33.8
Winter25.3
Spring15.7

### Confidentiality

41 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.

42 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.

### Rounding

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

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

### Acknowledgements

45 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.

46 The 2011–13 AHS, and particularly the NHMS component, was developed with the assistance of several advisory groups and expert panels. Members of these groups were drawn from Commonwealth and state/territory government agencies, non-government organisations, relevant academic institutions and clinicians. The valuable contributions made by members these groups are greatly appreciated.

### Products and services

47 Summary results from the NHMS are available in spreadsheet form from the Data downloads section in this release.

48 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 data items is available from the Australian Health Survey: Users' Guide, 2011–13 (cat. no. 4363.0.55.001).

### Related publications

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

## Appendix A - summary of NHMS biomarkers

### Show all

#### Summary of nutrient biomarkers

AgeTest typeFasting
Folate & Vitamin B12
Serum folate12+BloodNo
Red cell folate (RCF)12+BloodNo
Serum vitamin B1212+BloodNo
Iron
Serum ferritin12+BloodNo
Inflammation marker (C-reactive Protein (CRP))12+BloodNo
Soluble transferrin receptor (sTfR)12+BloodNo
Vitamin D
Serum 25-hydroxyvitamin D [25(OH)D]12+BloodNo
Iodine
Iodine concentration5+UrineNo
Sodium
Sodium concentration5+UrineNo
Potassium
Potassium concentration5+UrineNo

#### Summary of chronic disease biomarkers

AgeTest typeFasting
Cardiovascular disease biomarkers
Total cholesterol12+BloodNo
High Density Lipoprotein (HDL) cholesterol12+BloodNo
Low Density Lipoprotein (LDL) cholesterol12+BloodYes
Triglycerides12+BloodYes
Apolipoprotein B (Apo B)12+BloodNo
Diabetes biomarkers
Fasting plasma glucose12+BloodYes
Glycated Haemoglobin (HbA1c)12+BloodNo
Kidney disease biomarkers
Albumin creatinine ratio (ACR)5+UrineNo
Estimated glomerular filtration rate (eGFR)18+BloodNo
Liver function biomarkers
Alanine aminotransferase (ALT)12+BloodNo
Gamma glutamyl transferase (GGT)12+BloodNo
Anaemia
Haemoglobin (Hb)12+BloodNo
Tobacco use
Cotinine12+BloodNo

## Technical note

### Reliability of the estimates

1 Two types of errors 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 a sample of persons they are subject to sampling variability; that is, they may differ from the figures that would have been produced if all persons 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 persons 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.

$$\mathrm{RSE} \%=\left(\frac{\mathrm{SE}}{\text { estimate }}\right) \times 100$$

3 RSEs for the published estimates and proportions are supplied in the online version of this publication on the ABS website.

4 The smaller the estimate the higher 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.

$$\operatorname{RSE}\left(\frac{\mathrm{X}}{\mathrm{Y}}\right)=\sqrt{\mathrm{RSE}(\mathrm{X})^{2}-\mathrm{RSE}(\mathrm{Y})^{2}}$$

### 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:

$$\mathrm{SE}(\mathrm{x}-\mathrm{y})=\sqrt{[\mathrm{SE}(\mathrm{x})]^{2}+[\mathrm{SE}(\mathrm{y})]^{2}}$$

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 population value 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:

$$\operatorname{MoE}(\mathrm{y}) \approx \frac{\operatorname{RSE}(\mathrm{y}) \times \mathrm{y}}{100} \times 1.96$$

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

$$\large \frac{1.645}{1.96}$$

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

$$\large \frac{2.576}{1.96}$$

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, in this publication, the estimated proportion of females aged 18 years and over who have urinary iodine levels less than 100 μg/L is 40.1%. The RSE for this estimate is 2.8%, and the SE is calculated by:

\begin{aligned} \mathrm{SE} \text { of estimate } &=\left(\frac{\mathrm{RSE}}{100}\right) \times \text { estimate } \\ &=0.028 \times 40.1 \\ &=1.1 \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 18 years and over who have urinary iodine levels less than 100 μg/L is +/- 2.2 percentage points. The SE is calculated by:

\begin{aligned} \text { SE of estimate } &=\left(\frac{\mathrm{MoE}}{1.96}\right) \\ &=\left(\frac{2.2}{1.96}\right) \\ &=1.1 \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 18 years and over who have urinary iodine level less than 100 μg/L is within +/- 2.2 percentage points from the population value.

17 Similarly, there are about 19 chances in 20 that the proportions of females aged 18 years and over who have urinary iodine level less than 100 μg/L is within the confidence interval of 37.9% to 42.3%.

### 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:

$$\large \frac{|x-y|}{\operatorname{SE}(x-y)}$$

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.

