Australian Health Survey: Biomedical Results for Chronic Diseases methodology

Latest release
Reference period
2011-12 financial year
Released
5/08/2013
Next release Unknown
First release

Explanatory notes

Introduction

1 This publication is the first release of information from the 2011–12 National Health Measures Survey (NHMS), which forms part of the 2011–13 Australian Health Survey (AHS).

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
  • nutrition biomarkers, including tests for iron, folate, iodine and vitamin D levels.
     

See Appendix A for the full list of tests conducted.

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 biomarkers of chronic disease, including cardiovascular disease, diabetes and kidney disease. Information on nutrition biomarkers will be released in late 2013. Further publications from the Australian Health Survey are outlined in the Release Schedule, while the list of data items available from the survey will be available in the Australian Health Survey: Users' Guide (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 13,000 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. Results from this separate survey will be released progressively from November 2013.

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 chronic 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 In order to get an accurate reading for the fasting plasma glucose, LDL cholesterol and triglyceride tests, participants providing blood samples were asked to fast for 8 hours before their test. The results presented in this publication for these tests refer only to those people who did fast (approximately 79% of adults who participated in the NHMS).

19 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 (cat. no. 4363.0.55.001).

20 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

21 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).

NHS/NNPAS response rates, 2011-12

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

 

22 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 24837.1
  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

 

23 In 2011–12, 79% of persons aged 18 years and over who participated in the NHMS fasted. Data relating to fasting tests (for example, the fasting plasma glucose test) relate to the fasting population only. Analysis of the characteristics of people who fasted compared with those who did not fast showed no difference between fasters and non-fasters.

24 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 cholesterol12.09.6
Excellent or Very Good self-assessed health53.452.9
Current daily smoker12.017.6
Overweight/obese(d)66.464.9

a. Includes de facto couples.
b. Includes persons who self-reported they had diabetes, regardless if it was current or long-term (excludes gestational diabetes).
c. Includes persons who self-reported they had high cholesterol and it was current and long-term.
d. Includes only persons for whom height and weight were measured.
 

25 More information on response rates is available in the Australian Health Survey: Users' Guide (cat. no. 4363.0.55.001).

Weighting, benchmarking and estimation

26 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.

27 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.

28 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.

29 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.

30 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.

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

32 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).

33 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

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

35 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).

36 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.

37 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.

38 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.

39 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.

Classifications

40 Country of birth was classified to the Standard Australian Classification of Countries (cat. no. 1269.0).

41 Main language spoken at home was classified according to the Australian Standard Classification of Languages (cat. no. 1267.0).

42 Descriptions for data items such as diabetes, Body Mass Index and blood pressure are included in the Glossary to this publication.

Confidentiality

43 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.

44 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

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

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

Acknowledgements

47 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.

48 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

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

50 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 (cat. no. 4363.0.55.001).

Related publications

52 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 - NHMS biomarkers

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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.

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

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.

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

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:

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

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:

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

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 the 2011-12 AHS: Biomedical results for chronic diseases, the estimated proportion of males aged 18 years and over who have abnormal levels of total cholesterol is 32.4%. The RSE for this estimate is 3.2%, and the SE is calculated by:

\(\begin{aligned} \mathrm{SE} \text { of estimate } &=\left(\frac{\mathrm{RSE}}{100}\right) \times \text { estimate } \\ &=0.032 \times 32.4 \\ &=1.0 \end{aligned}\)

14 Standard errors can also be calculated using the MoE. For example, the MoE for the estimate of the proportion of males aged 18 years and over who have abnormal levels of total cholesterol is +/- 2.0 percentage points. The SE is calculated by:

\(\begin{aligned} \mathrm{SE} \text { of estimate } &=\left(\frac{\mathrm{MoE}}{1.96}\right) \\ &=\left(\frac{2.0}{1.96}\right) \\ &=1.0 \end{aligned}\)

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 males aged 18 years and over who have abnormal levels of total cholesterol is within +/- 2.0 percentage points from the population value.

17 Similarly, there are about 19 chances in 20 that the proportions of males aged 18 years and over who have abnormal levels of total cholesterol is within the confidence interval of 30.4% to 34.4%.

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:

\(\Large \frac{|\mathrm{x}-\mathrm{y}|}{\mathrm{SE}(\mathrm{x}-\mathrm{y})}\)

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

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Abbreviations

The following symbols and abbreviations are used in this publication:

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