## Glossary

### Show all

#### Blood pressure

See High blood pressure.

#### Body Mass Index (BMI)

Body Mass Index (BMI) is a simple index of weight-for-height that is commonly used to classify underweight, normal weight, overweight and obesity. It is calculated from height and weight information, using the formula weight (kg) divided by the square of height (m). To produce a measure of the prevalence of underweight, normal weight, overweight or obesity in adults, BMI values are grouped according to the table below which allows categories to be reported against both the World Health Organization (WHO) and National Health and Medical Research Council (NHMRC) guidelines.

Body Mass Index, adults
CategoryRange
UnderweightLess than 18.50
Normal range18.50 - 24.99
Overweight25.00 - 29.99
Obese30.00 or more

Separate BMI classifications were produced for children. BMI scores were created in the same manner described above but also took into account the age and sex of the child. There are different cutoffs for BMI categories (underweight/normal combined, overweight or obese) for male and female children. These categories differ to the categories used in the adult BMI classification and follow the scale provided in Cole TJ, Bellizzi MC, Flegal KM and Dietz WH, Establishing a standard definition for child overweight and obesity worldwide: international survey, BMJ 2000; 320. For a detailed list of the cutoffs used to calculate BMI for children see the Australian Health Survey: Users' Guide, 2011–13 (cat. no. 4363.0.55.001).

#### C-reactive protein (CRP)

CRP is used for ferritin interpretations and measures general levels of inflammation in the body. High levels may mask iron deficiency and therefore people with CRP levels above 10mg/L were excluded from the ferritin results.

Also see Iron and Ferritin.

#### Current daily smoker

A current daily smoker is a respondent who reported at the time of interview that they regularly smoked one or more cigarettes, cigars or pipes per day.

Also see Smoker status.

#### Ferritin

Ferritin measures the amount of iron stores in the body. Low ferritin in the blood reflects depleted iron stores. Levels of ferritin can be affected by infection or inflammation, therefore people with inflammation (defined as a C-reactive protein level of >10mg/L) were excluded from the NHMS ferritin results.

Also see C-reactive protein, Iron and Serum transferrin receptor.

#### Folate

Folate is a B group vitamin that is essential for healthy growth and development. Folate is found naturally in food, such as green leafy vegetables, fruits and grains, while folic acid is the synthetic form of folate added to food or used in dietary supplements. Folate can help prevent neural tube defects in babies, including spina bifida, if it is taken before conception and early in pregnancy. Folate status can be assessed by measuring serum folate, which provides information on recent intake, and red cell folate, which measures the level of folate stores and long-term nutritional status.

Also see Serum folate and Red cell folate

#### High blood pressure

A measured blood pressure reading of 140/90 mm Hg (millimetres of mercury) or higher. Data on high blood pressure in this publication refer to measured blood pressure only, and do not take into account whether people who might otherwise have high blood pressure are managing their condition through the use of blood pressure medications.

#### Hypothyroidism

A condition where the thyroid gland is underactive. Iodine deficiency is a known cause of hypothyroidism.

#### Index of Relative Socio-Economic Disadvantage

This is one of four Socio-Economic Indexes for Areas (SEIFA) compiled by ABS following each Census of Population and Housing. The indexes are compiled from various characteristics of persons resident in particular areas: the Index of Relative Socio-Economic Disadvantage summarises attributes such as low income, low educational attainment, high unemployment and jobs in relatively unskilled occupations. A lower Index of Relative Socio-Economic Disadvantage quintile (e.g. the first quintile) indicates relatively greater disadvantage and a lack of advantage in general. A higher Index of Relative Socio-Economic Disadvantage (e.g. the fifth quintile) indicates a relative lack of disadvantage and greater advantage in general. For further information about SEIFA see the Australian Health Survey: Users' Guide, 2011–13 (cat. no. 4363.0.55.001).

#### Iodine

Iodine is an important nutrient for the production of thyroid hormones. It is essential for brain development, particularly in young children and infants. Deficiency in iodine can cause goitres, hypothyroidism, fetal brain damage and developmental delays. The major dietary sources of iodine include seafood, especially seaweed, and baked bread and dairy milk.

The WHO considers a population iodine deficient if the median urinary iodine concentration is less than 100 μg/L. They also recommended that no more than 20% of the population have iodine concentrations below 50 μg/L.

#### Iron

Iron is an essential mineral for transporting oxygen around the body. Iron deficiency can lead to fatigue, tiredness and decreased immunity. Measures of iron intake in the NHMS include Ferritin and Serum transferrin receptor.

Also see Ferritin and Serum transferrin receptor

#### Margin of Error (MoE)

Describes the distance from the precision of the estimate at a given confidence level, and is specified at a given level of confidence (95% in this publication). In this publication, Margin of error has only been provided for proportions tables. For more information see the Technical notes of this publication.

#### Normal weight

See Body Mass Index (BMI).

#### Non-smoker

A respondent who reported at the time of interview that they were an ex-smoker or had never smoked.

Also see Smoker status.

#### Obese

See Body Mass Index (BMI).

#### Overweight

See Body Mass Index (BMI).

#### Red cell folate

A measure the level of folate stores in the body. In the NHMS, red cell folate is primarily used to measure the risk of neural tube defects for women of childbearing age

See also Folate and Serum folate.

#### Relative Standard Error (RSE)

The standard error expressed as a percentage of the estimate. For more information see the Technical notes in this publication.

#### Remoteness

The Remoteness Structure for the Australian Statistical Geography Standard (ASGS) 2011, has five categories based on an aggregation of geographical areas which share common characteristics of remoteness, determined in the context of Australia as a whole. These categories are:

• Major cities of Australia
• Inner regional Australia
• Outer regional Australia
• Remote Australia
• Very remote Australia

The five categories are generally aggregated in some way for use in output.

The 2011 Remoteness Structure has been built using the same principles as the 2006 Remoteness Structure. The primary difference is that it was built from ASGS Statistical Area Level 1 (SA1) regions rather than from 2006 Census Collection Districts (CCD).

#### Serum folate

A measure the level of folate in the body. It is sensitive to folate intake and can fluctuate due to short-term changes in diet. In the NHMS, serum folate is used to measure the folate status in the general population, while red cell folate is used to measure the risk of neural tube defects for women of childbearing age.

Also see Folate and Red cell folate.

#### Serum transferrin receptor

An indicator of iron levels in the body. It is not as affected by infection or inflammation as other measures, such as ferritin.

#### Smoker status

The extent to which a respondent was smoking at the time of interview, and refers to regular smoking of tobacco, including manufactured (packet) cigarettes, roll-your-own cigarettes, cigars and pipes, but excludes chewing tobacco and smoking of non-tobacco products. Categorised as:

• Current daily smoker - a respondent who reported at the time of interview that they regularly smoked one or more cigarettes, cigars or pipes per day;
• Current smoker - Other - a respondent who reported at the time of interview that they smoked cigarettes, cigars or pipes, less frequently than daily;
• Ex-smoker - a respondent who reported that they did not currently smoke, but had regularly smoked daily, or had smoked at least 100 cigarettes, or smoked pipes, cigars, etc at least 20 times in their lifetime; and
• Never smoked - a respondent who reported they had never regularly smoked daily, and had smoked less than 100 cigarettes in their lifetime and had smoked pipes, cigars, etc less than 20 times.

#### Underweight

See Body Mass Index (BMI).

#### Vitamin B12

Vitamin B12 is a water-soluble vitamin with a key role in the normal functioning of the brain and nervous system, and for the formation of blood. Vitamin B12 is a nutrient that helps keep the body's nerve and blood cells healthy and helps make DNA. If left untreated, vitamin B12 deficiency, also known as B12 deficiency, can lead to anemia, as well as nerve and brain damage.

#### Vitamin D

Vitamin D is essential for the body to absorb calcium effectively. The main source of Vitamin D is exposure to sunlight, although small amounts can be obtained through some foods, such as eggs, fatty fish and fortified margarine and milk. The main consequence of severe Vitamin D deficiency is rickets in children and osteopenia (fragile bones) in older people. In the NHMS, the levels recommended in a recent Australian position statement on Vitamin D have been applied to determine Vitamin D deficiency. These are:

• Mild deficiency: 30 – 49 nmol/L
• Moderate deficiency: 13* – 29 nmol/L
• Severe deficiency: <13* nmol/L
• Total deficiency: <50 nmol/L
• Adequate levels: ≥50 nmol/L#

* Note that the cut-off recommended in the position statement is <12.5 nmol/L, but the NHMS is unable to output against this cut-off as the Vitamin D data is only available in whole numbers.

# Note that the position statement states that levels may need to be 10 to 20 nmol/L higher at the end of summer, to allow for seasonal decrease.

## Abbreviations

The following symbols and abbreviations are used in this publication:

### Show all

 . . not applicable ABS Australian Bureau of Statistics AHS Australian Health Survey BMI Body Mass Index CRP C-reactive protein DHM Douglass Hanly Moir kg kilogram mg/L milligram per litre MoE Margin of Error NHMS National Health Measures Survey NHS National Health Survey nmol/L nanomole per litre NNPAS National Nutrition and Physical Activity Survey pmol/L picomole per liter RCF red cell folate RSE relative standard error SEIFA Socio-Economic Indexes for Areas sTfR Serum transferrin receptor UIC Urinary Iodine Concentration µg/L microgram per litre WHO World Health Organization